Sunday, 25 February 2018
Tuesday, 6 February 2018
LinuxWorld Informatics Pvt Ltd Summer Internship 2018 for Computer Science Engineering Students May / June, 2018 - Apply Now
LinuxWorld invites applications for summer internships for Computer Science Engineering Students to be conducted during May-July 2018.
The Research Based Internship Projects Core technologies covered in the Summer Internship Program are Machine Learning, Artificial Intelligence, Deep Learning, DevOps, Docker, Cloud Computing, Big Data Hadoop, RedHat, Cassandra, Spark, Python, Splunk and many more
All the projects including the training would be imparted by Renowned Industry Expert Mr. Vimal Daga (CTO - LinuxWorld Informatics Pvt Ltd, Sr. Machine Learning, Cloud, BigData, Splunk & DevOps Consultant, KeyNote Speaker, Entrepreneur, Corporate Trainer)
You will be engaged for a period of 4 / 6 weeks. Your project leader (MENTOR) will evaluate your work at the end of the internship. We expect everyone to work hard, and successfully complete the assignments well in advance of the last date.
To know more about Your Mentor : http://bit.ly/2pcuUa9
To Know more About Summer Internship Program :http://bit.ly/2hkn2Rh
Register online at : http://bit.ly/2D2M2Kk
If you have any query feel free write to hr@lwindia.com
For further advice feel free to call us at +91 9829105960
Monday, 22 January 2018
How Linux World succeed in developing a fraternity of constantly updated technocrats in its free lifelong support program?
Linux World technologies - as part of its inspired learning system - offers the attendees of its training programs a scope to join a growing community of informed technocrats. The community is populated with socially connected people ready to learn new things in the constantly evolving technological world. Mr. Vimal Daga - the founder of the company - mentors these students and leaves no opportunity unused to prepare them with high-end knowledge they can use to future-proof their career. Learn more about how this fraternity of regularly updated technocrats is witnessing a new wave of learning system.
Every year, the company selects a few talented students from its training programs, and after the completion of the courses, gives them a chance to join an existing community of socially connected technocrats. The community keeps growing with every year, with techies ready to learn new things, practice the skills they already learnt at Linux World, and enrich their learning with new development in the technology world. The community is acknowledged and mentored by Mr. Vimal Daga; who regularly updates the students with what's happening in the industry and guides them hone their existing learning.
The key objective behind this development is to form a ready to learn, agile and outgoing community of technocrats, train them with newest learning resources, and equip them with what's needed in this competitive eco system. The results are obvious. It creates updated technocrats who are ready to tackle any challenges in the corporate world, and excel their career. It helps them contribute in nation building with their technologically advanced creations. It helps attendees of Linux World offer Summer Internship for Computer Science programs practice what they learnt post training.
And did we mention, all of these benefits are offered to the students at absolutely FREE OF COST?
Linux World training programs are known for their integrity, and for their content, other than its exclusive resources and state of the art training facilities. The additional advantage of joining the training programs are many, one apparent benefit is the scope of this growing fraternity of technocrats in lifelong, free support system.
Wednesday, 15 November 2017
Why Is Hadoop Training Important?
"The growing importance of Hadoop around the world has made Hadoop training an important topic. It is important that you understand the concept of Hadoop before you start off with your training program"
In the recent times, an increased demand for Hadoop has been seen around the world. Thus, if you are interested in knowing more about Hadoop and you are keen to get Hadoop Training then you've come to the right place. There are various online programs which are known for teaching the online audience about the art of Hadoop at the convenience of their home. By making use of online video training offers, such institutions are known for imparting the knowledge pertaining to Hadoop so that the online audience can utilize the skills and make good use of the Hadoop knowledge in progressing in their respective fields.
One of the highlights of Hadoop Training is the fact that it teaches an individual about the wide array of aspects which are attached to the big data. Such training programs help teach the online audience about the analytics and the reporting skills which are considered to be imperative in terms of effectively understanding the big data, all of which helps in improving the performance of the business on a holistic level. It is important that a person makes good and effective use out of these Hadoop Training programs which the Hadoop community around the world is increasing and trending at a phenomenal pace and the leading names in the IT sector are looking for professionals who are equipped with the necessary skillset.
Such Hadoop Training programs help the individual realize the importance that is being placed on big data around the world. It analyzes the insights of the data which ensure that the reporting and dashboard is managed effectively. Considering the growing importance and the potential job market for individuals who poses sound knowledge regarding Hadoop and big data, it is imperative that the opportunity of equipping oneself with the Hadop Training should be capitalized as soon as possible.
Understanding the importance of big data, compiling and organizing it in a systematic way and making sure that the big data is kept in such a way that it makes sense to a larger segment of the audience is a skill that is effectively imparted on the online audience in the Hadoop training programs. This helps save the organization a lot of hassle, money and time. Whereas on the personnel point of view, such skills ensure that their chances of being employed are higher than usual.
Thus, if you are looking to equip yourself with the latest trends in the field if IT, then Hadoop is what you should be effectively seeking out for. It is important for you to choose an online course that thoroughly covers the dynamics of the program on a holistic level, making sure that you get the understanding of Hadoop in a way that helps you make good use of your skills.
In the recent times, an increased demand for Hadoop has been seen around the world. Thus, if you are interested in knowing more about Hadoop and you are keen to get Hadoop Training then you've come to the right place. There are various online programs which are known for teaching the online audience about the art of Hadoop at the convenience of their home. By making use of online video training offers, such institutions are known for imparting the knowledge pertaining to Hadoop so that the online audience can utilize the skills and make good use of the Hadoop knowledge in progressing in their respective fields.
One of the highlights of Hadoop Training is the fact that it teaches an individual about the wide array of aspects which are attached to the big data. Such training programs help teach the online audience about the analytics and the reporting skills which are considered to be imperative in terms of effectively understanding the big data, all of which helps in improving the performance of the business on a holistic level. It is important that a person makes good and effective use out of these Hadoop Training programs which the Hadoop community around the world is increasing and trending at a phenomenal pace and the leading names in the IT sector are looking for professionals who are equipped with the necessary skillset.
Such Hadoop Training programs help the individual realize the importance that is being placed on big data around the world. It analyzes the insights of the data which ensure that the reporting and dashboard is managed effectively. Considering the growing importance and the potential job market for individuals who poses sound knowledge regarding Hadoop and big data, it is imperative that the opportunity of equipping oneself with the Hadop Training should be capitalized as soon as possible.
Understanding the importance of big data, compiling and organizing it in a systematic way and making sure that the big data is kept in such a way that it makes sense to a larger segment of the audience is a skill that is effectively imparted on the online audience in the Hadoop training programs. This helps save the organization a lot of hassle, money and time. Whereas on the personnel point of view, such skills ensure that their chances of being employed are higher than usual.
Thus, if you are looking to equip yourself with the latest trends in the field if IT, then Hadoop is what you should be effectively seeking out for. It is important for you to choose an online course that thoroughly covers the dynamics of the program on a holistic level, making sure that you get the understanding of Hadoop in a way that helps you make good use of your skills.
Sunday, 12 November 2017
Benefits of Big Data Processing
Ability to process 'Big Data' brings in multiple benefits, such as-
• Businesses can utilize outside intelligence while taking decisions
Access to social data from search engines and sites like facebook, twitter are enabling organizations to fine tune their business strategies.
• Improved customer service
Traditional customer feedback systems are getting replaced by new systems designed with 'Big Data' technologies. In these new systems, Big Data and natural language processing technologies are being used to read and evaluate consumer responses.
• Early identification of risk to the product/services, if any
• Better operational efficiency
'Big Data' technologies can be used for creating staging area or landing zone for new data before identifying what data should be moved to the data warehouse. In addition, such integration of 'Big Data' technologies and data warehouse helps organization to offload infrequently accessed data.
• Businesses can utilize outside intelligence while taking decisions
Access to social data from search engines and sites like facebook, twitter are enabling organizations to fine tune their business strategies.
• Improved customer service
Traditional customer feedback systems are getting replaced by new systems designed with 'Big Data' technologies. In these new systems, Big Data and natural language processing technologies are being used to read and evaluate consumer responses.
• Early identification of risk to the product/services, if any
• Better operational efficiency
'Big Data' technologies can be used for creating staging area or landing zone for new data before identifying what data should be moved to the data warehouse. In addition, such integration of 'Big Data' technologies and data warehouse helps organization to offload infrequently accessed data.
Saturday, 28 October 2017
Power of Hadoop
Apache Hadoop is an open source software project based on JAVA. Basically it is a framework that is used to run applications on large clustered hardware (servers). It is designed to scale up from a single server to thousands of machines, with a very high degree of fault tolerance. Rather than relying on high-end hardware, the reliability of these clusters comes from the software's ability to detect and handle failures of its own.
Credit for creating Hadoop goes to Doug Cutting and Michael J. Cafarella. Doug a Yahoo employee found it apt to rename it after his son's toy elephant "Hadoop". Originally it was developed to support distribution for the Nutch search engine project to sort out large amount of indexes.
In a layman's term Hadoop is a way in which applications can handle large amount of data using large amount of servers. First Google created Map-reduce to work on large data indexing and then Yahoo! created Hadoop to implement the Map Reduce Function for its own use.
Map Reduce: The Task Tracker- Framework that understands and assigns work to the nodes in a cluster. Application has small divisions of work, and each work can be assigned on different nodes in a cluster. It is designed in such a way that any failure can automatically be taken care by the framework itself.
HDFS- Hadoop Distributed File System. It is a large scale file system that spans all the nodes in a Hadoop cluster for data storage. It links together the file systems on many local nodes to make them into one big file system. HDFS assumes nodes will fail, so it achieves reliability by replicating data across multiple nodes.
Big Data being the talk of the modern IT world, Hadoop shows the path to utilize the big data. It makes the analytics much easier considering the terabytes of Data. Hadoop framework already has some big users to boast of like IBM, Google, Yahoo!, Facebook, Amazon, Foursquare, EBay etc. for large applications. Infact Facebook claims to have the largest Hadoop Cluster of 21PB. Commercial purpose of Hadoop includes Data Analytics, Web Crawling, Text processing and image processing.
Most of the world's data is unused, and most businesses don't even attempt to use this data to their advantage. Imagine if you could afford to keep all the data generated by your business and if you had a way to analyze that data. Hadoop will bring this power to an enterprise.
Credit for creating Hadoop goes to Doug Cutting and Michael J. Cafarella. Doug a Yahoo employee found it apt to rename it after his son's toy elephant "Hadoop". Originally it was developed to support distribution for the Nutch search engine project to sort out large amount of indexes.
In a layman's term Hadoop is a way in which applications can handle large amount of data using large amount of servers. First Google created Map-reduce to work on large data indexing and then Yahoo! created Hadoop to implement the Map Reduce Function for its own use.
Map Reduce: The Task Tracker- Framework that understands and assigns work to the nodes in a cluster. Application has small divisions of work, and each work can be assigned on different nodes in a cluster. It is designed in such a way that any failure can automatically be taken care by the framework itself.
HDFS- Hadoop Distributed File System. It is a large scale file system that spans all the nodes in a Hadoop cluster for data storage. It links together the file systems on many local nodes to make them into one big file system. HDFS assumes nodes will fail, so it achieves reliability by replicating data across multiple nodes.
Big Data being the talk of the modern IT world, Hadoop shows the path to utilize the big data. It makes the analytics much easier considering the terabytes of Data. Hadoop framework already has some big users to boast of like IBM, Google, Yahoo!, Facebook, Amazon, Foursquare, EBay etc. for large applications. Infact Facebook claims to have the largest Hadoop Cluster of 21PB. Commercial purpose of Hadoop includes Data Analytics, Web Crawling, Text processing and image processing.
Most of the world's data is unused, and most businesses don't even attempt to use this data to their advantage. Imagine if you could afford to keep all the data generated by your business and if you had a way to analyze that data. Hadoop will bring this power to an enterprise.
Sunday, 22 October 2017
6 Reasons You Must Switch Career to Big Data Now
Big Data has got a lot of young
professionals excited about the sterling career prospects and rightly so
due to the sheer promise that this new domain holds. Getting a foothold
in this exciting arena can take your career places, for sure. First
let’s put things into perspective about Big Data. Read on.
These nuggets of information will convince you of the preponderance and inevitability of Big Data:
- Data production will be 44 times greater in 2020 than it was in 2009 – wikibon
- Bad data or poor data quality costs US businesses $600 billion annually – TDWI
According to TechCrunch study,
we shall see an overwhelming proliferation of smartphones in the near
future and it is estimated that we will have over 6 Billion of them by
2020. Also, did you know that by improving the data accessibility by a
mere 10% can raise the bottom line of a Fortune 1000 company by as much
as $65 million! Here is another eye opener – today only about 0.5% of the data at our disposal is every analyzed or utilized according to research from MIT Technology Review. So just imagine the potential of what we can do with Big Data in the near future.
Get the LinuxWorld Combo Pack Big Data Training Course to stay ahead of the curve!
Seven reasons why you should switch over to a career in Big Data now:
1. Big Shortage of Skilled Professionals
As per a report from the International Data Corporation (IDC) there is a serious shortage of skilled workforce in the Big Data sphere. People
with deep analytical expertise will be needed to the tune of 181,000 by
2018 and the need for people with skills in data management and
interpretation could be five times this number, as per this IDC Report.
It would be prudent to expect that the Big Data market would be worth at least $46.34 billion by 2018 since that is what IDC has forecast.
There will be huge upside in the various related fields of Big Data
like software, services and infrastructure in the next five years. The
rate at which Hadoop will grow will also be quite astounding.
2. The Massive IoT is just around the corner
The Internet of Things is on the cusp of
a major boom. IoT is the set of devices, sensors, objects and all kinds
of things that will be connected to the Internet in the grand scheme of
things. There will be a lot of machine to machine data exchange in the
not so distant future.
So it would be safe to say that the data
of future would not be limited to the spreadsheet data that we are so
used to. There will be all kinds of data and all this needs processing
and analyzing capabilities on an unprecedented scale. Most of this data
will be unstructured or semi-structured at best and there is an urgent
need of technologies and skills to make sense of it all.
This is a no-brainer – Big Data Training
means big bucks. For the professionals with the right skills the salary
can go through the roof and there is always another competing
organization that is ready to top the already exorbitant salary that the
Big Data professional is earning.
According to a salary survey from O’Reilly Media it has been proven that Big Data sits at the very top of the salary ladder. The job search portal Indeed says that the average salary that a Big Data professional can command is about $114,000 per annum!
4. Rapid Growth in Career
With Big Data growing at such a torrid
pace how could your Big Data career possibly grow any slower? The trick
for Big Data professionals is to learn and get trained in the next big
thing in Big Data. This could be a new technology or a process that is
finding much favour among the giants in the Big Data sphere like Google,
Amazon, Facebook, IBM and the ilk.
In the field of Big Data, the professionals who show much promise can expect rapid promotions, blitzkrieg career growth and
5. Job Satisfaction: Never a dull moment at office
The job of Big Data professionals might
look like any other nine-to-five job for the uninitiated but those
working in this domain know better. Merit wins big time in this field
and you need to explore ways to add value to the company that you are
working with in hitherto unheard ways. The technology can do only so
much but it is the sheer human ingenuity that adds the ultimate value
and improves the revenue and profits of any organizations. Expect a lot
of unguided exploration, exciting discoveries, newer ways of doing
things and ‘aha moments’ at your office if you are in this promising Big
Data field.
6. Vast Field with Big Job Opportunities
In the world of Big Data we are
presently playing on a chessboard so big we are not able to see the
entire board. Hadoop is just the tip of the iceberg. Expect newer
technologies to come real thick and fast. Also organizations regardless
of their industries need professionals with diverse skills sets. Here
are some of the job titles that companies are looking out for:
Saturday, 23 September 2017
Know More About Apache Hadoop Software Training and Big Data Technology
As a regular Internet visitor, you might have come across many websites. Have you ever thought of that no two websites are alike in structure, layout, color theme, graphics, texts and presentation of contents? This is because of the handiwork of website developers using assorted software solutions, and web designing and development technologies. As of today, the World Wide Web network is bubbling with more than 634 million websites and growing. Newer additions in technologies and software applications get invented by experts and offered for use for web developers constantly. Apache Hadoop Software is one such latest sophisticated solution; another is Big Data technology to handle huge data sets inside websites.
Here is an overview of Apache Hardware and where you can get suitable training for making use of this software solution. It is too technical to explain the intricacies of Apache Hardware here. Suffice it to understand what is what about this software and where it is useful. In the Internet World, there are many software solutions developed and distributed for free as Open Source and for a price. Apache Hadoop is Open Source software.
Apache Hadoop is mainly used to support data-intensive web applications. Simply it can divide software applications relating to huge data clusters, into small fragments for easy understanding, recording and repeated usage. For programming Apache Hadoop the ideal computer programming language is Java; many other languages can also be used provided they are streamlined to implement the parts of Apache Hadoop software. With more and more end-users for this software solutions coming, they become contributors for refining with latest additions of Apache Hadoop platform.
Apache Hadoop is gaining rapid popularity, as this is used by many world-renowned websites like Google, Yahoo, Facebook, Amazon, Apple, IBM etc. These big names denote the importance of this sophisticated software for commercial use, in today's intensive competition of Internet Marketing. No wonder many web developers and individual software developers are keen in getting online training in this technically-advanced software solution.
Here it is important to learn how Big Data technology is clubbed with Apache Hadoop software training. There are several commonly used software applications to create, handle, manage, control, and maintain data-bases all over the corporate world in computers. Your head will be reeling how much of data is created and transmitted every day, with these common data-creation software. Yet when compared to Big Data technology, which operates in petabytes for creation of complex data sets, these are dwarfed in size.
A few examples where Big Data technology is put into use will help understand the magnanimity of it. Internet Search Indexing, scientific researches such as genomics, atmospheric science, biological, biochemical, astronomy, medical records, military surveillance, and photography archives, social networks and big ecommerce websites etc. are some of the end users for Big Data technology.
Here is an overview of Apache Hardware and where you can get suitable training for making use of this software solution. It is too technical to explain the intricacies of Apache Hardware here. Suffice it to understand what is what about this software and where it is useful. In the Internet World, there are many software solutions developed and distributed for free as Open Source and for a price. Apache Hadoop is Open Source software.
Apache Hadoop is mainly used to support data-intensive web applications. Simply it can divide software applications relating to huge data clusters, into small fragments for easy understanding, recording and repeated usage. For programming Apache Hadoop the ideal computer programming language is Java; many other languages can also be used provided they are streamlined to implement the parts of Apache Hadoop software. With more and more end-users for this software solutions coming, they become contributors for refining with latest additions of Apache Hadoop platform.
Apache Hadoop is gaining rapid popularity, as this is used by many world-renowned websites like Google, Yahoo, Facebook, Amazon, Apple, IBM etc. These big names denote the importance of this sophisticated software for commercial use, in today's intensive competition of Internet Marketing. No wonder many web developers and individual software developers are keen in getting online training in this technically-advanced software solution.
Here it is important to learn how Big Data technology is clubbed with Apache Hadoop software training. There are several commonly used software applications to create, handle, manage, control, and maintain data-bases all over the corporate world in computers. Your head will be reeling how much of data is created and transmitted every day, with these common data-creation software. Yet when compared to Big Data technology, which operates in petabytes for creation of complex data sets, these are dwarfed in size.
A few examples where Big Data technology is put into use will help understand the magnanimity of it. Internet Search Indexing, scientific researches such as genomics, atmospheric science, biological, biochemical, astronomy, medical records, military surveillance, and photography archives, social networks and big ecommerce websites etc. are some of the end users for Big Data technology.
Wednesday, 13 September 2017
An Insight Into Big Data Analytics Using Hadoop
he large heap of data generated everyday is giving rise to the Big Data and a proper analysis of this data is getting the necessity for every organization. Hadoop, serves as a savior for Big Data Analytics and assists the organizations to manage the data effectively.
Big Data Analytics
The process of gathering, regulating and analyzing the huge amount of data is called the Big Data Analytics. Under this process, different patterns and other helpful information is derived that helps the enterprises in identifying the factors that boost up the profits.
What is it required?
For analyzing the large heap of data, this process turns very helpful, as it makes use of the specialized software tools. The application also helps in giving the predictive analysis, data optimization, and text mining details. Hence, it needs some high-performance analytics.
The processes consist of functions that are highly integrated and provides the analytics that promise high-performance. When an enterprise uses the tools and the software, it gets an idea about making the apt decisions for the businesses. The relevant data is analyzed and studied to know the market trends.
What Challenges Does it Face?
Numerous organizations get through various challenges; the reason behind is the large number of data saved in various formats, namely structured and unstructured forms. Also the sources differ, as the data is gathered from different sections of the organization.
Therefore, breaking down the data that is stored in different places or at different systems, is one of the challenging tasks. Another challenge is to sort the unstructured data in the way that it becomes as easily available as the accessibility of structured data.
How is it used in Recent Days?
The breaking down of data into small chunks helps the business to a high extent and helps in the transformation and achieving growth. The analysis also helps the researchers to analyze the human behavior and the trend of responses toward particular activity, decoding innumerable human DNA combinations, predict the terrorists plan for any attack by studying the previous trends, and studying the different genes that are responsible for specific diseases.
Big Data Analytics
The process of gathering, regulating and analyzing the huge amount of data is called the Big Data Analytics. Under this process, different patterns and other helpful information is derived that helps the enterprises in identifying the factors that boost up the profits.
What is it required?
For analyzing the large heap of data, this process turns very helpful, as it makes use of the specialized software tools. The application also helps in giving the predictive analysis, data optimization, and text mining details. Hence, it needs some high-performance analytics.
The processes consist of functions that are highly integrated and provides the analytics that promise high-performance. When an enterprise uses the tools and the software, it gets an idea about making the apt decisions for the businesses. The relevant data is analyzed and studied to know the market trends.
What Challenges Does it Face?
Numerous organizations get through various challenges; the reason behind is the large number of data saved in various formats, namely structured and unstructured forms. Also the sources differ, as the data is gathered from different sections of the organization.
Therefore, breaking down the data that is stored in different places or at different systems, is one of the challenging tasks. Another challenge is to sort the unstructured data in the way that it becomes as easily available as the accessibility of structured data.
How is it used in Recent Days?
The breaking down of data into small chunks helps the business to a high extent and helps in the transformation and achieving growth. The analysis also helps the researchers to analyze the human behavior and the trend of responses toward particular activity, decoding innumerable human DNA combinations, predict the terrorists plan for any attack by studying the previous trends, and studying the different genes that are responsible for specific diseases.
Friday, 8 September 2017
Top Two Concerns of Big Data Hadoop Implementation
In general, data can be classified into three categories. Any data which can be stored in databases can be called as Structured data. For example, transaction records of online purchase can be stored in databases. Hence, it can be called as Structured data. Some data can be partially stored in databases which can be called as Semi-Structured data. For example, the data on the XML records can be partially stored in databases and it can be called as Semi Structured Data.
The other forms of data which will not fit into these two categories are called as Unstructured Data. To name a few, data from social media sites, web logs cannot be stored analysed and processed in databases, therefore it is categorised as Unstructured Data. The other term used for Unstructured Data is Big Data.
According to NASSCOM, Structured Data accounts for 10% of the total data that exists today in the Internet. It accounts for 10% of semi-structured data and the remaining 80% of data comes under Unstructured Data. In general, organizations use analysis of Structured and Semi Structured Data using traditional data analytics tools. There was no sophisticated tools available to analyse the Unstructured Data till the Map Reduce framework which was developed by Google. Later, Apache developed a framework called "Hadoop" which analyses all these Data and reveals information which will be of great help for business to take better decisions.
Hadoop has already proved its importance in several areas. For example, according to NASSCOM, many organizations have started using Big Data analytics. National Oceanic and Atmosphere Administration (NOAA), National Aeronautics and Space Administration (NASA) and several pharmaceutical and energy companies have started using big data analytics extensively to predict their customer behaviour.
According to a recent research from Nemertes group, organizations perceive value in Big Data analytics and planning to have a better leverage in reaping the benefits of Big Data Analytics. The New York Times is using Big Data tools for text analysis, and Walt Disney Company use them to correlate and understand customer behaviour in all of its stores and theme parks. Indian IT companies such as TCS, Wipro, Infosys and other key players have also started to reap the immense potential which Big Data continues to offer.
This clearly shows that Big Data is an emerging area and many companies have started to explore new opportunities. Meanwhile, usage Big Data is proving to be worthwhile but at the same time it may also be noted that privacy and data protection concerns have also risen.
The concern about Big Data analytics is very much valid from the viewpoint of privacy. Let me give a very simple example. Nowadays I am very much sure that most of us use Social media such as Face book, Twitter and many other social forums and most of us watch videos on YouTube. Imagine these websites using Big Data Analytical tools to identify your activity on the Internet, to analyse data, your search behaviour and the content you have watched in social media. Through Big Data your activity on the Social Media Forum can be clearly identified. This is a blatant violation of your privacy. Further, just imagine the organization is sharing the data from the analysis to a few marketing agencies, this in turn creates more privacy issues.
The other forms of data which will not fit into these two categories are called as Unstructured Data. To name a few, data from social media sites, web logs cannot be stored analysed and processed in databases, therefore it is categorised as Unstructured Data. The other term used for Unstructured Data is Big Data.
According to NASSCOM, Structured Data accounts for 10% of the total data that exists today in the Internet. It accounts for 10% of semi-structured data and the remaining 80% of data comes under Unstructured Data. In general, organizations use analysis of Structured and Semi Structured Data using traditional data analytics tools. There was no sophisticated tools available to analyse the Unstructured Data till the Map Reduce framework which was developed by Google. Later, Apache developed a framework called "Hadoop" which analyses all these Data and reveals information which will be of great help for business to take better decisions.
Hadoop has already proved its importance in several areas. For example, according to NASSCOM, many organizations have started using Big Data analytics. National Oceanic and Atmosphere Administration (NOAA), National Aeronautics and Space Administration (NASA) and several pharmaceutical and energy companies have started using big data analytics extensively to predict their customer behaviour.
According to a recent research from Nemertes group, organizations perceive value in Big Data analytics and planning to have a better leverage in reaping the benefits of Big Data Analytics. The New York Times is using Big Data tools for text analysis, and Walt Disney Company use them to correlate and understand customer behaviour in all of its stores and theme parks. Indian IT companies such as TCS, Wipro, Infosys and other key players have also started to reap the immense potential which Big Data continues to offer.
This clearly shows that Big Data is an emerging area and many companies have started to explore new opportunities. Meanwhile, usage Big Data is proving to be worthwhile but at the same time it may also be noted that privacy and data protection concerns have also risen.
The concern about Big Data analytics is very much valid from the viewpoint of privacy. Let me give a very simple example. Nowadays I am very much sure that most of us use Social media such as Face book, Twitter and many other social forums and most of us watch videos on YouTube. Imagine these websites using Big Data Analytical tools to identify your activity on the Internet, to analyse data, your search behaviour and the content you have watched in social media. Through Big Data your activity on the Social Media Forum can be clearly identified. This is a blatant violation of your privacy. Further, just imagine the organization is sharing the data from the analysis to a few marketing agencies, this in turn creates more privacy issues.
Wednesday, 30 August 2017
Why Big Data and Hadoop Training Is a Must for Organizations
Business is never been a cake walk and it consists of lots of information, data and other skills. With the technological advancements professionals and learners need to keep themselves updated and match the pace. For an example, sorting and tracking Big Data requires enough skilled man power.
If sources are to be believed, there are institutes which have started offering Big Data and Hadoop Training. Both professionals and newbie learners are looking for these trainings for their career betterment.
This article is about why Big Data and Hadoop Training are important and where one can avail these courses? Well, to start with, Big Data is one terminology that explains a huge volume of data. Big Data is both structured and unstructured data which enter to any business on day to day basis. At the same time, one can easily differentiate the numbers of data they can actually count on. Most important thing is that what the organizations will do with those data.
Companies prefer analyzing those data and get all the insights which can lead them to take the better decisions and accomplish the business moves with the right strategy. Thus Big Data plays an important role for any organization for the development of that particular organization. At the same time, Hadoop is an open source software platform for collecting data and running various applications in bulk for serving hardware.
Hadoop is the one of the efficient sources that offers huge data storage of different kinds of data. This open source platform has the ability of enormous processing authority and has the capability to control limitless synchronized jobs.
Are you looking for Big Data and Hadoop training? Then you have landed on the right article. As per different sources, there are different organizations which have started offering different Hadoop courses for Hadoop Developer, Hadoop Admin and Hadoop Data Analytics. Thus, enrollment can be done according to the preference areas and as per the professional requirements.
All you need to do is just contact the institute and ask for the preferred courses and these short-term strategic professional courses will be just the best in shaping your career graph. Most of these organizations have their own Live Support and Drop a Query form for the sake of communication.
If sources are to be believed, there are institutes which have started offering Big Data and Hadoop Training. Both professionals and newbie learners are looking for these trainings for their career betterment.
This article is about why Big Data and Hadoop Training are important and where one can avail these courses? Well, to start with, Big Data is one terminology that explains a huge volume of data. Big Data is both structured and unstructured data which enter to any business on day to day basis. At the same time, one can easily differentiate the numbers of data they can actually count on. Most important thing is that what the organizations will do with those data.
Companies prefer analyzing those data and get all the insights which can lead them to take the better decisions and accomplish the business moves with the right strategy. Thus Big Data plays an important role for any organization for the development of that particular organization. At the same time, Hadoop is an open source software platform for collecting data and running various applications in bulk for serving hardware.
Hadoop is the one of the efficient sources that offers huge data storage of different kinds of data. This open source platform has the ability of enormous processing authority and has the capability to control limitless synchronized jobs.
Are you looking for Big Data and Hadoop training? Then you have landed on the right article. As per different sources, there are different organizations which have started offering different Hadoop courses for Hadoop Developer, Hadoop Admin and Hadoop Data Analytics. Thus, enrollment can be done according to the preference areas and as per the professional requirements.
All you need to do is just contact the institute and ask for the preferred courses and these short-term strategic professional courses will be just the best in shaping your career graph. Most of these organizations have their own Live Support and Drop a Query form for the sake of communication.
Tuesday, 22 August 2017
Become a Certified Big Data Practitioner and Learn About the Hadoop Ecosystem
A report by Forbes estimates that big data & Hadoop market is growing at a CAGR of 42.1% from 2015 and it will touch the mark of $99.31 billion by 2022. Another report from Mckinsey estimates a shortage of some 1.5 million big data experts by 2018. The findings of both the reports clearly suggest that market for big data analytics is growing worldwide at a massive rate and this trend looks to benefit IT professionals in a big way. After all, a big data hadoop certification is about gaining in-depth knowledge of the big data framework and becoming familiar with the Hadoop ecosystem.
More so, the objective of the training is to learn the use of Hadoop and Spark, together with gaining familiarity with HDFS, YARN and MapReduce. The participants learn to process and analyze big datasets, and also gain information in regard to data ingestion with the use of Sqoop and Flume. The training will offer the knowledge and mastery of real-time data processing to trainees who can also learn the ways to create, query and transform data forms of any scale. Anyone to the training will be able to master the concepts of Hadoop framework and learn its deployment in any environment.
Similarly, an enrollment in big data Hadoop training will help IT professionals learn different major components of Hadoop ecosystems such as Pig, Hive, Impala, Flume, Sqoop, Apache Spark and Yarn and implement them on projects. They will also learn about the ways to work with HDFS and YARN architecture for storage and resource management. The course is designed to also enrich trainees with the knowledge of MapReduce, its characteristics and its assimilation. The participants can also get to know how to ingest data with the help of Flume and Sqoop and how to create tables and database in Hive and Impala.
What's more, the training teaches about Impala and Hive for portioning purposes and also imparts knowledge about different types of file formats to work with. The trainees can expect to understand all about Flume, including its configurations and then become familiar with HBase and its architecture and data storage. Some of other major aspects to learn in the training include Pig components, Spark applications and RDD in detail. The training is also good for understanding Spark SQL and knowing about various interactive algorithms. All this information will be particularly helpful to those IT professionals planning to move into the big data domain
More so, the objective of the training is to learn the use of Hadoop and Spark, together with gaining familiarity with HDFS, YARN and MapReduce. The participants learn to process and analyze big datasets, and also gain information in regard to data ingestion with the use of Sqoop and Flume. The training will offer the knowledge and mastery of real-time data processing to trainees who can also learn the ways to create, query and transform data forms of any scale. Anyone to the training will be able to master the concepts of Hadoop framework and learn its deployment in any environment.
Similarly, an enrollment in big data Hadoop training will help IT professionals learn different major components of Hadoop ecosystems such as Pig, Hive, Impala, Flume, Sqoop, Apache Spark and Yarn and implement them on projects. They will also learn about the ways to work with HDFS and YARN architecture for storage and resource management. The course is designed to also enrich trainees with the knowledge of MapReduce, its characteristics and its assimilation. The participants can also get to know how to ingest data with the help of Flume and Sqoop and how to create tables and database in Hive and Impala.
What's more, the training teaches about Impala and Hive for portioning purposes and also imparts knowledge about different types of file formats to work with. The trainees can expect to understand all about Flume, including its configurations and then become familiar with HBase and its architecture and data storage. Some of other major aspects to learn in the training include Pig components, Spark applications and RDD in detail. The training is also good for understanding Spark SQL and knowing about various interactive algorithms. All this information will be particularly helpful to those IT professionals planning to move into the big data domain
Saturday, 5 August 2017
DevOps Automation for Faster and Continuous Product Release
DevOps
Although the above example is somewhat crude it is a fair assessment of what application development can be like end-to-end. Everyone in the industry knows that this is the 'normal' state of affairs and accept that it is less than perfect. DevOps has begun to appear on the scene as the answer to the traditional silo approach. DevOps attempts to remove the silos and replace them with a collaborative and inclusive activity that is the Project. Application Development and Solution Design benefit from DevOps principles.
What needs to be done to remove silos:
Change the working culture
Remove the walls between teams (and you remove the silos)
Keys:
Communication, Collaboration, Integration and Information Sharing
Easy to say and hard to do.
Most SMEs like to keep their information to themselves. Not true of all but, of many. It's part of the traditional culture that has developed over many years. Working practices have made change difficult. Management of change is one of the most challenging tasks any company can embark on. Resistance will be resilient as it is important that people give up something to gain something. Making it clear what the gains are is imperative. People will change their attitudes and behaviours but, you have to give them really good reasons to do so. I've found that running multi-discipline workshops for the SMEs has proven an effective method of encouraging information-sharing and the breaking down of those 'pit-walls'.
Explaining to the teams what DevOps is and what it is supposed to achieve is the first part of the educational process. The second is what needs to be done.
State specific, measurable objectives:
Implement an organization structure that is 'flat'. If we espouse horizontal scaling, why not horizontal organizations?
Each App-Dev or Solution-Dev is a project and the team is end-to-end across the disciplines
Implement ongoing informational exchange and reviews
Make sure that everyone signs up to DevOps and understands the paradigm
What is DevOps
Just like the Cloud paradigm it is simply another way of doing something. Like Cloud it has different definitions depending on to whom you are speaking at the time.
Wikipedia states: Because DevOps is a cultural shift and collaboration between development and operations, there is no single DevOps tool, rather a set or "toolchain" consisting of multiple tools. Generally, DevOps tools fit into one or more categories, which is reflective of the software development and delivery process.
I don't think that this is all DevOps is. The inference is that DevOps is concerned only with application development and operations. I do not believe that. I believe that DevOps is a paradigm and that like other IT 'standards' and paradigms it is relevant to all IT and not just applications. By removing the partitions between each practice in the chain and having all the key players involved from day one, as part of an inclusive and collaborative team, the cycle of application development and solution design becomes a continuous process that doesn't have to divert to consult each required expert. No-one needs to throw a document over the wall to the next crew. Each document is written within the collaboration process and this has to make the document more relevant and powerful. Imagine that the project team is always in the same room from concept to deployment and each expert is always available to comment on and add to each step of that project. How much better than the traditional method where it can take days to get an answer to a simple question, or to even find the right person to ask.
The mantra is: Develop, Test, Deploy, Monitor, Feedback and so on. This sounds application-orientated. In fact, it can apply to the development of any IT solution. Like ITIL, TOGAF and the Seven Layer Reference Model it can be applied to any and all IT activities from development right through to support services. DevOps puts us all on the same page from the start to the finish.
Although the above example is somewhat crude it is a fair assessment of what application development can be like end-to-end. Everyone in the industry knows that this is the 'normal' state of affairs and accept that it is less than perfect. DevOps has begun to appear on the scene as the answer to the traditional silo approach. DevOps attempts to remove the silos and replace them with a collaborative and inclusive activity that is the Project. Application Development and Solution Design benefit from DevOps principles.
What needs to be done to remove silos:
Change the working culture
Remove the walls between teams (and you remove the silos)
Keys:
Communication, Collaboration, Integration and Information Sharing
Easy to say and hard to do.
Most SMEs like to keep their information to themselves. Not true of all but, of many. It's part of the traditional culture that has developed over many years. Working practices have made change difficult. Management of change is one of the most challenging tasks any company can embark on. Resistance will be resilient as it is important that people give up something to gain something. Making it clear what the gains are is imperative. People will change their attitudes and behaviours but, you have to give them really good reasons to do so. I've found that running multi-discipline workshops for the SMEs has proven an effective method of encouraging information-sharing and the breaking down of those 'pit-walls'.
Explaining to the teams what DevOps is and what it is supposed to achieve is the first part of the educational process. The second is what needs to be done.
State specific, measurable objectives:
Implement an organization structure that is 'flat'. If we espouse horizontal scaling, why not horizontal organizations?
Each App-Dev or Solution-Dev is a project and the team is end-to-end across the disciplines
Implement ongoing informational exchange and reviews
Make sure that everyone signs up to DevOps and understands the paradigm
What is DevOps
Just like the Cloud paradigm it is simply another way of doing something. Like Cloud it has different definitions depending on to whom you are speaking at the time.
Wikipedia states: Because DevOps is a cultural shift and collaboration between development and operations, there is no single DevOps tool, rather a set or "toolchain" consisting of multiple tools. Generally, DevOps tools fit into one or more categories, which is reflective of the software development and delivery process.
I don't think that this is all DevOps is. The inference is that DevOps is concerned only with application development and operations. I do not believe that. I believe that DevOps is a paradigm and that like other IT 'standards' and paradigms it is relevant to all IT and not just applications. By removing the partitions between each practice in the chain and having all the key players involved from day one, as part of an inclusive and collaborative team, the cycle of application development and solution design becomes a continuous process that doesn't have to divert to consult each required expert. No-one needs to throw a document over the wall to the next crew. Each document is written within the collaboration process and this has to make the document more relevant and powerful. Imagine that the project team is always in the same room from concept to deployment and each expert is always available to comment on and add to each step of that project. How much better than the traditional method where it can take days to get an answer to a simple question, or to even find the right person to ask.
The mantra is: Develop, Test, Deploy, Monitor, Feedback and so on. This sounds application-orientated. In fact, it can apply to the development of any IT solution. Like ITIL, TOGAF and the Seven Layer Reference Model it can be applied to any and all IT activities from development right through to support services. DevOps puts us all on the same page from the start to the finish.
Thursday, 7 July 2016
Hadoop and Big Data
Hadoop and Big Data are dramatically impacting business, yet the
exact relationship between Hadoop and Big Data remains open to
discussion
You might think of Hadoop as the horse and Big Data as the rider. Or perhaps more accurate: Hadoop as the tool and Big Data as the house being built. Whatever the analogy, these two technologies – both seeing rapid growth – are inextricably linked.
No matter how you define it, though, Big Data is increasingly the tool that sets businesses apart. Those that can reap competitive insights from a Big Data solution gain key advantage; companies unable to leverage this technology will fall behind.
Hadoop and Big Data: The Perfect Union?
Whether Hadoop and Big Data are the ideal match “depends on what you’re doing,” says Nick Heudecker, a Gartner analyst who specializes in Data Management and Integration.
“Hadoop certainly allows you to onboard a tremendous amount of data very quickly, without making any compromises about what you’re storing and what you’re keeping. And that certainly facilitates a lot of the Big Data discovery,” he says.
Hadoop offers a full ecosystem along with a single Big Data platform. It is sometimes called a “data operating system.” Source: Gartner
Mike Gualtieri, a Forrester analyst whose key coverage areas include Big Data strategy and Hadoop, notes that Hadoop is part of a larger ecosystem – but it’s a foundational element in that data ecosystem.
Who’s Choosing Hadoop as a Big Data Tool
Based on Gartner research, the industries most strongly drawn to Hadoop are those in banking and financial services. Additional Hadoop early adopters include “more generally, services, which we define as anyone selling software or IT services,” Heudecker says. Insurance, as well as manufacturing and natural resources also see Hadoop users.
Those are the kinds of industries that encounter more – and more diverse – kinds of data. “I think Hadoop certainly lends itself well to that, because now you don’t have to make compromises about what you’re going to keep and what you’re going to store,” Heudecker says. “You just store everything and figure it out later.”
Hadoop Headwinds
Yet not all is rosy in the world of Hadoop. Recent Gartner research about Hadoop adoption notes that “investment remains tentative in the face of sizable challenges around business value and skills.”
The May 2015 report, co-authored by Heudecker and Gartner analyst Merv Adrian, states:
"Despite considerable hype and reported successes for early adopters, 54 percent of survey respondents report no plans to invest at this time, while only 18 percent have plans to invest in Hadoop over the next two years. Furthermore, the early adopters don't appear to be championing for substantial Hadoop adoption over the next 24 months; in fact, there are fewer who plan to begin in the next two years than already have."
The Gartner report’s gloomiest news for Hadoop:
I asked Heudecker about these Hadoop impediments and he noted the lack of IT pros with top Hadoop skills:
“We talked with a large financial services organization, they were just starting their Hadoop journey,” he says, “and we asked, ‘Who helped you?’ And they said ‘Nobody, because the companies we called had just as much experience with Hadoop as we did.’ So when the largest financial service companies on the planet can’t find help for their Hadoop project, what does that mean for the global 30,000 companies out there?”
This lack of skilled tech pros for Hadoop is a true concern, Heudecker says. “That’s certainly being borne out in the data that we have, and in the conversations that we have with clients,” he says. “And I think it’s going to be a while before Hadoop skills are plentiful in the market.”
The Hadoop/Big Data Vendor Connection
A growing community of Hadoop vendors offer a byzantine array of solutions. Flavors and configurations abound. These vendors are leveraging the fact that Hadoop has a certain innate complexity – meaning buyers need some help. Hadoop is comprised of various software components, all of which need to work in concert. Adding potential confusion, different aspects of the ecosystem progress at varying speeds.
Handling these challenges is “one of the advantages of working with a vendor,” Heudecker says. “They do that work for you.” As mentioned, a key element of these solutions is SQL – Heudecker refers to SQL as “the Lingua Franca of data management.”
Is there a particular SQL solution that will be the perfect match for Hadoop?
“I think over the next 3-5 years, you’ll actually see not one SQL solution emerge as a winner, but you’ll likely see several, depending on what you want to do,” Heudecker says. “In some cases Hive may be your choice depending on certain use cases. In other cases you may want to use Drill or something like Presto, depending on what your tools will support and what you want to accomplish.”
As for winner or losers in the race for market share? “I think it’s too soon. We’ll be talking about survivors, not winners.”
The emerging community of vendors tends to tout one key attribute: ease of use. Matchett notes that, “If you go to industry events, it’s just chock full of startups saying, ‘Hey, we’ve got this new interface that allows the business [user] just to drag and drop and leverage Big Data without having to know anything.’”
Hadoop Appliances: Big Data in a Box
Even as Hadoop matures, there continues to be Big Data solutions that far outmatch it – at a higher price for those who need greater capability.
“There’s still definitely a gap between what a Teradata warehouse can do, or an IBM Netezza, Oracle Exadata, and Hadoop,” says Forrester’s Gualtieri. “I mean, if you need high concurrency, if you need tons of users and you’re doing really complicated queries that you need to have perform super fast, that’s just like if you’re in a race and you need race car.” In that case you simply need the best. “So, there’s still a performance gap, and there’s a lot of engineering work that has to be done.”
Big Data Debate: Hadoop vs. Spark, or Hadoop and Spark?
A discussion – or debate – is now raging within the Big Data community: sure, Hadoop is hot, but now Spark is emerging. Maybe Spark is better – some tech observers trumpet its advantages – and so Hadoop (some observers suggest) will soon fade from its high position.
Like Hadoop, Spark is a cluster computing platform (both Hadoop and Spark are Apache projects). Spark is earning a reputation as a good choice for complicated data processing jobs that need to be performed quickly. Its in-memory architecture and directed acyclic graph (DAG) processing is far faster than Hadoop’s MapReduce – at least at the moment. Yet Spark has its downsides. For instance, it does not have its own file system. In general, IT pros think of Hadoop is best for volume where Spark is best for speed, but in reality the picture isn’t that clear.
Hadoop and Big Data Future Speak: Data Gravity, Containers, IoT
Clearly, there’s been a lot of hype about Big Data, about how it’s the new Holy Grail of business decision making.
That hype may have run its course. “Big data is essentially turning into data,” opines Heudecker. “It’s time to get past the hype and start thinking about where the value is for your business.” The point: “Don’t treat Big Data as an end unto itself. It has to derive from a business need.”
As for Hadoop’s role in this, its very success may contain a paradox. With time, Hadoop may grow less visible. It may become so omnipresent that it’s no longer seen as a stand alone tool.
“Over time, Hadoop will eventually bake into your information infrastructure,” Huedecker says. “It should never have been an either/or choice. And it won’t be in the future. It will be that I have multiple data stores. I will use them depending on the SLAs I have to comply with for the business. And so you’ll have a variety of different data stores.”
Hadoop and Big Data are in many ways the perfect union – or at least they have the potential to be.
Hadoop is hailed as the open
source distributed computing platform that harnesses dozens – or
thousands – of server nodes to crunch vast stores of data. And Big Data earns massive buzz as the quantitative-qualitative science of harvesting insight from vast stores of data.
You might think of Hadoop as the horse and Big Data as the rider. Or perhaps more accurate: Hadoop as the tool and Big Data as the house being built. Whatever the analogy, these two technologies – both seeing rapid growth – are inextricably linked.
However, Hadoop and Big Data share the same “problem”: both are
relatively new, and both are challenged by the rapid churn that’s
characteristic of immature, rapidly developing technologies.
Hadoop was developed in 2006, yet it wasn’t until Cloudera’s launch in 2009 that it moved toward commercialization. Even years later it prompts mass disagreement. In June 2015 The New York Times offered the gloomy assessment that Companies Move On From Big Data Technology Hadoop. Furthermore, leading Big Data experts (see below) claim that Hadoop suffers major headwinds.
Similarly, while Big Data has been around for years – called
“business intelligence” long before its current buzz – it still creates
deep confusion. Businesses are unclear about how to harness its power.
The myriad software solutions and possible strategies leaves some users
only flummoxed. There’s backlash, too, due to its level of Big Data
hype. There’s even confusion about the term itself: “Big Data” has as
many definitions as people you’ll ask about it. It’s generally defined
as “the process of mining actionable insight from large quantities of
data,” yet it also includes machine learning, geospatial analytics and
an array of other intelligence uses.
No matter how you define it, though, Big Data is increasingly the tool that sets businesses apart. Those that can reap competitive insights from a Big Data solution gain key advantage; companies unable to leverage this technology will fall behind.
Big bucks are at stake. Research firm IDC forecasts that Big Data technology and services will grow at a 26.4% compound annual growth rate
through 2018, to become a $41.4 billion dollar global market. If
accurate, that forecast means it’s growing a stunning six times the rate
of the overall tech market.
Research by Wikibon
predicts a similar growth rate; the chart below reflects Big Data’s
exponential growth from just a few years ago. Given Big Data’s explosive
trajectory, it’s no wonder that Hadoop – widely seen as a key Big Data
tool – is enjoying enormous interest from enterprises of all sizes.
Hadoop and Big Data: The Perfect Union?
Whether Hadoop and Big Data are the ideal match “depends on what you’re doing,” says Nick Heudecker, a Gartner analyst who specializes in Data Management and Integration.
“Hadoop certainly allows you to onboard a tremendous amount of data very quickly, without making any compromises about what you’re storing and what you’re keeping. And that certainly facilitates a lot of the Big Data discovery,” he says.
However, businesses continue to use other Big Data technologies,
Heudecker says. A Gartner survey indicates that Hadoop is the third
choice for Big Data technology, behind Enterprise Data Warehouse and
Cloud Computing.
While Hadoop is a leading Big Data tool, it is not the top option for enterprise users.
It’s no surprise that the Enterprise Data Warehouse tops Hadoop as
the leading Big Data technology. A company’s complete history and
structure can be represented by the data stored in the data warehouse.
Moreover, Heudecker says, based on the Gartner user survey, “we see the
Enterprise Data Warehouse being combined with a variety of different
databases: SQL, graph databases, memory technologies, complex
processing, as well as stream processing.”
So while Hadoop is a key Big Data tool, it remains one contender
among many at this point. “I think there’s a lot of value in being able
to tell a cohesive federated story across multiple data stores,”
Huedecker says. That is, “Hadoop being used for some things; your data
warehouse being used for others. I don’t think anybody realistically
wants to put the whole of their data into a single platform. You need to
optimize to handle the potential workloads that you’re doing.”
Hadoop offers a full ecosystem along with a single Big Data platform. It is sometimes called a “data operating system.” Source: Gartner
Mike Gualtieri, a Forrester analyst whose key coverage areas include Big Data strategy and Hadoop, notes that Hadoop is part of a larger ecosystem – but it’s a foundational element in that data ecosystem.
“I would say ‘Hadoop and friends’ is a perfect match for
Big Data,” Gualtieri says. A variety of tools can be combined for best
results. “For example, you need streaming technology to process
real-time data. There’s software such as DataTorrent that runs on Hadoop, that can induce streaming. There’s Spark
[more on Spark later]. You might want to do batch jobs that are in
memory, and it’s very convenient, although not required, to run that
Spark cluster on a Hadoop cluster.”
Still, Hadoop’s position in the Big Data universe is
truly primary. “I would say Hadoop is a data operating system,”
Gualtieri says. “It’s a fundamental, general purpose platform. The
capabilities that it has those of an operating system: It has a file
system, it has a way to run a job.” And the community of vendors and
open source projects all feed into a healthy stream for Hadoop. “They’re
making it the Big Data platform.”
In fact, Hadoop’s value for Big Data applications goes
beyond its primacy as a data operating system. As Gualtieri sees it,
Hadoop is also an application platform. This capability is enabled by YARN, the cluster management technology that’s part of Hadoop (YARN stands for Yet Another Resource Manager.)
“YARN is really an important piece of glue here because
it allows innovation to occur in the Big Data community,” he says,
“because when a vendor or an open source project contributes something
new, some sort of new application, whether it’s machine learning,
streaming, a SQL engine, an ETL tool, ultimately, Hadoop becomes an
application platform as well as a data platform. And it has the
fundamental capability to handle all of these applications, and to
control the resources they use.”
YARN and HDFS provide Hadoop with a diverse array of capabilities.
Regardless of how technology evolves in the years ahead,
Hadoop will always have a place in the pioneering days of Big Data
infancy. There was a time when many businesses looked at their vast
reservoir of data – perhaps a sprawling 20 terabytes – and in essence
gave up. They assumed it was too big to be mined for insight.
But Hadoop changed that, notes Mike Matchett,
analyst with the Tenaja Group who specializes in Big Data. The
development of Hadoop meant “Hey, if you get fifty white node cluster
servers – they don’t cost you that much – you get commodity servers,
there’s no SAN you have to have because you can use HDFS and local disc,
you can do something with it. You can find [Big Data insights]. And
that was when [Hadoop] kind of took off.”
Based on Gartner research, the industries most strongly drawn to Hadoop are those in banking and financial services. Additional Hadoop early adopters include “more generally, services, which we define as anyone selling software or IT services,” Heudecker says. Insurance, as well as manufacturing and natural resources also see Hadoop users.
Those are the kinds of industries that encounter more – and more diverse – kinds of data. “I think Hadoop certainly lends itself well to that, because now you don’t have to make compromises about what you’re going to keep and what you’re going to store,” Heudecker says. “You just store everything and figure it out later.”
On the other hand, there are laggards, says Teneja
Group’s Matchett. “You see people who say, ‘We’re doing fine with our
structured data warehouse. There’s not a lot of real-time menus for the
marketing we’re doing yet or that we see the need for.’”
But these slow adopters will get on board, he says.
“They’ll come around and say, ‘If we have a website and we have any
user-tracking and it’s creating a quick stream of Big Data, we’re going
to have to use that for market data.’” And really, he asks, “Who
doesn’t have a website and a user base of some kind?”
Forrester’s Gualtieri notes that interest in Hadoop is
very high. “We did a Hadoop Wave,” he say, referring to the Forrester
report Big Data Hadoop Solutions.
“We evaluated and published that last year, and of all of the thousands
of Forrester documents published that year on all kinds of topics, it
was like the second most read document.” Driving this popularity is
Hadoop’s fundamental place as a data operating system, he says.
Furthermore, “The amounts of investments by – and I’m
not even talking about the startup guys – the investments by companies
like SAS, IBM, Microsoft, all of the commercial guys – their goal is to
make it easy and do more sophisticated things,” Gaultieri says. “So
there’s a lot of value being added.”
He foresees a potential scenario in which Hadoop is part
of every operating system. And while adoption is still growing, “I
estimate that in the next few years, the next 2-3 years, it will be 100
percent,” of enterprises will deploy Hadoop. Gaultieri refers to a
phenomenon he calls “Hadoopenomics,” that is, Hadoop’s ability to unlock
a full ecosystem of profitable Big Data scenarios, chiefly because
Hadoop offers lower cost storing and accessing of data, relative to a
sophisticated data warehouse. “It’s not as capable as a data warehouse,
but it’s good for many things,” he says.
Yet not all is rosy in the world of Hadoop. Recent Gartner research about Hadoop adoption notes that “investment remains tentative in the face of sizable challenges around business value and skills.”
The May 2015 report, co-authored by Heudecker and Gartner analyst Merv Adrian, states:
"Despite considerable hype and reported successes for early adopters, 54 percent of survey respondents report no plans to invest at this time, while only 18 percent have plans to invest in Hadoop over the next two years. Furthermore, the early adopters don't appear to be championing for substantial Hadoop adoption over the next 24 months; in fact, there are fewer who plan to begin in the next two years than already have."
“Only 26 percent of respondents claim to be either deploying,
piloting or experimenting with Hadoop, while 11 percent plan to invest
within 12 months and seven percent are planning investment in 24 months.
Responses pointed to two interesting reasons for the lack of intent.
First, several responded that Hadoop was simply not a priority. The
second was that Hadoop was overkill for the problems the business faced,
implying the opportunity costs of implementing Hadoop were too high
relative to the expected benefit.”
The Gartner report’s gloomiest news for Hadoop:
With such large incidence of organizations with no plans or
already on their Hadoop journey, future demand for Hadoop looks fairly
anemic over at least the next 24 months. Moreover, the lack of near-term
plans for Hadoop adoption suggest that, despite continuing enthusiasm
for the big data phenomenon, demand for Hadoop specifically is not
accelerating. The best hope for revenue growth for providers would
appear to be in moving to larger deployments within their existing
customer base."
“We talked with a large financial services organization, they were just starting their Hadoop journey,” he says, “and we asked, ‘Who helped you?’ And they said ‘Nobody, because the companies we called had just as much experience with Hadoop as we did.’ So when the largest financial service companies on the planet can’t find help for their Hadoop project, what does that mean for the global 30,000 companies out there?”
This lack of skilled tech pros for Hadoop is a true concern, Heudecker says. “That’s certainly being borne out in the data that we have, and in the conversations that we have with clients,” he says. “And I think it’s going to be a while before Hadoop skills are plentiful in the market.”
Gualtieri, however, voices quite a different view. The
idea that Hadoop faces a lack is skilled workers is “a myth” he says.
Hadoop is based on Java, he notes. “A large enterprise has lots of Java
developers, and Java developers over the years always have to learn new
frameworks. And guess what? Just take a couple of your good Java guys
and say, ‘Do this on Hadoop,’ and they will figure it out. It’s not that
hard.” Java developers will be able to get a sample app running that
can do simple tasks before long, he says.
These in-house, homegrown Hadoop experts enable cost
savings, he says. “So instead of looking for the high-priced Hadoop
experts who say, ‘I know Hadoop,’ what I see when I talk to a lot of
enterprises, I’m talking to people who have been there for ten years –
they just became the Hadoop expert.”
An additional factor makes Hadoop dead simple to adopt,
Gualtieri says: “That is SQL for Hadoop. SQL is known by developers.
It’s known by many business intelligence professionals, and even
business people and data analysis [professionals], right? It’s very
popular.
“And there are at least thirteen different SQL for Hadoop
query engines on Hadoop. So you don’t need to know a thing about
MapReduce. You don’t need to know anything about distributed data or
distributed jobs” to accomplish an effective query.
Gualtieri points to a diverse handful of Hadoop SQL solutions: “Apache Drill, Cloudera Impala, Apache Hive …Presto, HP Vertica has a solution, Pivotal Hawk, Microsoft Polybase…
Naturally all the database companies and data warehouse companies have a
solution. They’ve repurposed their engines. And then there the open
source firms.” All (or most) of these solutions tout their usability.
Matchett takes a middle ground between Heudecker’s view
that Hadoop faces a shortage of skilled workers and Gualtieri’s belief
that in-house Java developers and vendor solutions can fill the gap:
“There are plenty of places where people can get lots of
mileage out of it,” he says, referring to easy-to-use Hadoop
deployments – particularly AWS’s offering. “You and I can both go to
Amazon with a credit card and check out an EMR cluster,
which is a Hadoop cluster, and get it up and running without knowing
anything. You could do that in ten minutes with your Amazon account and
have a Big Data cluster.”
However, “at some level of professionalism or scale of
productivity, you’re going to need experts, still,” Matchett says. “Just
like you would with an RDBMS. It’s going to be pretty much analogous to
that.” Naturally these experts are more expensive and harder to find.
To be sure, there are easier solutions: “There are lots
of startup businesses that are committed to being cloud-based and
Web-based, and there’s no way they’re going to go run their Hadoop
clusters internally,” Matchett says. “They’re going to check them out of
the cloud.”
Again, though, at some point they may need top talent:
“They may still want a data scientist to solve their unique competitive
problem. They need the scientist to figure out what they can do
differently than their competitors or anybody else.”
A growing community of Hadoop vendors offer a byzantine array of solutions. Flavors and configurations abound. These vendors are leveraging the fact that Hadoop has a certain innate complexity – meaning buyers need some help. Hadoop is comprised of various software components, all of which need to work in concert. Adding potential confusion, different aspects of the ecosystem progress at varying speeds.
Handling these challenges is “one of the advantages of working with a vendor,” Heudecker says. “They do that work for you.” As mentioned, a key element of these solutions is SQL – Heudecker refers to SQL as “the Lingua Franca of data management.”
Is there a particular SQL solution that will be the perfect match for Hadoop?
“I think over the next 3-5 years, you’ll actually see not one SQL solution emerge as a winner, but you’ll likely see several, depending on what you want to do,” Heudecker says. “In some cases Hive may be your choice depending on certain use cases. In other cases you may want to use Drill or something like Presto, depending on what your tools will support and what you want to accomplish.”
As for winner or losers in the race for market share? “I think it’s too soon. We’ll be talking about survivors, not winners.”
The emerging community of vendors tends to tout one key attribute: ease of use. Matchett notes that, “If you go to industry events, it’s just chock full of startups saying, ‘Hey, we’ve got this new interface that allows the business [user] just to drag and drop and leverage Big Data without having to know anything.’”
He compares the rapid evolution in Hadoop tools to the
evolution of virtualization several years ago. If a vendor wants to make
a sale, simpler user interface is a selling point. Hadoop vendors are
hawking their wares by claiming, “‘We’ve got it functional. And now
we’re making it manageable,’” Matchett says. “‘We’re making it mature
and we’re adding security and remote-based access, and we’re adding
availability, and we’re adding ways for DevOps people to control it
without having to know a whole lot.’”
Even as Hadoop matures, there continues to be Big Data solutions that far outmatch it – at a higher price for those who need greater capability.
“There’s still definitely a gap between what a Teradata warehouse can do, or an IBM Netezza, Oracle Exadata, and Hadoop,” says Forrester’s Gualtieri. “I mean, if you need high concurrency, if you need tons of users and you’re doing really complicated queries that you need to have perform super fast, that’s just like if you’re in a race and you need race car.” In that case you simply need the best. “So, there’s still a performance gap, and there’s a lot of engineering work that has to be done.”
One development that he finds encouraging for Hadoop’s
growth is the rise of the Hadoop appliance. “Oracle has the appliance,
Teradata has the appliance, HP is coming out with an appliance based
upon their Moonshot, [there’s] Cray Computer, and others,” he notes,
adding Cisco to his list.
What’s happening now is far beyond what might be called
“appliance 1.0 for Hadoop,” Gualtieri says. That first iteration was
simply a matter of getting a cabinet, putting some nodes in it,
installing Hadoop and offering it to clients. “But what they’re doing
now is they’re saying, okay, ‘Hadoop looks like it’s here to stay. How
can we create an engineered solution that helps overcome some of the
natural bottlenecks of Hadoop? That helps IO throughput, uses more
caching, puts computer resources where they’re needed virtually?’ So,
now they’re creating a more engineered system.”
Matchett, too, notes that there’s renewed interest in Hadoop appliances after the first wave. “DDN, a couple years ago, had an HScaler appliance
where they packaged up their super-duper storage and compute modes and
sold it as a rack, and you could buy this Hadoop appliance.”
Appliances appeal to businesses. Customers like being
able to download Hadoop for free, but when it comes to turning it into a
workhorse, that task (as noted above) calls for expertise. It’s often
easier to simply buy a pre-built appliance. Companies “don't want to go
hire an expert and waste six months converging it themselves,” Matchett
says. “So, they can just readily buy an appliance where it’s all
pre-baked, like a VCE appliance such VBlock, or a hyper-converged
version that some other folks are considering selling. So, you buy a
rack of stuff…and it’s already running Hadoop, Spark, and so on.” In
short, less headaches, more productivity.
A discussion – or debate – is now raging within the Big Data community: sure, Hadoop is hot, but now Spark is emerging. Maybe Spark is better – some tech observers trumpet its advantages – and so Hadoop (some observers suggest) will soon fade from its high position.
Like Hadoop, Spark is a cluster computing platform (both Hadoop and Spark are Apache projects). Spark is earning a reputation as a good choice for complicated data processing jobs that need to be performed quickly. Its in-memory architecture and directed acyclic graph (DAG) processing is far faster than Hadoop’s MapReduce – at least at the moment. Yet Spark has its downsides. For instance, it does not have its own file system. In general, IT pros think of Hadoop is best for volume where Spark is best for speed, but in reality the picture isn’t that clear.
Spark’s proponents point out that processing is far faster when the data set fits in memory.
“I think there’s an awful lot of hype out there,”
Gualtieri says. To be sure, he thinks highly of Spark and its
capabilities. And yet: “There are some things it doesn’t do very well.
Spark, for example, doesn’t have its own file system. So it’s like a car
without wheels.”
The debate doesn’t take into consideration how either
Spark or Hadoop might evolve – quickly. For instance, Gualtieri says,
“Some people will say Hadoop’s much slower than Spark because it’s
disc-based. Does it have to be disc-based six months from now? In fact,
part of the Hadoop community is working on supporting SSD cards, and
then later, files and memory. So people need to understand that,
especially now with this Spark versus Hadoop fight.”
The two processing engines are often compared. “Now,
most Hadoop people will say MapReduce is lame compared to the [Spark]
DAG engine,” Gualtieri says. “The DAG engine is superior to MapReduce
because it helps the programmer parallelize jobs much better. But who’s
to say that someone couldn’t write a DAG engine for Hadoop? They could.
So, that’s what I’m saying: This is not a static world where code bases
are frozen. And this is what annoys me about the conversations, is that
it’s as if these technologies are frozen in time and they’re not going
to evolve and get better.” But of course they are – and likely sooner
rather than later.
Like Hadoop, Spark includes an ever growing array of tools and features to augment the core platform. Source: Forrester Research
Ultimately the Hadoop-Spark debate may not matter,
Matchett says, because the two technologies may essentially merge, in
some form. In any case, “What you’re still going to have is a commodity
Big Data ecosystem, and whether the Spark project wins, or the Map
Reduce project wins, Spark is part of Apache now. It’s all part of that
system.”
As Hadoop and Spark evolve, “They could merge. They
could marry. They could veer off in different directions. I think what’s
important, though, is that you can run Hadoop and Spark jobs in the
same cluster.”
Plenty of options confront a company seeking to assemble
a Big Data toolset, Matchett points out. “If you were to white board it
and say ‘I’ve got this problem I want to solve, do I use Map Reduce? Do
I use Spark? Do I use one of the other dozen things that are out there?
Or a SQL database or a graph database?’ That’s a wide open discussion
about architecture.” Ultimately there are few completely right and wrong
answers, only a question of which solution(s) work best for a specific
scenario.
Instead of a choosing one or the other, many Big
Data practitioners point to a scenario in which Hadoop and Spark work in
tandem to enable the best of both.
Clearly, there’s been a lot of hype about Big Data, about how it’s the new Holy Grail of business decision making.
That hype may have run its course. “Big data is essentially turning into data,” opines Heudecker. “It’s time to get past the hype and start thinking about where the value is for your business.” The point: “Don’t treat Big Data as an end unto itself. It has to derive from a business need.”
As for Hadoop’s role in this, its very success may contain a paradox. With time, Hadoop may grow less visible. It may become so omnipresent that it’s no longer seen as a stand alone tool.
“Over time, Hadoop will eventually bake into your information infrastructure,” Huedecker says. “It should never have been an either/or choice. And it won’t be in the future. It will be that I have multiple data stores. I will use them depending on the SLAs I have to comply with for the business. And so you’ll have a variety of different data stores.”
In Gualtieri’s view, the near term future of Hadoop is
based on SQL. “What I would say this year is that SQL on Hadoop is the
killer app for Hadoop,” he says. “It’s going to be the application on
Hadoop that allows companies to adopt Hadoop very easily.” He predicts:
“In two years from now you’re going to see companies building
applications specifically that run on Hadoop.”
Looking ahead, Gualtieri sees the massive Big Data potential of the Internet of Things
as a boost for Hadoop. For instance, he points to the ocean of data
created by cable TV boxes. All that data needs to be stored somewhere.
“You’re probably going to want to dump that in the most
economical place possible, which is HDFS [in Hadoop],” he says, “and
then you’re probably going to want to analyze it to see if you can
predict who’s watching the television at that time, and predict the
volumes [of user trends], and you’ll probably do that in the Hadoop
cluster. You might do it in Spark, too. You might take a subset to
Spark.”
He adds, “A lot of the data that’s landed in Hadoop has
been very much about moving data from data warehouses and transactional
systems into Hadoop. It’s a more central location. But I think for
companies where IOT is important, that’s going to create even more of a
need for a Big Data platform.”
As an aside, Gualtieri made a key point about Hadoop and the cloud, pointing to what he calls “the myth of data gravity.”
Businesses often ask him where to store their data: in the cloud? on
premise? The conventional wisdom is that you should store your data
where you handle most of your analytics and processing. However,
Gualtieri disagrees – this attitude is too limiting, he says.
Here’s why data gravity is a myth: “It probably takes
only about 50 minutes to move a terabyte to the cloud, and a lot of
enterprises only have a hundred terabytes.” So if your Hadoop cluster
resides in the cloud, it would take mere hours to move your existing
data to the cloud, after which it’s just incremental updates. “I’m
hoping companies will understand this, so that some of them can actually
use the cloud, as well,” he says.
When Matchett looks to the future of Hadoop and Big
Data, he sees the affect of convergence: any number of vendors and
solutions combining together to handle an ever more flexible array of
challenges. “We’re just starting to see a little bit [of convergence]
where you have platforms, scale-up commodity platforms with data
processing, that have increasing capabilities,” he says. He points to
the combination of MapReduce and Spark. “We also have those SQL
databases that can run on these. And we see databases like Vertica
coming in to run on databases with the same platforms…Green Plum from
EMC, and some from Teradata.”
He adds: “If you think about that kind of push, the data
lake makes more sense not as a lake of data, but as a data processing
platform where I can do anything I want with the data I put there.”
The future of Hadoop and Big Data will contain a
multitude of technologies all mixed and matched together – including
today’s emerging container technology.
“You start to look at what’s happening, with workload
scheduling and container scheduling and container cluster management,
and there’s Big Data from this side coming in and you realize: well,
what MapReduce is, it’s really a Java job that gets mapped out. And what
a container really is, it’s a container that holds a Java application…
You start to say, we’re really going to see a new kind of data center
computing architecture take hold, and it started with Hadoop.”
How will it all evolve? As Matchett notes, “the story is still being written.”
What is Hadoop? – Simplified!
Scenario 1: Any global bank today has more than 100 Million customers doing billions of transactions every month
Scenario 2: Social
network websites or eCommerce websites track customer behaviour on the
website and then serve relevant information / product.
Traditional systems find it difficult to cope up with this scale at required pace in cost-efficient manner.
This is where Big data platforms come to
help. In this article, we introduce you to the mesmerizing world of
Hadoop. Hadoop comes handy when we deal with enormous data. It may not
make the process faster, but gives us the capability to use parallel
processing capability to handle big data. In short, Hadoop gives us
capability to deal with the complexities of high volume, velocity and variety of data (popularly known as 3Vs).
Please note that apart from Hadoop,
there are other big data platforms e.g. NoSQL (MongoDB being the most
popular), we will take a look at them at a later point.
Introduction to Hadoop
Hadoop is a complete eco-system of open
source projects that provide us the framework to deal with big data.
Let’s start by brainstorming the possible challenges of dealing with big
data (on traditional systems) and then look at the capability of Hadoop
solution.
Following are the challenges I can think of in dealing with big data :
1. High capital investment in procuring a server with high processing capacity.
2. Enormous time taken
3. In case of long query, imagine an error happens on the last step. You will waste so much time making these iterations.
4. Difficulty in program query building
Here is how Hadoop solves all of these issues :
LinuxWorld Informatics Pvt. Ltd Offer Bigdata hadoop Training
Tuesday, 22 March 2016
Wednesday, 9 March 2016
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Students who are Pursing or completed B-tech, M.C.A. M.Sc, B.C.A, B.Sc are welcome to apply
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Summer Training/Internships are very important as far as an engineer's career is involved. Summer Internship is common for one particular reason: big summer holidays. Students learn a lot and gain industry exposure through trainee positions. Engineer's take their opportunity to learn, develop and apply known skills. Apart from these, there are a bunch of other benefits as well.
Benefits for Students
Chances to gain jobs increases
Real exposure of the field
Lots of practical experience gained
Excellent place to interact with professionals
Discovering & applying new techniques
Develops professional skills
Develops a work ethic standard
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Employers prefer past-interns as employees
This is an exciting opportunity to directly work with the experienced leading Trainer. This is a wonderful opportunity for a student who is pursuing a degree or interested in Computer science Engineering. Make a difference this summer and lend a helping hand to the LinuxWorld Informatics pvt. ltd.
Summer Training/Internships are very important as far as an engineer's career is involved. Summer Internship is common for one particular reason: big summer holidays. Students learn a lot and gain industry exposure through trainee positions. Engineer's take their opportunity to learn, develop and apply known skills. Apart from these, there are a bunch of other benefits as well.
Benefits for Students
Chances to gain jobs increases
Real exposure of the field
Lots of practical experience gained
Excellent place to interact with professionals
Discovering & applying new techniques
Develops professional skills
Develops a work ethic standard
Boosts self-confidence
Employers prefer past-interns as employees
This is an exciting opportunity to directly work with the experienced leading Trainer. This is a wonderful opportunity for a student who is pursuing a degree or interested in Computer science Engineering. Make a difference this summer and lend a helping hand to the LinuxWorld Informatics pvt. ltd.
Saturday, 27 February 2016
Hadoop turns 10, Big Data industry rolls along
Apache Hadoop, the open source project that arguably sparked the Big
Data craze, turned 10 years old this week. The project's founder,
Cloudera's Doug Cutting, waxed nostalgic as vendors in the space churned
out new releases of their own.
It's hard to believe, but it's true. The Apache Hadoop project, the open source implementation of Google's File System (GFS) and MapReduce execution engine, turned 10 this week.
The technology, originally part of Apache Nutch, an even older open source project for Web crawling, was separated out into its own project in 2006, when a team at Yahoo was dispatched to accelerate its development.
Proud dad weighs inDoug Cutting, founder of both projects (as well as Apache Lucene), formerly of Yahoo, and presently Chief Architect at Cloudera, wrote a blog post commemorating the birthday of the project, named after his son's stuffed elephant toy.
In his post, Cutting correctly points out that "Traditional enterprise RDBMS software now has competition: open source, big data software." The database industry had been in real stasis for well over a decade. Hadoop and NoSQL changed that, and got the incumbent vendors off their duffs and back in the business of refreshing their products with major new features
Sleeping giants awaken
Microsoft SQL Server now supports columnstore indexes in order to handle analytic queries on large volumes of data and its upcoming 2016 version adds PolyBase functionality for integrated query of data in Hadoop. Meanwhile, Oracle and IBM have added their own Hadoop bridges, along with better handling of semi-structured data.
Teradata has pivoted rather sharply towards Hadoop and Big Data, starting with its acquisition of Aster Data and continuing through its multifaceted partnerships with Cloudera and Hortonworks. Meanwhile, in the Hadoop Era, perhaps in deference to Teradata, virtually every megavendor acquired one of the data warehousing pure plays.
New generationCutting points out, also accurately, that the original core components of Hadoop have been challenged and/or replaced: "New execution engines like Apache Spark and new storage systems like Apache Kudu (incubating) demonstrate that this software ecosystem evolves rapidly, with no central point of control." Granted, both of these projects are heavily championed by Cloudera, so take the commentary with a grain of salt.
Salt or no salt though, Cutting's comment that the Hadoop ecosystem has "no central point of control" is one worth considering carefully; because, while it is correct, it's not necessarily good. The term "creative destruction" sometimes truly is an oxymoron. The Big Data scene's rapid technology replacement cycles leave the space stability-challenged.
Give peace a chancePerhaps, but the moving technology target may also mean they get no software at all, because the current environment is sufficiently risk-prone as to hinder the growth of enterprise projects. We need some equilibrium if we want growth to be proportionate to the level of technological innovation.
Cutting concludes his post by declaring: "I look forward to following Hadoop's continued impact as the data century unfolds." While I'm not sure data and analytics will define the whole century, they probably have a good decade or two. Hopefully the industry can get a little better at developing standards that are cooperative and compatible, rather than overlapping and competitive. We don't want to go back to stasis, but more navigable terrain would suit the industry and its customers
Meanwhile, back in the competitive marketSpeaking of the industry, there were a slew of announcements this week, beside (and even despite) Hadoop's birthday.:
That's a pretty busy week. And I dare say, without Hadoop as a catalyst, it would have been much less so. As climate change, financial markets, geopolitics and the price of oil reach frightening new levels of volatility, the data sector of the technology industry is thriving. We might hope that the technology around Big Data could be deployed to help solve, or at least better understand, some of our world's truly big problems.
This won't be the century of data unless that in fact happens
Article Source - http://www.zdnet.com/article/hadoop-turns-10-big-data-industry-rolls-along/
It's hard to believe, but it's true. The Apache Hadoop project, the open source implementation of Google's File System (GFS) and MapReduce execution engine, turned 10 this week.
The technology, originally part of Apache Nutch, an even older open source project for Web crawling, was separated out into its own project in 2006, when a team at Yahoo was dispatched to accelerate its development.
Proud dad weighs inDoug Cutting, founder of both projects (as well as Apache Lucene), formerly of Yahoo, and presently Chief Architect at Cloudera, wrote a blog post commemorating the birthday of the project, named after his son's stuffed elephant toy.
In his post, Cutting correctly points out that "Traditional enterprise RDBMS software now has competition: open source, big data software." The database industry had been in real stasis for well over a decade. Hadoop and NoSQL changed that, and got the incumbent vendors off their duffs and back in the business of refreshing their products with major new features
Sleeping giants awaken
Microsoft SQL Server now supports columnstore indexes in order to handle analytic queries on large volumes of data and its upcoming 2016 version adds PolyBase functionality for integrated query of data in Hadoop. Meanwhile, Oracle and IBM have added their own Hadoop bridges, along with better handling of semi-structured data.
Teradata has pivoted rather sharply towards Hadoop and Big Data, starting with its acquisition of Aster Data and continuing through its multifaceted partnerships with Cloudera and Hortonworks. Meanwhile, in the Hadoop Era, perhaps in deference to Teradata, virtually every megavendor acquired one of the data warehousing pure plays.
New generationCutting points out, also accurately, that the original core components of Hadoop have been challenged and/or replaced: "New execution engines like Apache Spark and new storage systems like Apache Kudu (incubating) demonstrate that this software ecosystem evolves rapidly, with no central point of control." Granted, both of these projects are heavily championed by Cloudera, so take the commentary with a grain of salt.
Salt or no salt though, Cutting's comment that the Hadoop ecosystem has "no central point of control" is one worth considering carefully; because, while it is correct, it's not necessarily good. The term "creative destruction" sometimes truly is an oxymoron. The Big Data scene's rapid technology replacement cycles leave the space stability-challenged.
Give peace a chancePerhaps, but the moving technology target may also mean they get no software at all, because the current environment is sufficiently risk-prone as to hinder the growth of enterprise projects. We need some equilibrium if we want growth to be proportionate to the level of technological innovation.
Cutting concludes his post by declaring: "I look forward to following Hadoop's continued impact as the data century unfolds." While I'm not sure data and analytics will define the whole century, they probably have a good decade or two. Hopefully the industry can get a little better at developing standards that are cooperative and compatible, rather than overlapping and competitive. We don't want to go back to stasis, but more navigable terrain would suit the industry and its customers
Meanwhile, back in the competitive marketSpeaking of the industry, there were a slew of announcements this week, beside (and even despite) Hadoop's birthday.:
- Pentaho introduced Python language integration into its Data Integration Suite
- Paxata launched its new Winter '15 release (albeit in 2016), which includes new auto number and fill down transformations, new algorithms to aid its data prep recommendations, and integration with LDAP and SAML, for enterprise security, single sign-on and identity management
- SkyTree, a predictive analytics vendor, discussed that it will soon launch a free single-user version of its product, which it will soon announce more formally (and RapidMiner, also in the predictive space, released its new version 7 last week, with a revamped UI)
- NoSQL vendor Aerospike launched a new release of its eponymous database, which now features geospatial data support, added resiliency in cloud-hosted environments and server-side support for list and map data structures
That's a pretty busy week. And I dare say, without Hadoop as a catalyst, it would have been much less so. As climate change, financial markets, geopolitics and the price of oil reach frightening new levels of volatility, the data sector of the technology industry is thriving. We might hope that the technology around Big Data could be deployed to help solve, or at least better understand, some of our world's truly big problems.
This won't be the century of data unless that in fact happens
Article Source - http://www.zdnet.com/article/hadoop-turns-10-big-data-industry-rolls-along/
Wednesday, 25 November 2015
Hive Basic Understanding
Hive is Petabyte scale dataware house system on Hadoop.
Hadoop based system for querying & managing structured data.
Its used to Query Big Data in SQL fashion.
For Execution Hive uses - Map/Reduce
For Storage Hive uses - HDFS
For Metadata - RDBMS
Origin of Hive -
Hive was designed by Facebook for querying from petabytes of data. There was sudden data explosion at Facebook which was impossible to store in traditional DBMS & query.
Hive made users job extremly esay to query data stored on HDFS.
Hive now became parallel DBMS which uses Hadoop for its storage & execution architecture.
Why Hive -
Hive is another dataware house system designed because existing Dataware house systems do not meet all the requirement in scalable , agile & cost effeciant way.
Programming model used in Hadoop is - MapReduce. Its very difficult to write Map-Reduce program for every small or big reports. Also it's requires highly skilled resources to write such a complex code.
Using Hive one can simply issue the query as simple & similar we do in SQL. But here Hive generates Map Reduce code for user based on Query issued.
Advantages of Hive -
Hive can work with very large data (100's to Terabytes).
Hive can work on large hadoop cluster (100's of Nodes).
Data stored on Hive has defined Schema.
Hive is used for Batch jobs also (Load & Query).
Where not to use Hive -
If you need responses in seconds.
If you don't want to impose a schema.
If traditional DBMS already can do the job.
If your data is measured in GB's or even less.
If you don't have enough time & highly skilled resources.
Hive Entities -
Database, Table, Partitions, Bucketing Columns.
MORE....
Hive Data Types -
Primitive Data Types
TINYINT 1 Byte Signed Integer
SMALLINT 2 Byte Signed Integer
INT 4 Byte Signed Integer
BIGINT 8 Byte Signed Integer
BOOLEAN True or False (Boolean)
FLOAT Single precision floating bytes
DOUBLE Double precision floating point
STRING Sequence of charaters (within Sigle or double quotes)
TIMESTAMP java.sql.Timestamp format
etc...
Collection Data Types
STRUCT Similar to Structure in C.
MAP (Key,Value) pair
ARRAY Ordered sequence of similar data types.
Hive operations -
DDL operations
[CREATE/ALTER/DROP] [TABLE/VIEW/PARTITION]
CREATE TABLE AS SELECT
DML operations
INSERT OVERWRITE
Queries...
Sub-Queries within "FROM" clause.
Joins [Inner join & Outer (Left, Right & Full outer join)]
Multi-Table insert
Sampling
Interfaces
JDBC/ODBC/THRIFT
Hadoop based system for querying & managing structured data.
Its used to Query Big Data in SQL fashion.
For Execution Hive uses - Map/Reduce
For Storage Hive uses - HDFS
For Metadata - RDBMS
Origin of Hive -
Hive was designed by Facebook for querying from petabytes of data. There was sudden data explosion at Facebook which was impossible to store in traditional DBMS & query.
Hive made users job extremly esay to query data stored on HDFS.
Hive now became parallel DBMS which uses Hadoop for its storage & execution architecture.
Why Hive -
Hive is another dataware house system designed because existing Dataware house systems do not meet all the requirement in scalable , agile & cost effeciant way.
Programming model used in Hadoop is - MapReduce. Its very difficult to write Map-Reduce program for every small or big reports. Also it's requires highly skilled resources to write such a complex code.
Using Hive one can simply issue the query as simple & similar we do in SQL. But here Hive generates Map Reduce code for user based on Query issued.
Advantages of Hive -
Hive can work with very large data (100's to Terabytes).
Hive can work on large hadoop cluster (100's of Nodes).
Data stored on Hive has defined Schema.
Hive is used for Batch jobs also (Load & Query).
Where not to use Hive -
If you need responses in seconds.
If you don't want to impose a schema.
If traditional DBMS already can do the job.
If your data is measured in GB's or even less.
If you don't have enough time & highly skilled resources.
Hive Entities -
Database, Table, Partitions, Bucketing Columns.
MORE....
Hive Data Types -
Primitive Data Types
TINYINT 1 Byte Signed Integer
SMALLINT 2 Byte Signed Integer
INT 4 Byte Signed Integer
BIGINT 8 Byte Signed Integer
BOOLEAN True or False (Boolean)
FLOAT Single precision floating bytes
DOUBLE Double precision floating point
STRING Sequence of charaters (within Sigle or double quotes)
TIMESTAMP java.sql.Timestamp format
etc...
Collection Data Types
STRUCT Similar to Structure in C.
MAP (Key,Value) pair
ARRAY Ordered sequence of similar data types.
Hive operations -
DDL operations
[CREATE/ALTER/DROP] [TABLE/VIEW/PARTITION]
CREATE TABLE AS SELECT
DML operations
INSERT OVERWRITE
Queries...
Sub-Queries within "FROM" clause.
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Wednesday, 21 October 2015
6 Reasons Why Java Developers Should Learn Hadoop
Imagine there are two girls standing in front of you - The first girl
is cute, beautiful, interesting and has the smile that any guy would
die for. And the other girl is average-looking, quiet,
not-so-impressive... no different from the ones that you usually see in
the restaurant cash counter. Which girl will you call out for a date? If
you're like me, you will choose the attractive girl. You see, life is
full of options and making the right choice is what matters the most.
If you're a Java developer, then you probably have more choices to make - like the switch from Java to Hadoop.
Big
data and Hadoop are the two most popular buzzwords in the industry.
Chances are that you have come across these two terms on the Java
payscale forums or seen your senior colleagues making the switch to get
bigger paychecks. I'll tell you what, the upgrade from Java to Hadoop is
not just about staying updated with the latest technology or getting
appraisals - it's about being competent and putting your career on the
fifth gear.
The good news for all the aspiring Hadoop developers
is that, the Big Data industry has already crossed the $50 billion
dollar mark and over 64% of the top 720 companies worldwide are
interesting to invest in this forward-thinking technology as revealed by
Gartner in 2013.
If that's not convincing, then take a look at these stats:
1. According to an IDC report, the Big Data industry is growing at the rate of 31.7% per year.
2. Java developers are seen as the best replacement option for Hadoop developers, says Forrester.
3. Hadoop developers enjoy a mighty 250% pay hike than Java developers, as stated in an Analytics Industry Report.
2. Java developers are seen as the best replacement option for Hadoop developers, says Forrester.
3. Hadoop developers enjoy a mighty 250% pay hike than Java developers, as stated in an Analytics Industry Report.
What's special about Hadoop?
Unlike
the traditional databases which weren't capable of dealing with large
volumes of data, Hadoop offers the quickest, cheapest, and smartest way
to store and process giant volumes of data - and that's the reason why
it is so popular among big corporations, government organizations,
hospitals, universities, financial services, marketing agencies,
etc. The best way to familiarize with the language is to check out a
beginner's big data hadoop course.
Okay, now let's some reasons why Java developers should switch to Hadoop.
1. Easy To Learn For Java Developers
A
tennis player like Rafael Nadal loves clay courts because the surface
suits him well and that's where he has been most successful. Similarly,
any Java developer would love Hadoop because it's completely written in
Java - a language that you are already so familiar with. Switching from
Java to Hadoop is a cake-walk for professionals like you because the
MapReduce script used in the Hadoop is actually written in Java itself.
Awesome, isn't it?
Your Java skills will come in handy when debugging Hadoop apps and employing Pig (programming tool) Latin commands.
2. Helps You To Stay Ahead Of Your Competition
If
you are a Java professional, you are just seen as a person in the
crowd. But, if you are a Hadoop developer, you are seen as potential
leader in the crowd. Big Data and Hadoop jobs are a hot deal in the
market and Java professionals with the required skill set are easily
picked by big companies for high salary packages. All you have to do is
attend a big data hadoop training program and learn the concepts
from an expert.
3. Scope To Move Into Bigger Domains
Fortunately
for you, the road doesn't end with Hadoop and MapReduce. There is
always the golden opportunity to use your Hadoop skills and expertise to
move into higher levels such as Artificial Intelligence, Data Science,
Sensor Web data, and Machine Learning. These are emerging markets, and
you'll see them dominate the industry in the next 4-5 years. Good
knowledge in Big Data and Hadoop could boost your chances of getting
into some of the bigger Big Data-dependent companies such as Amazon,
Yahoo, Facebook, Twitter, IBM, and eBay.
4. Lucrative Packages For Hadoop Professionals
By
switching from Java to Hadoop, you can expect a higher salary and
better career prospects - the kind of salary and designation that your
wife would like to rave about. According to Indeed, the average salary
for a Big Data Hadoop developer with 1-2 years of experience is around
$140,000 per annum in the United States. However, as you gain experience
and become a senior Hadoop developer, you will be able to make a good
$400,000+ salary.
5. An Improved Quality Of Work
Learning
Big Data Hadoop can be highly beneficial because it will help you to
deal with bigger, complex projects much easier and deliver better output
than your colleagues. In order to be considered for appraisals, you
need to be someone who can make a difference in the team, and that's
what Hadoop lets you to be.
6. Grow With The Industry
With
IDC predicting that the Big Data and Hadoop user base (big companies
and government organizations) is likely to increase at 27% per year, you
have a great opportunity to upgrade your knowledge and skills and grow
with the industry.
Big Data and Hadoop are widely used in
applications such as IT log analytics, Fraud detection, Social media
analysis, and Call centre analytics - and learning a big data hadoop tutorial
could be the way to kick-start your Hadoop career right away. Once you
do that, you will find that staying updated with the latest technology
will be a lot easier and getting into top organizations will never be
'just a dream' - it will be a reality.
That's about it, folks!
These are some rock-solid reasons why learning Hadoop is important and
how it can help take your career to the next level
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