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

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.:

• 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

Weekend pondering
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/

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

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.

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

Article Source: http://EzineArticles.com/9189164

Data Lake Showdown: Object Store or HDFS?

The explosion of data is causing people to rethink their long-term storage strategies. Most agree that distributed systems, one way or another, will be involved. But when it comes down to picking the distributed system–be it a file-based system like HDFS or an object-based file store such as Amazon S3–the agreement ends and the debate begins.

The Hadoop Distributed File System (HDFS) has emerged as a top contender for building a data lake. The scalability, reliability, and cost-effectiveness of Hadoop make it a good place to land data before you know exactly what value it holds. Combine that with the ecosystem growing around Hadoop and the rich tapestry of analytic tools that are available, and it’s not hard to see why many organizations are looking at Hadoop as a long-term answer for their big data storage and processing needs.

At the other end of the spectrum are today’s modern object storage systems, which can also scale out on commodity hardware and deliver storage costs measured in the cents-per-gigabyte range. Many large Web-scale companies, including Amazon, Google, and Facebook, use object stores to give them certain advantages when it comes to efficiently storing petabytes of unstructured data measuring in the trillions of objects.

But where do you use HDFS and where do you use object stores? In what situations will one approach be better than the other? We’ll try to break this down for you a little and show the benefits touted by both.

Bigdata Hadoop Training in Jaipur

Why You Should Use Object-Based Storage

According to the folks at Storiant, a provider of object-based storage software, object stores are gaining ground among large companies in highly regulated industries that need greater assurances that no data will be lost.

“They’re looking at Hadoop to analyze the data, but they’re not looking at it as a way to store it long term,” says John Hogan, Storiant’s vice president of engineering and product management. “Hadoop is designed to pour through a large data set that you’ve spread out across a lot of compute. But it doesn’t have the reliability, compliance, and power attributes that make it appropriate to store it in the data lake for the long term.”

Object-based storage systems such as Storiant’s offer superior long-term data storage reliability compared to Hadoop for several reasons, Hogan says. For starters, they use a type of algorithm called erasure encoding that spreads the data out across any number of commodity disks. Object stores like Storiant’s also build spare drives into their architectures to handle unexpected drive failures, and rely on the erasure encoding to automatically rebuild the data volumes upon failure.

If you use Hadoop’s default setting, everything is stored three times, which delivers five 9s of reliability, which used to be the gold standard for enterprise computing. Hortonworks architect Arun Murthy, who helped develop Hadoop while at Yahoo, pointed out at the recent Hadoop Summit that if you only storing everything twice in HDFS, that it takes one 9 off the reliability, giving you four 9s. That certainly sounds good. Source

Step by Step learning guide for Hadoop

Just as I was frustrated and disappointed with the training I attended with Bigdata training academny in chennai, I decided to publish best sites and reference materials for Hadoop that I come across.

Atleast this way I can be of some help for the "to be"  Hadoop aspirants and professionals so that won't waste their money in cheap institutes like the Bigdata training 

I aim my blog to be one stop shop for learning Bigdata Apache Hadoop, PIG and Hbase ,,,

Also as and when time permits , I will also create tuorials for hadoop, pig and hbase and publish them.

First at the beginner level and then to advaned level

The best site is cloudera.com for all the beginners of Apache Bigdata Hadoop and its ecosystem . Go and visit this URL http://university.cloudera.com/onlineresources.html

Big Data-Hadoop and Its Impact on Business Intelligence Systems

Recently my work necessitated me look into the new features added in informatica 9.1, but I never thought the journey will take me to explore further on this and write a blog Let's see how I traversed through different new aspects that are getting very much related to data management and Business Intelligence. First we will look what is Bigdata and its position now.

People would always think how the organizations like Yahoo, Google, Facebook store large amounts of data of the users. We should take a note that Facebook stores more photos than Google's Picassa. Any guesses??

What is Hadoop

The answer is BigData Hadoop and it is a way to store large amounts of data in petabytes and zettabytes. This storage system is called as Hadoop Distributed File System. Hadoop was developed by Doug Cutting based on ideas suggested by Google's papers. Mostly we get large amounts of machine generated data. For example, the Large Hadron Collider to study the origins of universe produces 15 petabytes of data every year for each experiment carried out.

MapReduce

The next thing which comes to our mind is how quick we can access these large amounts of data. Hadoop uses MapReduce, which first appeared in research papers of Google. It follows 'Divide and Conquer'. The data is organized as key value pairs. It processes the entire data that is spread across countless number of systems in parallel chunks from a single node. Then it will sort and process the collected data.

With a standard PC server, Hadoop will connect to all the servers and distributes the data files across these nodes. It used all these nodes as one large file system to store and process the data, making it a 100% unadulterated distributed file system. Extra nodes can be added if data reaches the maximum installed capacity, making the setup highly scalable. It is very cheap as it is open source and doesn't require special processors like used in traditional servers. Hadoop is also one of the NoSQL implementations.

Hadoop in Real time

The Tennessee Valley Authority(TVA) uses smart-grid field devices to collect data on its power-transmission lines and facilities across the country. These sensors send in data at a rate of 30 times per second - at that rate, the TVA estimates it will have half a petabyte of data archived within a few years. TVA uses Hadoop to store and analyze data. In India Power Grid Corporation of India intends to install these smart devices in their grids for collecting data to reduce transmission losses. It is better they also emulate TVA. Recently Facebook moved to 30 Petabyte Hadoop, which sounds incredible and hard to digest the fact we are using such a myriad volume of data.

Data Warehouse and Business Intelligence [http://hexaware.com/business-intelligence-analytics.htm] Products supporting Hadoop and MapReduce

1) Greenplum
2) Informatica
3) Teradata
5) Pentaho
6) Talend

If Hadoop and other NoSQL implementations are widely used, the limitations of traditional SQL systems can be resolved like storing unstructured data. With the volume of data increasing exponentially, commercialization of Hadoop will happen in a large scale and data integrator tools will play a key role in mining data for business.

Readers share your experiences if any of you have worked with Hadoop on other ETL and BI Tools, tools that are available in the market.

Article Source - http://goarticles.com/article/Big-Data-Hadoop-and-Its-Impact-on-Business-Intelligence-Systems/6695051/

Overview of Hadoop Applications

Hadoop is nothing but a source of software framework that is generally used in the processing immense and bulk data simultaneously across many servers. In the recent years, it has turned out to be one of most viable option for enterprises, which has the never-ending requirement to save and manage all the data. Web based businesses such as Facebook, Amazon, eBay, and Yahoo have used high-end Hadoop applications to manage their large data sets. It is believed that Hadoop Training is still relevant to both small organizations as well as big time businesses.

Hadoop is able to process a huge chunk of data in a lesser time which enabled the companies to analyze that this was not possible before within that stipulated time. Another important advantage of the Hadoop applications is the cost effectiveness, which cannot be availed in any other technologies. One can avoid the high cost involved in the software licenses and the fees that has to be upgraded periodically when using anything apart from Hadoop. It is highly recommended for businesses, which have to work with huge amount of data, to go for Hadoop applications as it helps in fixing any issues.

Actually, Hadoop applications are made up of two parts; one is the HDFS, which means the Hadoop Distributed File System while the other is the Hadoop map reduce that helps in the processing of data and scheduling of job depending upon the priority, which is a technique that initially originated in Google search engine. Along with these two primary components, there are nine other parts, which are decided as per the distribution one uses along with other complementary tools. There are three most common functions of Hadoop applications. The first function is the storage and analysis of all the data, which does not require the loading of the relational database management system. Secondly, it is used in the conversion of huge repository of semi-structured and unstructured data, for example a log file in the form of a structured data. Such complicated data are hard to understand in SQL tools like analyzing the graph and data mining.

Hadoop applications are mostly used in the web-related businesses wherein one has to work with big log files and data from the social network sites. When it comes to media or the advertising world, enterprises use Hadoop, which enables the best performance of ad offer analysis and help understand online reviews. Before using any Hadoop tool, it is advisable to read through the Hadoop map tutorials available online.

Article Source: http://EzineArticles.com/6531213

PMO using Big Data techniques on mygov.in to translate popular mood into government action

NEW DELHI: The Prime Minister's Office is using Big Data techniques to process ideas thrown up by citizens on its crowd sourcing platform mygov. in, place them in context of the popular mood as reflected in trends on social media, and generate actionable reports for ministries and departments to consider and implement.

The Modi government has roped in global consulting firm PwC to assist in the data mining exercise, and now wants to elevate Mygov.in platform from a one-way flow of citizens' ideas to a dialogue where the government keeps them abreast of some of the actions that emerge from their brainstorming.

"There is a large professional data analytics team working behind the scenes to process and filter key points emerging from debates on mygov.in, gauge popular mood about particular issues from social media sites like Twitter and Facebook," said a senior official aware of the development, adding that these are collated into special reports about possible action points that are shared with the PMO and line ministries. Ministries are being asked to revert with an action taken report on these ideas and policy suggestions currently being generated on 19 different policy challenges such as expenditure reforms, job creation, energy conservation, skill development and government initiatives such as Clean India, Digital India and Clean Ganga.

With the PM inviting Indian communities in America and Australia to join the online platform, which he has termed a 'mass movement towards Surajya', the traffic handling capacity of mygov.in is being scaled up consistently, the official said.

PwC executive director Neel Ratan said that the firm is 'helping the government' process the citizen inputs coming through on Mygov. in in.

"There is a science and art behind it. We have people constantly looking at all ideas coming up, filtering them and after a lot of analysis, correlating it to sentiments coming through on the rest of social media," he said, stressing this is throwing up interesting trends and action points, being relayed to ministries. "It's turning out to be fairly action-oriented. I think it is distinctly possible that 30-50 million people would be actively contributing to Mygov.in over the next year and a half, given its current pace of growth," Ratan said.

PwC's global leader in government and public services Jan Sturesson told ET the participative governance model being adopted through mygov.in could become a model for the developed world.

"The biggest issue for governments today is how to be relevant. If all citizens are treated with dignity and invited to collaborate, it can be easier for administrations to have a direct finger on the pulse of the nation rather than lose it in transmission through multiple layers of bureaucracy," he said, not ruling out the possibility of using the mygov.in for quick referendums on contemporary policy dilemmas in a couple of years.
"The problem in the West has been that the US, Australia and UK follow a public management philosophy that treats citizens as consumers. That's ridiculous, because a consumer pays the bill and complains, while a citizen engages differently and takes responsibility," said Sturesson.

Within the 19 broad citizen engagement themes on mygov.in, there are multiple discussion groups focused on specific subsectors and themes. When it was launched in July, the site enabled brainstorming among its registered users around seven policy challenges. Users are allowed to sign up for four discussion groups in areas of interest apart from a group dealing with issues in their immediate vicinity.

Article Source - http://articles.economictimes.indiatimes.com/2014-11-26/news/56490626_1_mygov-digital-india-modi-government

Learning Hadoop with Linux World India

The best Big Data Hadoop Training in Jaipur is offered by LinuxWorld India. When it comes to technological training, we are the best in the business. The organization was established in 2005. Our training comprises of all the expert professionals and teachers who impart tremendous knowledge. When it comes to Cisco certifications, we are the first preference. We bring together a blend of network training and solutions with the help of our authorized training partner. We believe in providing the knowledge to its betterment. With the best study in Hadoop, you can build your super computer. We provide the best classroom and training of Version 2 Hadoop. We are the first ones to bring this facility to India.

The training fees required for this course is Rs. 25500 and the course module will be delivered to you by us. After completing this course with us you will be able a master in Hadoop and will produce a framework of MapReduce. You will also be an expert in writing complex programs related to MapReduce. Hadoop is a tool that was built on Java, and it focuses on improving the performance of hardware. By studying this, you can also create a cluster of data that will help you to program different models.

Big Data Training in Jaipur

Intelligence is a leading an advanced Hadoop Training in Jaipur. They are especially known for the Data Warehousing and Hadoop in Jaipur. It includes the core competencies such as ABAP, DW, DI, HANA, SAP BO, AIX, Solaris, Red Hat Certification, Linux, PLSQL, Oracle SQL, Business Objects, Data stage, Informatics, Congos, Software Testing, ISTQB certification, Amazon Web Services, Mobile application of the developer using the IOS and Android. They also provide you with the best online training for all of the above technologies. Their main objective is to provide production based knowledge for every training module. They also have experienced trainers and well-equipped lab for every technology. 

The Hadoop administrators are one of the finest and famous among the world that are more in demand and are highly compensated in the technical role. Many of them are nowadays going for the CCAH qualification. They get to learn all the Hadoop topics such as first, to determine the infrastructure for your cluster and to correct the hardware. Second, learn the internals of the HDFS, Map Reduce and YARN. Third, learn the deployment to integrate with the data center and proper cluster configuration. Fourth, learn how to load data into the cluster from the RDBMS with the help of Scoop and from the dynamically generated files with the help of the Flume. 

Fifth, to configure the fair scheduler in order to provide service level agreements for the multiple users of the cluster. Sixth, solving the Hadoop issues, tuning, diagnosing and troubleshooting problems. Seventh, best practices for maintaining and preparing the production in the Apache Hadoop. This training helps you with good jobs, you get the complete technology specified, and they conduct campus drives. This course is best for the IT managers and the system administrators who have the basic knowledge about the Linux experience. Despite the Apache Hadoop, knowledge is not required.

To More about the Big Data Hadoop Training in Jaipur ,Please Click This

Remember FLURPS to design better big data analytic solutions

FLURPS is an acronym for six components of well-rounded big data designs: Functionality, Localizability, Usability, Reliability, Performance, Supportability. Here's the case for using this template.

I've been advocating for customer-centric design as long as I've been designing solutions for customers. I still do this, because I have to. It's remarkable to me that after decades of building high-tech solutions for customers, technologists still seem to build solutions in an IT vacuum and then get upset when customers don't find them very functional.

Gold plating is a term used to describe developers who infer customer requirements and subsequently build features that the end users never requested -- because the developers know better. Unfortunately, data scientists are carrying this tradition forward with analytic solutions. That said, I wouldn't categorically dismiss any requirement that doesn't come from a customer or end user. It may seem odd coming from such a strong advocate of customer-centric design, but there are aspects of a well-built solution that customers don't know or appreciate.

When designing a big data analytic solution, make sure it includes a well-rounded set of requirements, including ones that the end user won't directly know about or care about.

What's FLURPS?

FLURPS is a great acronym that I learned as a young computer Big Data Training in Jaipur, and it works great as a template for building well-rounded analytic solutions. FLURPS stands for: Functionality, Localizability, Usability, Reliability, Performance, Supportability. It seems like a lost acronym that I'd like to resurrect to help us design and build better solutions.

This funny-sounding acronym reminds me of a big, hairy puppet like Mr. Snuffleupagus -- that's why it has stuck in my mind for so many years. Let's go through the different elements of FLURPS and how they can enhance your design.

Functionality

Functionality remains the key component of design; it represents all the features the customer knows and wants. When building a requirements document, most analysts separate functional from non-functional requirements, which is a good practice. Furthermore, functional requirements should always take precedence over non-functional requirements. Never sacrifice functional requirements for non-functional requirements -- you should satisfy non-functional requirements in addition to functional requirements.

Localizability

Localizability handles geographical concerns such as language. Internationalization (or i18n, for those in the know) is closely related to localizability in that it architecturally provides the technical infrastructure to localize a solution. Knowing that your recommendation engine will be used globally, you may internationalize it by automatically sensing where your user is located, and then localize it by providing, for example, German-, Russian-, or Chinese-specific content. Bear in mind that good localizability extends beyond language translation and caters to cultural differences in functionality.

Usability

Usability deals with the customer experience. This is a pet peeve of mine -- I'm tired of seeing analytic solutions that force the user into the mind of the developer. For instance, it's very common to see use cases where a batch operation seems obvious, but the solution only allows single transaction processing. If I could possibly have hundreds of input variables to my predictive analytics engine, why should I have to create them one by one?

Although usability seems like it should fall into the functional category, it does not. Most customers don't know how to design a usable solution; however, they know when it's not usable. Putting a user experience expert on your team is a fantastic idea.

Reliability

Reliability handles the stability of your application. Reliability is not something end users contemplate because they assume your solution will be stable; when it's not, frustration can quickly escalate to extreme dissatisfaction.

You must build reliable solutions. How many times have you lost work because your system crashed? And by the way, a cute little icon telling you that the system crashed doesn't help. Build requirements into your solution to recover from exceptional situations and gracefully exit only when all possible routes of recovery are lost. I once designed a web application that went through four or five levels of exception before it finally, gracefully quit -- after saving all of the user's work. The users never knew the application was going into its third and fourth level of exception, and that's the way it should be.

Performance

Performance is a bigger deal than you might think. I recently trained a group of users on a new web-based system that would on occasion take several minutes for a submenu to appear -- the industry standard is between two and three seconds. The performance of the system broadcasted the quality of the rest of the system, and it wasn't positive.

I know performance issues can be difficult to track down, but that's your problem, not the users. Make sure reasonable response times are documented in your requirements and thoroughly tested when the system is built.

Supportability

Supportability is the last, but not the least important, component of a robust design. Whether you're designing a product that will be used by customers or an internal system that will be used by employees, it's vitally important that the operations group is in a good position to support the solution.

For analytic solutions, supportability often extends beyond requirements into organizational design. The instrumentation on an analytic solution is often sophisticated, so it's important to staff the operations function with very knowledgeable technicians -- maybe even other data scientists. When I'm putting together a development team, I often include at least one person from the support team; this way, they can influence the solution's design from the perspective of someone who's going to support it.

Article Source - http://www.techrepublic.com/article/remember-flurps-to-design-better-big-data-analytic-solutions/

Normalizing Corporate Small Data With Hadoop and Data Science

In part one of this series (Hadoop for Small Data), we introduced the idea that Small Data is the mission-critical data management challenge. To reiterate, Small Data is “corporate structured data that is the fuel of its main activities, and whose problems with accuracy and trustworthiness are past the stage of being alleged. This includes financial, customer, company, inventory, medical, risk, supply chain, and other primary data used for decision making, applications, reports, and Business Intelligence.”

We are excluding stochastic data use cases which can succeed even if there is error in the source data and uncertainty in the results, because the business objective there is more focused on getting trends or making general associations. Most Big Data examples are this type. In stark contrast are deterministic use cases, where the ramifications for wrong results are severely negative. This is the realm of executive decision making, accounting, risk management, regulatory compliance, security, to name a few.

We chose this so-called Small Data use case for our inaugural Tech Lab series for several reasons. First, such data is obviously critical to the business, and should therefore be germane to any serious discussion of a information-driven enterprise. Second, the multivariate nature of the data presents a serious challenge in and of itself. Third, the rules and other business logic that give the data meaning tend to be opaque, sometimes embedded deep in operational systems; which means that effecting transparency into this layer can yield tremendous opportunity for the business to fine-tune its operations and grow smoothly.

The Tech Lab was designed to bring the rigors of scientific process to the world of data management, a la Data Science. Our mission is to demonstrate the gritty, brass tacks processes by which organizations can identify opportunities with data big and small, then build real-world solutions to deliver value. Each project features a Data Scientist (yours truly), who takes a set of enterprise software tools into the lab, then tackles some real-world data to build the solution. The entire process is documented via a series of blogs and several Webcasts, which detail the significant issues and hurdles encountered, and insights about how they were addressed or overcome.

All too often in the world of enterprise data, serious problems are ignored, or worse, assumed to be unsolvable. This leads to a cycle of spending money, time, and organizational capital. Not only can this challenge be solved, but doing so will vastly improve your personal and organizational success by having accurate, meaningful data that is understood, managed, and common.

Now, all of this probably sounds exactly like the marketing for the various conference fads of the past decade. We do not need to name them since we all recall the multiple expensive tools and bygone years which in the end did not yield much improvement. So, how can we inject success into this world?

The answer is to adopt what has been working for a very long time and is now a hot topic in data management, namely, Data Science. This new and exciting field (to data management not to science in general) comes with a tremendous amount of thoroughly tried and tested methods, and is linked to a strong community with deep knowledge and an ingrained willingness to help. This is the “science” part of Data Science. Data Science uses the fundamental precepts of how science deals with data: maintain detailed auditable and visible information of important activities, assumptions, and logic; embrace uncertainty since there can never be a perfect result; welcome questions of how and why the data values are obtained, used, and managed; understand the differences between raw and normalized data.

It is the latter tenet that we will concentrate on for this discussion and for the next Tech Lab with Cloudera. In science, normalizing data is done every day as a necessary and critical activity of work, whether experimental (as I used to do in Nanotechnology) or computer modeling. Normalizing data is more sophisticated than what is commonly done in integration (i.e. ETL). It combines subject matter knowledge, governance, business rules, and raw data. In contrast, ETL moves different data parts from their sources to a new location with some simple logic infused along the way. ETL has failed to solve even medium level problems with discordant, conflicting, real-world corporate data, albeit not for want of money and time. Indeed, the types of challenges in corporate Small Data are solved with an order of magnitude less expense, time, and organizational friction and with much higher complexity in many scientific and engineering fields.

One real-world example is a well governed part number used across major supply chain and accounting applications. Despite policy stating the specific syntax of the numbers, in actual data systems there are a variety of forms with some having suffixes, some having prefixes, and some having values taken out of circulation. Standard approaches like architecture and ETL cannot solve this (although several years have typically been spent trying) because the knowledge of why, who, when, and what is often not available. In the meantime, costs are driven up to support this situation, management is stifled in modernizing applications and infrastructure, legacy applications cannot be retired, and the lack of common data prevents meaningful Business Intelligence. Note that this lack of corporate knowledge also means that other top-down approaches like data virtualization and semantic mediation are doomed because they rely on mapping all source data values (not the models or metadata and this is a critical distinction to understand) to a common model.

This is much more typical of the state of corporate Small Data than simple variations in spelling or code lookups. This was for one element among many. Consider this for your company – whether related to clinical health, accounting, financial, and other core corporate data sets – and you can see the enormity of the challenge. This also explains why the techniques of the last years have not worked. If you do not have the complete picture then your architecture does not reflect your actual operations. Similarly, ETL tools work primarily on tables and use low level “transforms” like LTRIM. When the required transform crosses tables, and possibly even sources (compare element A in Table X in source 1 to elements B and C in source 2, and element D in source 3), then it becomes too difficult to develop and manage.

This was the status quo. I say “was” because we now have new tools and new methods that are well designed, engineered, tested, and understood to solve this challenge; and with the additional benefits that they are cheaper, faster, more accurate; and engender organizational cooperation. This is the combination of normalizing data with the computing power of Hadoop.

Data Normalization excels at correcting this challenge and does so with high levels of visibility, flexibility, collaboration, and alignment to business tempo. Raw data is the data that comes directly from sensors or other collectors and is typically known to be incorrect in some manner. This is not a problem as long as there is visible, collaborative, and evolving (remember the tenet that there is never a perfect result), knowledge of how to adjust it to make it better. This calibration is part of normalizing the raw data in a controlled, auditable manner to make it as meaningful as possible; while also having explicit information about how accurate it should be considered. Normalizing data needs adjustable and powerful computing tools. For very complicated data there are general purpose mathematical tools and specialized applications. However, corporate Small Data does not need this level of computing, but does need a way to code complicated business rules with clarity, openness to review, and ease and speed of updates.

This is what Hadoop provides. Hadoop is ready made for running programs on demand with the power of parallel computing and distributed storage. These are the very capabilities that enable Data Normalization to be part of mainstream business data management. One of the key needs of solving Small Data challenges that prior technologies could not provide, is low cycle time to make adjustments as more knowledge is gained and business requirements change (which will always occur, sometimes daily). Gone is the era when data could be managed in six to twelve month cycles of requirements, data modeling, ETL scripting, database engineering, and BI construction. All of this must respond and be in-step with business, not the other way around. With Hadoop, Data Normalization routines in Java programs can be run as often as desired and with multiple parallel jobs. This means a normalization routine that might have taken hours on an entire corporate warehouse can now be done in minutes. Results can then be used in any number of applications and tools.

A simple Hadoop cluster of just a handful of nodes will have enough power to normalize Small Data in concert with business tempo. Now, you can have accurate data that reflects the real business rules of your organization and that adapts and grows with you. Of course, getting this to work in your corporate production environment requires more than just the raw power of Hadoop technology. It requires mature and tested management of this technology and an assured integration of its parts that will not become a maintenance nightmare nor security risk.

This is exactly what the Cloudera distribution provides. All Hadoop components are tested, integrated, and bundled into a working environment with additional components specifically made to match the ease of management and maintenance of more traditional tools. Additionally, this distribution is being managed with clearly planned updates and version releases. While there are too many individual components to comment on now, one which deserves mention as a key aid to Data Normalization, and indeed the Hadoop environment itself, is Cloudera’s Hue web tool that allows browsing the file system, issuing queries to multiple data sources, planning and executing jobs, and reviewing metadata. If you have any questions, comments, or concerns about Small Data please join me live on Webcast II of our inaugural Tech Lab!

 

If anyone want to learn Bigdata hadoop than visit on -- www.bigdatahadoop.info

 

See more at: http://vision.cloudera.com/normalizing-corporate-small-data-with-hadoop-and-data-science/#sthash.ifmQLj4Z.dpuf