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

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

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

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

The Early Release Books Keep Coming: This Time, Hadoop Security

We are thrilled to announce the availability of the early release of Hadoop Security, a new book about security in the Apache Hadoop ecosystem published by O’Reilly Media. The early release contains two chapters on System Architecture and Securing Data Ingest and is available in O’Reilly’s catalog and in Safari Books.

Hadoop security

The goal of the book is to serve the experienced security architect that has been tasked with integrating Hadoop into a larger enterprise security context. System and application administrators also benefit from a thorough treatment of the risks inherent in deploying Hadoop in production and the associated how and why of Hadoop security.

As Hadoop continues to mature and become ever more widely adopted, material must become specialized for the security architects tasked with ensuring new applications meet corporate and regulatory policies. While it is up to operations staff to deploy and maintain the system, they won’t be responsible for determining what policies their systems must adhere to. Hadoop is mature enough that dedicated security professionals need a reference to navigate the complexities of security on such a massive scale. Additionally, security professionals must be able to keep up with the array of activity in the Hadoop security landscape as exemplified by new projects like Apache Sentry (incubating) and cross-project initiatives such as Project Rhino.

Security architects aren’t interested in how to write a MapReduce job or how HDFS splits files into data blocks, they care about where data is going and who will be able to access it. Their focus is on putting into practice the policies and standards necessary to keep their data secure. As more corporations turn to Hadoop to store and process their most valuable data, the risks with a potential breach of those systems increases exponentially. Without a thorough treatment of the subject, organizations will delay deployments or resort to siloed systems that increase capital and operating costs.

The first chapter available is on the System Architecture where Hadoop is deployed. It goes into the different options for deployment: in-house, cloud, and managed. The chapter also covers how major components of the Hadoop stack get laid out physically from both a server perspective and a network perspective. It gives a security architect the necessary background to put the overall security architecture of a Hadoop deployment into context.

The second available chapter is on Securing Data Ingest it covers the basics of Confidentiality, Integrity, and Availability (CIA) and applies them to feeding your cluster with data from external systems. In particular, the two most common data ingest tools, Apache Flume and Apache Sqoop, are evaluated for their support of CIA. The chapter details the motivation for securing your ingest pipeline as well as providing ample information and examples on how to configure these tools for your specific needs. The chapter also puts the security of your Hadoop data ingest flow into the broader context of your enterprise architecture.

We encourage you to take a look and get involved early. Security is a complex topic and it never hurts to get a jump start on it. We’re also eagerly awaiting feedback. We would never have come this far without the help of some extremely kind reviewers. You can also expect more chapters to come in the coming months. We’ll continue to provide summaries on this blog as we release new content so you know what to expect.

If anyone want to learn Big Data Hadoop Training than Visit on - http://www.bigdatahadoop.info