Best Hadoop training institute in Bangalore| Hadoop Bigdata trainings

Hadoop training in institute Bangalore | Bigdata Training

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Hadoop Developer training Bangalore

 080 4150 1359, 725 989 3449

What is this course about?

Big Data is a collection of large and complex data sets that cannot be processed using regular database management tools or processing applications. A lot of challenges such as capture, curation, storage, search, sharing, analysis, and visualization can be encountered while handling Big Data.

Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.  

Who should do this course?

Java developers, Architects, Big Data professionals, anyone who is looking forward towards building a career in Bigdata and Hadoop are ideal participants for the Big Data and Cloudera Hadoop training 

About Hadoop training:

This Hadoop developer training course delivers the key concepts and expertise participants need to create robust data processing applications using Apache Hadoop. From workflow implementation and working with APIs through writing MapReduce code and executing joins, this training course is the best preparation for the realworld challenges faced by Hadoop developers

Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning topics such as:
• The internals of MapReduce and HDFS and how to write MapReduce code
• Best practices for Hadoop development, debugging, and implementation of workflows and common algorithms
• How to leverage Hive, Pig, Sqoop, Flume, Oozie, and other Hadoop ecosystem projects
• Creating custom components such as WritableComparables and InputFormats to manage complex data types
• Writing and executing joins to link data sets in MapReduce
• Advanced Hadoop API topics required for real-world data analysis

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Download Hadoop training Bangalore Syllabus

Hadoop training Duration

12-15 days/ 2-3 hours per day

Hadoop training in Bangalore Availability

Batches available on Weekends

8AM, 11AM and 2 PM

2-3 hours per day on Saturdays and Sundays

Hadoop training Project:

This training includes a Case study/project included in the entire curriculum taken along with the course

Hadoop training in Bangalore Infrastructure

High End Servers & Setups available for Lab practice 

Hadoop course Trainer Profile

8+ Experienced real time Professionals

Hadoop course Pre-requisite/s: 

Working knowledge of SQL is required
Knowledge of Java is strongly recommended

Hadoop training Topics


The Motivation for Hadoop
Problems with Traditional Large-Scale Systems
Introducing Hadoop
Hadoopable Problems

Hadoop: Basic Concepts and HDFS
The Hadoop Project and Hadoop Components
The Hadoop Distributed File System

Introduction to MapReduce 
MapReduce Overview 
Example: WordCount 

Hadoop Clusters and the Hadoop Ecosystem
Hadoop Cluster Overview
Hadoop Jobs and Tasks
Other Hadoop Ecosystem Components

Writing a MapReduce Program in Java
Basic MapReduce API Concepts
Writing MapReduce Drivers, Mappers, and Reducers in Java
Speeding Up Hadoop Development by Using Eclipse
Unit Testing MapReduce Programs

Delving Deeper into the Hadoop API
Setting Up and Tearing Down Mappers and Reducers
Decreasing the Amount of Intermediate Data with Combiners
Using The Distributed Cache
Using the Hadoop API’s Library of Mappers, Reducers, and Partitioners

Partitioners and Reducers
How Partitioners and Reducers Work Together
Determining the Optimal Number of Reducers for a Job
Writing Custom Partitioners

Data Input and Output
Writable and WritableComparable Techniques
File Compression Technique
Implementing Custom InputFormats and OutputFormats

Joining Data Sets in MapReduce Jobs
Writing a Map-Side Join
Writing a Reduce-Side Join

Integrating Hadoop into the Enterprise Workflow
Integrating Hadoop into an Existing Enterprise
Loading Data from an RDBMS into HDFS by Using Sqoop

An Introduction to Hive, and Pig
The Motivation for Hive, and Pig
Hive Overview
Pig Overview
Choosing Between Hive, and Pig

An Introduction to Oozie
Introduction to Oozie
Creating Oozie Workflows


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What is Big Data?

The term Big Data is being increasingly used almost everywhere on the planet – online and offline. The term Big Data is used to explain the huge volume of data that is so large and is difficult to be processed using traditional methods.

But Big Data isn’t just about the amount of data we’re generating, it’s also about all the different types of data . In fact, Big Data has four important characteristics that are known in the industry as the 4 V’s:

Volume – the increasing amount of data that is generated every second
Velocity – the speed at which data is being generated
Variety – the different types of data being generated
Veracity – the messiness of data, ie. it’s unstructured nature

 Why is Bigdata important?

All this data can be used to get different results using different types of analysis. It is not necessary that that all analysis use all the data. Different analysis uses different parts of the BIG DATA to produce the results and predictions necessary. Big Data is essentially the data that you analyze for results that you can use for predictions and for other uses. 

What is Hadoop?

Hadoop is an open source software framework that supports data-intensive distributed applications. It is a family of open-source products and technologies under the Apache Software Foundation (ASF). The Apache Hadoop library includes: the Hadoop Distributed File System (HDFS), MapReduce, Hive, Hbase, Pig, Zookeeper, Flume, Sqoop, Oozie, Hue, and ther applications.

What is MapReduce?

Hadoop MapReduce is a software framework for easily writing applications which process vast amounts of data (multi-terabyte data-sets) in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner.

A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system. The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks.

What is HIVE?

The Apache Hive datawarehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage. A command line tool and JDBC driver are provided to connect users to Hive.

What is HBASE?

Apache HBase™ is the Hadoop database, a distributed, scalable, big data store. Use Apache HBase™ when you need random, realtime read/write access to your Big Data. This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, Apache HBase provides Bigtable-like capabilities on top of Hadoop and HDFS.

What is PIG?

Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets.

At the present time, Pig's infrastructure layer consists of a compiler that produces sequences of Map-Reduce programs, for which large-scale parallel implementations already exist (e.g., the Hadoop subproject). Pig's language layer currently consists of a textual language called Pig Latin, which has the following key properties:

1. Ease of programming. It is trivial to achieve parallel execution of simple, "embarrassingly parallel" data analysis tasks. Complex tasks comprised of multiple interrelated data transformations are explicitly encoded as data flow sequences, making them easy to write, understand, and maintain.

2. Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically, allowing the user to focus on semantics rather than efficiency.

3. Extensibility. Users can create their own functions to do special-purpose processing.

What is ZooKeeper?

ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. All of these kinds of services are used in some form or another by distributed applications. Each time they are implemented there is a lot of work that goes into fixing the bugs and race conditions that are inevitable. Because of the difficulty of implementing these kinds of services, applications initially usually skimp on them ,which make them brittle in the presence of change and difficult to manage. Even when done correctly, different implementations of these services lead to management complexity when the applications are deployed.

What is Oozie?

Oozie is a workflow scheduler system to manage Apache Hadoop jobs. Oozie Workflow jobs are Directed Acyclical Graphs (DAGs) of actions. Oozie Coordinator jobs are recurrent Oozie Workflow jobs triggered by time (frequency) and data availability. Oozie is integrated with the rest of the Hadoop stack supporting several types of Hadoop jobs out of the box (such as Java map-reduce, Streaming map-reduce, Pig, Hive, Sqoop and Distcp) as well as system specific jobs (such as Java programs and shell scripts). Oozie is a scalable, reliable and extensible system.