Big Data Hadoop Analyst Training Online

1. Hadoop Architecture and Ecosystem
2. Learn about Apache Hive, Pig, YARN
3. Complex data processing techniques
4. Come up with Hadoop real-time queries using Impala
5. Integrate HBase with MapReduce
6. Deploy MapReduce advanced Indexing
7. ETL connectivity with Hadoop ecosystem
8. Real-time analysis on large data sets

• Business Professionals,Data and System Analysts
• ETL and Data warehousing Professionals, Project Managers and Business Intelligence experts
• Anyone wants to learn Big data and Hadoop and doesn’t have programming experience

A basic knowledge in any programming language is beneficial but not necessary.

Hadoop is gaining a steady groundswell with some of the biggest companies exclusively relying on Hadoop for making sense of Big Data. This Combo Course will help you work on the Hadoop framework and process humungous amounts of data at top speeds so as to make sense of it in real-time. There is a huge demand for professionals with the exact skills that this Training Course is providing. This shall ensure you can get top salaries and grow in your career.

What is Big Data, Where does Hadoop fit in, Hadoop Distributed File System – Replications, Block Size, Secondary Namenode, High Availability, Understanding YARN – ResourceManager, NodeManager, Difference between 1.x and 2.x

Hadoop 2.x Cluster Architecture , Federation and High Availability, A Typical Production Cluster setup , Hadoop Cluster Modes, Common Hadoop Shell Commands, Hadoop 2.x Configuration Files, Cloudera Single node cluster

How Mapreduce Works, How Reducer works, How Driver works, Combiners, Partitioners, Input Formats, Output Formats, Shuffle and Sort, Mapside Joins, Reduce Side Joins, MRUnit, Distributed Cache

Working with HDFS, Writing WordCount Program, Writing custom partitioner, Mapreduce with Combiner , Map Side Join, Reduce Side Joins, Unit Testing Mapreduce, Running Mapreduce in Local Job Runner Mode

What is Graph, Graph Representation, Breadth first Search Algorithm, Graph Representation of Map Reduce, How to do the Graph Algorithm, Example of Graph Map Reduce,

Exercise 1: Exercise 2:Exercise 3:

A. Introduction to Pig

Understanding Apache Pig, the features, various uses and learning to interact with Pig

B. Deploying Pig for data analysis

The syntax of Pig Latin, the various definitions, data sort and filter, data types, deploying Pig for ETL, data loading, schema viewing, field definitions, functions commonly used.

C. Pig for complex data processing

Various data types including nested and complex, processing data with Pig, grouped data iteration, practical exercise

D. Performing multi-dataset operations

Data set joining, data set splitting, various methods for data set combining, set operations, hands-on exercise

E. Extending Pig

Understanding user defined functions, performing data processing with other languages, imports and macros, using streaming and UDFs to extend Pig, practical exercises

F. Pig Jobs

Working with real data sets involving Walmart and Electronic Arts as case study

A. Hive Introduction

Understanding Hive, traditional database comparison with Hive, Pig and Hive comparison, storing data in Hive and Hive schema, Hive interaction and various use cases of Hive

B. Hive for relational data analysis

Understanding HiveQL, basic syntax, the various tables and databases, data types, data set joining, various built-in functions, deploying Hive queries on scripts, shell and Hue.

C. Data management with Hive

The various databases, creation of databases, data formats in Hive, data modeling, Hive-managed Tables, self-managed Tables, data loading, changing databases and Tables, query simplification with Views, result storing of queries, data access control, managing data with Hive, Hive Metastore and Thrift server.

D. Optimization of Hive

Learning performance of query, data indexing, partitioning and bucketing

E. Extending Hive

Deploying user defined functions for extending Hive

F. Hands on Exercises – working with large data sets and extensive querying

Deploying Hive for huge volumes of data sets and large amounts of querying

G. UDF, query optimization

Working extensively with User Defined Queries, learning how to optimize queries, various methods to do performance tuning.

A. Introduction to Impala

What is Impala?, How Impala Differs from Hive and Pig, How Impala Differs from Relational Databases, Limitations and Future Directions, Using the Impala Shell

B. Choosing the Best (Hive, Pig, Impala)

C. Modeling and Managing Data with Impala and Hive

Data Storage Overview, Creating Databases and Tables, Loading Data into Tables, HCatalog, Impala Metadata Caching

D. Data Partitioning

Partitioning Overview, Partitioning in Impala and Hive

Selecting a File Format, Tool Support for File Formats, Avro Schemas, Using Avro with Hive and Sqoop, Avro Schema Evolution, Compression

What is Hbase, Where does it fits, What is NOSQL

Multi Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup, Running Map Reduce Jobs on Cluster

How ETL tools work in Big data Industry, Connecting to HDFS from ETL tool and moving data from Local system to HDFS, Moving Data from DBMS to HDFS, Working with Hive with ETL Tool, Creating Map Reduce job in ETL tool, End to End ETL PoC showing big data integration with ETL tool.

Major Project, Hadoop Development, cloudera Certification Tips and Guidance and Mock Interview Preparation, Practical Development Tips and Techniques, certification preparation.

Project 1 – Working with MapReduce, Hive, Sqoop

Problem Statement – It describes that how to import mysql data using sqoop and querying it using hive and also describes that how to run the word count mapreduce job.

Project 2 – Connecting Pentaho with Hadoop Eco-system

Problem Statement – It includes:

Topics: Quick Overview of ETL and BI, Configuring Pentaho to work with Hadoop Distribution, Loading data into Hadoop cluster, Transforming data into Hadoop cluster, Extracting data from Hadoop Cluster