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BIg Data Hadoop Analyst Certification in Delhi NCR | Yami Services

Big Data Hadoop Analyst

Join With Our Courses To Develop Yourself.


Courses Overview

his course will enable an Analyst to work on Big Data and Hadoop which takes into consideration the burgeoning demands of the industry to process and analyze data at high speeds. This Training Course will give you the right skills to deploy various tools and techniques to be a Hadoop Analyst working with Big Data.

Introduction to Big Data & Hadoop and its Ecosystem, Map Reduce and HDFS

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 Installation & setup

Hadop 2.x cluster architecture, federation and high availability, a specific production cluster setup, headop cluster mode, common headop shell command, headop 2.x configuration files, claudera single node cluster

Deep Dive in Mapreduce

How the Works of Howard Works, How Reduce Works, How the Driver Works, Combines, Participants, Input Formats, Output Formats, Shuffle and Sort, MapsSide Join, Side Join, MRuneite, Distributed Cash

Lab exercises :

Working with HDFS, composing wordcount programs, composing custom accomplices, mappidas, outline joins with organizer, side joining, unit testing mappidas, running mappidas in neighborhood work sprinter mode

Graph Problem Solving

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:

Detailed understanding of Pig

A. Introduction to Pig

Understanding Apache pig, learning to talk with features, different uses and pigs

B. Deploying Pig for data analysis

Pig Latin syntax, various definitions, data sort and filter, data type, pig deployment for ETL, data loading, schema viewing, field definitions, commonly used functions

C. Pig for complex data processing

Various data types including nests and complexes, processing data with pig, grouped data repetition, practical exercises

D. Performing multi-dataset operations

Joining the Data Set, Data Set Partition, Different Methods for Combining Data Set, Set Operations, Handheld Practice

E. Extending Pig

Understanding user-defined functions, streaming to increase pig and using UDF to do data processing with other languages, import and macros, practical exercises

F. Pig Jobs

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

Detailed understanding of Hive

A. Hive Introduction

Understanding hive, traditional database comparison with hive, pig and hive comparison, data collection in hive and hive schema, different use cases of hive interaction and hive

B. Hive for relational data analysis

HiveQL, basic syntax, deploying various tables and databases, data types, data sets, understanding various underlying tasks, deploying hive queries on scripts, shell and hue.

C. Data management with Hive

Various databases, creation of databases, data formats in the hive, data modeling, hive-managed tables, self-managed tables, data loading, changing database and tables, query simplification with views, storage results 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 inquiries

Deploying hive for large amounts of data sets and large amounts of inquiries

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 are the relation databases, boundaries and impala distorted from future directions using Impla Shell

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

C. Modeling and Managing Data with Impala and Hive

Data Storage Overview, Creating Database and Tables, Loading Tables, Hctel, Impla Metadata Caching Data

D. Data Partitioning

Partition Overview, Split In Impala And Hive

(AVRO) Data Formats

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

Introduction to Hbase architecture

What is Hubble, where does it fit, what is NOSQL?

Hadoop Cluster Setup and Running Map Reduce Jobs

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

ETL Connectivity with Hadoop Ecosystem

How ETL tools work in the Big Data Industry, connect with HDFS with ETL equipment and taking data from HDFS to local system, taking data from DBMS to HDFS, working with it Hive with ETL Tool, Creating Map Reduce job in ETL tool, End to End ETL PoC showing big data integration with ETL tool.

Job and Certification

Significant undertaking, Hadop improvement, Claudera Certification tips and direction and fake meeting arrangement, down to earth advancement tips and methods, accreditation planning.


  • Duration: 40 Hrs
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