Best Hadoop Training in Bangalore

Big Data Hadoop Training in Bangalore INNOVATIVE ACADEMY's best Hadoop training institute in Bangalore is designed so Innovative to help you a clear understanding of Hadoop .You will gain an excellent knowledge on Bigdata and Hadoop, HDFS, PIG, HBASE and HIVE.

Best Hadoop Training in Bangalore.

Overview


Why Hadoop training?

  • Top MNC is trying to get into Big Data Hadoop hence there is a huge demand for Certified Big Data professionals.
  • Big Data is fastest growing and most promising technology for handling large volumes of data for doing data analytics
  • Hadoop Market is Expected to Reach $84.6 Billion, Globally, by 2021
  • The average salary is $120k for Hadoop developer as per the major job portal.

About Course

Hadoop tutorial will help you to understand the problem with the traditional system while processing Big Data and how Hadoop solves it and will provide you with a brief idea about HDFS, YARN, PIG, HBASE, HIVE and SPARK using examples and practical demonstration.

Why choose Innovative Academy for your Hadoop training?

  • Experienced technical trainers
  • Real Time and Hands-on Experience Training
  • On Time Course Completion & Superb satisfaction scores
  • Comprehensive curriculum, Innovative & interactive Training
  • High Pass Rate

Who can take this Training?

This Hadoop Training is constructed for IT pro's who needs to pursue the career in Cloud Industry and become Hadoop developer.

This training is suitable for:

  • Candidates with basic programming knowledge
  • professionals from BI( Business Intelligence) background
  • data warehousing professionals
  • Graduates looking to build a career in Big Data Analytics

Syllabus


1. Understanding Big Data and Hadoop

  • Big Data
  • big data characteristics
  • types of big data
  • Examples
  • applications of big data
  • challenges with big data
  • Features
  • Hadoop Ecosystem
  • Hadoop 2.x core components
  • Hadoop Storage: HDFS
  • Hadoop Processing: MapReduce Framework
  • Hadoop installation.

2. Hadoop Architecture and HDFS

  • HDFS
  • features of HDFS
  • HDFS architecture
  • advantage of hdfs
  • HDFS operations
  • HDFS commands.

3. Hadoop MapReduce Framework

  • MapReduce
  • Traditional way Vs MapReduce way
  • Why MapReduce
  • Hadoop 2.x MapReduce Architecture
  • Hadoop 2.x MapReduce Components
  • YARN MR Application Execution Flow
  • YARN Workflow
  • Anatomy of MapReduce Program
  • Input Splits
  • The relation between Input Splits and HDFS Blocks
  • MapReduce: Combiner & Partitioner

4. Advanced MapReduce

  • Counters
  • Distributed Cache
  • Joins in MapReduce
  • Custom Input Format
  • Sequence Input Format
  • XML file Parsing using MapReduce

5. Pig

  • About Pig
  • MapReduce Vs Pig
  • Pig Use Cases
  • Programming Structure in Pig
  • Pig Running Modes
  • Pig components
  • Pig Execution
  • Pig Latin Program
  • Data Models in Pig
  • Pig Data Types, Shell and Utility Commands
  • Pig Latin: Relational Operators
  • Built-In Functions

6. Hive

  • Hive Background
  • Hive Use Case
  • About Hive
  • Hive Vs Pig
  • Hive Architecture and Components
  • Metastore in Hive
  • Limitations of Hive
  • Comparison with Traditional Database
  • Hive Data Types and Data Models
  • Partitions and Buckets
  • Hive Tables (Managed Tables and External Tables)
  • Importing Data
  • Querying Data
  • Managing Outputs
  • Hive Script
  • Hive UDF
  • The retail use case Hive.

7. Advanced Hive and HBase

  • Hive QL: Joining Tables
  • Dynamic Partitioning
  • Custom Map/Reduce Scripts
  • Hive Indexes and views Hive query optimizers
  • User Defined Functions
  • HBase: Introduction
  • HBase Components
  • HBase Architecture
  • Run Modes & Configuration
  • HBase Cluster Deployment.

8. Advanced HBase

  • HBase Data Model
  • HBase Shell
  • HBase Client API
  • Data Loading Techniques
  • Zookeeper Data Model
  • Zookeeper Service
  • Demos on Getting and Inserting Data
  • Filters in HBase.

9. Apache Spark

  • What is Apache Spark
  • spark-Architecture
  • Execution and Related concepts
  • RDD operations
  • Functional programming in spark.

10. Apache RDDs in spark

  • RDD data types and RDD creation
  • operations in RDDs
  • RDD optimization techniques introduction
  • RDD persistence, Exercise and quiz

Duration


Course Curriculum and Duration:

We provide both Class-room Training and Offline Training.

Duration: 50Hrs.

Weekdays (Mon-Fri 2Hrs per day)

Weekend batches (Sat-Sun 4Hrs per day)

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