Big Data Training Topics


Credo Systemz is the Best big data training in Chennai and we have designed the hadoop training syllabus in such a way to meet all the requirements. Hadoop Course Content is the combination of Hadoop Developer, Hadoop Administrator, Hadoop Testing, and Analytics training which is the effective way to provide big data training with placement in Chennai.We have framed the best big data training in chennai course syllabus that facilitates the needs of beginners and advanced levels professionals. In Credo Systemz big data training topics are prepared by hadoop industry experts and that topics are based on interview focused.We are also Provided Basic Java Programming training needed for hadoop training course so helpful to understand the all hadoop course concepts.

Big data training topics are covered by our hadoop tutors who having 10+ experience in hadoop industry. We are provides best class room,online and corporate training and covered all training topics at practical oriented. Credo Systemz conducted Hadoop Demo Session before joining the hadoop bigdata training which helpful to know about trainer. The below Big Data training topics are based on interview topics and all topics are covered by professional way in hadoop training in chennai velachery. Book a Free Hadoop demo session to know about  big data course fees in Chennai and get best big data training.

BIG DATA-HADOOP TRAINING COURSE MODULES


Section 1: INTRODUCTION TO BIG DATA-HADOOP

  • Overview of Hadoop Ecosystem
  • Role of Hadoop in Big data– Overview of other Big Data Systems
  • Who is using Hadoop
  • Hadoop integrations into Exiting Software Products
  • Current Scenario in Hadoop Ecosystem
  • Installation
  • Configuration
  • Use Cases ofHadoop (HealthCare, Retail, Telecom)

Section 2: HDFS

  • Concepts
  • Architecture
  • Data Flow (File Read , File Write)
  • Fault Tolerance
  • Shell Commands
  • Data Flow Archives
  • Coherency -Data Integrity
  • Role of Secondary NameNode

Section 3: MAPREDUCE

  • Theory
  • Data Flow (Map – Shuffle – Reduce)
  • MapRed vs MapReduce APIs
  • Programming [Mapper, Reducer, Combiner, Partitioner]
  • Writables
  • InputFormat
  • Outputformat
  • Streaming API using python
  • Inherent Failure Handling using Speculative Execution
  • Magic of Shuffle Phase
  • FileFormats
  • Sequence Files

Section 4: HBASE

  • Introduction to NoSQL
  • CAP Theorem
  • Classification of NoSQL
  • Hbase and RDBMS
  • HBASE and HDFS
  • Architecture (Read Path, Write Path, Compactions, Splits)
  • Installation
  • Configuration
  • Role of Zookeeper
  • HBase Shell  Introduction to Filters
  • RowKeyDesign -What’s New in HBase  Hands On

Section 5: HIVE

  • Architecture
  • Installation
  • Configuration
  • Hive vs RDBMS
  • Tables
  • DDL
  • DML
  • UDF
  • Partitioning
  • Bucketing
  • Hive functions
  • Date functions
  • String functions
  • Cast function Meta Store
  • Joins
  • Real-time HQL will be shared along with database migration project

Section 6: PIG

  • Architecture
  • Installation
  • Hive vs Pig
  • Pig Latin Syntax
  • Data Types
  • Functions (Eval, Load/Store, String, DateTime)
  • Joins
  • UDFs- Performance
  • Troubleshooting
  • Commonly Used Functions

Section 7: SQOOP

  • Architecture , Installation, Commands(Import , Hive-Import, EVal, Hbase Import, Import All tables, Export)
  • Connectors to Existing DBs and DW

Section 8: KAFKA

  • Kafka introduction
  • Data streaming Introduction
  • Producer-consumer-topics
  • Brokers
  • Partitions
  • Unix Streaming via kafka

Section 9: OOZIE

  • Architecture
  • Installation
  • Workflow
  • Coordinator
  • Action (Mapreduce, Hive, Pig, Sqoop)
  • Introduction to Bundle
  • Mail Notifications

Section 10: HADOOP 2.0 and Spark

  • Limitations in Hadoop
  • 1.0 – HDFS Federation
  • High Availability in HDFS
  • HDFS Snapshots
  • Other Improvements in HDFS2
  • Introduction to YARN aka MR2
  • Limitations in MR1
  • Architecture of YARN
  • MapReduce Job Flow in YARN
  • Introduction to Stinger Initiative and Tez
  • BackWard Compatibility for Hadoop 1.X
  • Spark Fundamentals
  • RDD- Sample Scala Program- Spark Streaming

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