Big Data is among Top 3 Technologies and also having Highest Paid Jobs in this domain.

Benifits of Big Data Training

  • Big Data is among the Top 3 Technologies in the Market.
  • Data Generation increasingly day by day that’s why Now, Every sector is going to use Big Data.
  • So, Every MNC’s Interviewer wants this skill in your Resume.
  • You can get upto 12 LPA – 18 LPA Package for your 1st Job (as a Fresher) even you are from private engineering college because skills matters today.
  • You will Get Authentic Certification of Big Data that will help you to Crack Job Interview.

Course Description and Objectives

Big Data is the hot new buzzword in IT circles. The proliferation of digital technologies with digital storage and recording media has created massive amounts of diverse data, which can be used for marketing and many other purposes. The concept of Big Data refers to massive and often unstructured data, on which the processing capabilities of traditional data management tools result to be inadequate. Big Data can take up terabytes and petabytes of storage space in diverse formats including text, video, sound, images, and more.

Prerequisites

No Prerequisites Required for this Training. (Everything starts from Scratch to Advanced)

Syllabus of the Course

1. Introduction to Bigdata and Hadoop

  1. • Introduction to Big Data
  2. • Big Data Analytics
  3. • What is Big Data?
  4. • Four vs of Big Data
  5. • Challenges of Traditional System
  6. • Distributed Systems
  7. • Introduction to Hadoop

2. Hadoop Architecture Distributed Storage (HDFS) and YARN

  1. • What is HDFS
  2. • Need for HDFS
  3. • Regular File System vs HDFS
  4. • Characteristics of HDFS
  5. • HDFS Architecture and Components
  6. • High Availability Cluster Implementations
  7. • HDFS Component File System Namespace
  8. • Data Block Split
  9. • Data Replication Topology
  10. • HDFS Command Line
  11. • Demo: Common HDFS Commands
  12. • Practice Project: HDFS Command Line
  13. • Yarn Introduction
  14. • Yarn Use Case
  15. • Yarn and its Architecture
  16. • Resource Manager
  17. • Application Master
  18. • Demo

3. Data Ingestion into Big Data Systems

  1. • Data Ingestion Into Big Data Systems.
  2. • Apache Sqoop
  3. • Sqoop and Its Uses
  4. • Sqoop Processing
  5. • Sqoop Import Process

4. Distributed Processing MapReduce Framework

  1. • Distributed Processing Mapreduce Framework
  2. • Distributed Processing in Mapreduce
  3. • Word Count Example
  4. • Map Execution Phases
  5. • Mapreduce Jobs
  6. • Usage of Combiner
  7. • Different classes used in Map Reduce
  8. • Using Distributed Cache
  9. • Joins in Mapreduce
  10. • Replicated Join

5. Apache Hive

  1. • Apache Hive
  2. • Hive SQL over Hadoop Mapreduce
  3. • Hive Architecture
  4. • Hive Metastore
  5. • Hive DDL and DML
  6. • Creating New Table
  7. • File Format Types
  8. • Data Serialization
  9. • Hive Table and Avro Schema
  10. • Hive Optimization Partitioning Bucketing and Sampling
  11. • Dynamic Partitioning in Hive
  12. • Bucketing
  13. • Functions of Hive
  14. • Different type of Compression
  15. • Hive Table with Parquet schema

6. Apache Spark Next-Generation Big Data Framework

  1. • History of Spark
  2. • Limitations of Mapreduce in Hadoop
  3. • Introduction to Apache Spark
  4. • Components of Spark
  5. • Application of In-memory Processing
  6. • Hadoop Ecosystem vs Spark
  7. • Advantages of Spark
  8. • RDD in Spark
  9. • Creating Spark RDD
  10. • Pair RDD
  11. • RDD Operations
  12. • Lineage and DAG
  13. • Spark SQL Processing Data Frames
  14. • Data Frames
  15. • Process Data Frame Using SQL Query