20 students

Understanding Big Data and Hadoop

Learning Objectives: In this module, you will understand what Big Data is, the limitations of the traditional solutions for Big Data problems, how Hadoop solves those Big Data problems, Hadoop Ecosystem, Hadoop Architecture, HDFS, Anatomy of File Read and Write & how MapReduce works.

Introduction to Big Data & Big Data Challenges

  • Limitations & Solutions of Big Data Architecture
  • Hadoop & its Features
  • Hadoop Ecosystem
  • Hadoop 2.x Core Components
  • Hadoop Storage: HDFS (Hadoop Distributed File System)
  • Hadoop Processing: MapReduce Framework
  • Different Hadoop Distributions

Hadoop Architecture and HDFS

Learning Objectives: In this module, you will learn Hadoop Cluster Architecture, important configuration files of Hadoop Cluster, Data Loading Techniques using Sqoop & Flume, and how to setup Single Node and Multi-Node Hadoop Cluster.

  • Hadoop 2.x Cluster Architecture 
  • Federation and High Availability Architecture 
  • Typical Production Hadoop Cluster
  • Hadoop Cluster Modes
  • Common Hadoop Shell Commands 
  • Hadoop 2.x Configuration Files
  • Single Node Cluster & Multi-Node Cluster set up
  • Basic Hadoop Administration

Hadoop MapReduce Framework

Learning Objectives: In this module, you will understand the Hadoop MapReduce framework comprehensively, the working of MapReduce on data stored in HDFS. You will also learn the advanced MapReduce concepts like Input Splits, Combiner & Partitioner.

Traditional way vs MapReduce way

  • Why MapReduce 
  • YARN Components
  • YARN Architecture
  • YARN MapReduce Application Execution Flow
  • YARN Workflow
  • Anatomy of MapReduce Program 
  • Input Splits, Relation between Input Splits and HDFS Blocks
  • MapReduce: Combiner & Partitioner
  • Demo of Health Care Dataset
  • Demo of Weather Dataset

Advanced Hadoop MapReduce

Learning Objectives: In this module, you will learn advanced MapReduce concepts such as Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format and XML parsing

  • Counters
  • Distributed Cache
  • MR unit
  • Reduce Join
  • Custom Input Format 
  • Sequence Input Format
  • XML file Parsing using MapReduce

Apache Pig

Learning Objectives: In this module, you will learn Apache Pig, types of use cases where we can use Pig, tight coupling between Pig and MapReduce, and Pig Latin scripting, Pig running modes, Pig UDF, Pig Streaming & Testing Pig Scripts. You will also be working on healthcare dataset.

  • Introduction to Apache Pig 
  • MapReduce vs Pig
  • Pig Components & Pig Execution
  • Pig Data Types & Data Models in Pig
  • Pig Latin Programs
  • Shell and Utility Commands
  • Pig UDF & Pig Streaming
  • Testing Pig scripts with Punit
  • Aviation use-case in PIG
  • Pig Demo of Healthcare Dataset

Apache Hive

Learning Objectives: This module will help you in understanding Hive concepts, Hive Data types, loading and querying data in Hive, running hive scripts and Hive UDF.

  • Introduction to Apache Hive 
  • Hive vs Pig
  • Hive Architecture and Components 
  • Hive Metastore
  • Limitations of Hive
  • Comparison with Traditional Database
  • Hive Data Types and Data Models
  • Hive Partition
  • Hive Bucketing
  • Hive Tables (Managed Tables and External Tables)
  • Importing Data
  • Querying Data & Managing Outputs
  • Hive Script & Hive UDF
  • Retail use case in Hive
  • Hive Demo on Healthcare Dataset

Advanced Apache Hive and HBase

Learning Objectives: In this module, you will understand advanced Apache Hive concepts such as UDF, Dynamic Partitioning, Hive indexes and views, and optimizations in Hive. You will also acquire in-depth knowledge of Apache HBase, HBase Architecture, HBase running modes and its components.

  • Hive QL: Joining Tables, Dynamic Partitioning 
  • Custom MapReduce Scripts
  • Hive Indexes and views
  • Hive Query Optimizers
  • Hive Thrift Server
  • Hive UDF 
  • Apache HBase: Introduction to NoSQL Databases and HBase 
  • HBase v/s RDBMS
  • HBase Components
  • HBase Architecture 
  • HBase Run Modes
  • HBase Configuration
  • HBase Cluster Deployment

Advanced Apache HBase

Learning Objectives: This module will cover advanced Apache HBase concepts. We will see demos on HBase Bulk Loading & HBase Filters. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster & why HBase uses Zookeeper.

  • HBase Data Model 
  • HBase Shell
  • HBase Client API
  • Hive Data Loading Techniques
  • Apache Zookeeper Introduction
  • ZooKeeper Data Model
  • Zookeeper Service
  • HBase Bulk Loading 
  • Getting and Inserting Data
  • HBase Filters

Processing Distributed Data with Apache Spark

Learning Objectives: In this module, you will learn what is Apache Spark, SparkContext & Spark Ecosystem. You will learn how to work in Resilient Distributed Datasets (RDD) in Apache Spark. You will be running the application on Spark Cluster & comparing the performance of MapReduce and Spark.

  • What is Spark 
  • Spark Ecosystem
  • Spark Components 
  • What is Scala 
  • Why Scala
  • SparkContext
  • Spark RDD

Oozie and Hadoop Project

Learning Objectives: In this module, you will understand how multiple Hadoop ecosystem www.edureka.co © 2019 Brain4ce Education Solutions Pvt. Ltd. All rights Reserved. components work together to solve Big Data problems. This module will also cover Flume & Sqoop demo, Apache Oozie Workflow Scheduler for Hadoop Jobs, and Hadoop Talend integration.

  • Oozie 
  • Oozie Components
  • Oozie Workflow
  • Scheduling Jobs with Oozie Scheduler
  • Demo of Oozie Workflow
  • Oozie Coordinator
  • Oozie Commands
  • Oozie Web Console
  • Oozie for MapReduce
  • Combining flow of MapReduce Jobs
  • Hive in Oozie
  • Hadoop Project
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₹15,000.00