Big Data Hadoop Certification Training
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Big Data & Hadoop Course Curriculum 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. Topics: 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. Topics: 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 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. Topics: 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
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. Topics: Counters Distributed Cache MRunit 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. Topics: 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. Topics: 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 indepth knowledge of Apache HBase, HBase Architecture, HBase running modes and its components. Topics: 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
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 application on Spark Cluster & comparing the performance of MapReduce and Spark. Topics: What is Spark Spark Ecosystem Spark Components What is Scala Why Scala SparkContext Spark RDD
