Big Data Hadoop Developer Course Content. Big Data Hadoop Developer - The Complete Course Course Duration: 45 Hours

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Barnard Perkins
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1 Big Data Hadoop Developer Course Content Who is the target audience? Big Data Hadoop Developer - The Complete Course Course Duration: 45 Hours Complete beginners who want to learn Big Data Hadoop Professionals who want to go for Big Data Hadoop Certifications Prerequisite Basic knowledge of UNIX Commands Knowledge of Core Java. Basic knowledge of SQL Commands Course Overview. Introduction to Big Data & Hadoop Learning Objectives: In this module, you will understand what Big Data is, Big Data Sources, Types of Data, the limitations of the traditional solutions for Big Data problems, how Hadoop solves those Big Data problems, Big Data Challenges, Big Data Use Cases, Hadoop Ecosystem, Hadoop Architecture, HDFS Introduction to Big Data & Big Data Challenges Vs of Big Data Limitations & Solutions of Big Data Architecture Hadoop & its Features Hadoop Ecosystem Hadoop 2.x Core Components Hadoop Storage: HDFS (Hadoop Distributed File System)

2 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, 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 HDFS Practical on Cloudera Java essential for Hadoop Learning Objectives: In this module, you will learn Java Programming language which is required to write code for MapReduce and Hadoop Installing and Configuring Java and Eclipse - Arrays Objects and Classes Control Flow Statements Interfaces and Inheritance Polymorphism Exception Handling

3 Checked Exception Unchecked Exception: Collection MapReduce Learning Objectives: In this module, you will understand Hadoop MapReduce framework and the working of MapReduce on data stored in HDFS. understand the different classes in Java, creating a Jar, Executing the program with data set, transferring the results into new File in HDFS. Concepts like Input Splits in MapReduce, Combiner & Partitioned and Demos on MapReduce using different data sets. Overview of MapReduce Framework, MapReduce Architecture, Learn about Job tracker and Task tracker, Use cases of MapReduce, Anatomy of MapReduce Program. Basic MapReduce API Concepts, Writing MapReduce Driver, Mappers, and Reducers in Java, Speeding up Hadoop Development by Using Eclipse, Unit Testing MapReduce Programs, Demo Example Pig Learning Objectives: In this module, you will learn Apache Pig, types of use cases where we can use Pig, Pig Latin scripting, Pig running modes, Pig UDF, Pig Streaming & Testing Pig Scripts Introduction to Apache Pig MapReduce vs Pig

4 Pig Components & Pig Execution Pig Data Types & Data Models in Pig Pig Latin Programs Pig Relational Operators Pig Evaluation Operators and Other Functions Shell and Utility Commands Pig UDF & Pig Streaming Testing Pig scripts with Punit Hive Learning Objectives: In this module, 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 Impala

5 Learning Objectives: In this module, you will understand Impala, benefits of Impala, Impala installation, how to run query with Impala, Learn the concept of Data Storage and File Format and many more Introduction to Impala Impala Architecture Querying with Hive and Impala Data Storage and File Format Working with the Impala Impala Daemon Impala Statestore Query Execution Flow in Impala User - Defined Functions Hive UDFs with Impala HBase Learning Objectives: In this module, you will understand in-depth knowledge of HBase Architecture and its components. how to use the HBase API and Clients, its integration with Hadoop Introduction to NoSQL Databases and HBase, HBase v/s RDBMS, HBase Components, HBase Architecture, HBase Cluster deployment. Storage Mechanism in HBase Bulk Load in HBase Advanced HBase HBase Data Model

6 HBase Shell HBase Client API for JAVA Data Loading Technique. Zookeeper Learning Objectives In this module, you will understand What is zookeeper, role of zookeeper, how it works, why we need zookeeper in Distributed applications. Requirements basic knowledge of hadoop and java Introduction to Zookeeper Architecture of Zookeeper Zookeeper workflow Zookeeper CLI Zookeeper Application on Industry use cases Oozie Learning Objectives In this module, you will understand How does OOZIE work? Example Workflow Diagram, Packaging and deploying an Oozie workflow application, Why use Oozie? Features of OOZIE Introduction to OOZIE Architecture of OOZIE OOZIE workflow Property file Coordinator OOZIE CLI

7 OOZIE workflow example Running OOZIE workflow Sqoop and Flume Learning Objectives In this module, you will understand techniques for moving data into Hadoop. There are a variety of ways to get data into Hadoop from simple Hadoop shell commands to more sophisticated processes. Several techniques are presented but two, Sqoop and Flume, are covered in greater detail. What is Sqoop and Flume Sqoop and Flume Architecture Benefits of Sqoop and Flume Load Scenarios on Industry use cases Hadoop 2.0 & YARN Learning Objectives: In this module, you will understand all the aspects about Apache Hadoop Yarn like Yarn introduction, Yarn Architecture, Yarn nodes/daemons resource manager and node manager. Yarn features, characteristics, and High availability modes. Introduction to YARN and MR2 daemons MapReduce vs YARN YARN Architecture Benefits of YARN

8 Active and Standby Namenodes Scheduler Resource Manager and Application Master Node Manager Container Objects and Container Namenode Federation Architectural differences between Hadoop 1.0 and 2.0 Hadoop 2.0 Configuration MongoDB Learning Objectives: In this module, you will get you up and running with MongoDB quickly, and teach you how to leverage its power for data analytics. We ll start by mastering the fundamentals of MongoDB, including MongoDB s Document data model, importing data into a cluster, working with our CRUD API and Aggregation Framework Introduction to MongoDB - Architecture and Installation Schema Design and Data Modelling Setup MongoDB MongoDB UI Client Basics Operation in MongoDB Indexing and Aggregation MongoDB Security Spark Learning Objectives: In this module, you will understand Spark s fundamental mechanics, Use the core Spark APIs to operate on data, Articulate and implement typical use cases for Spark, build data pipelines with SparkSQL and DataFrames, Analyze Spark jobs using the UIs and logs, Create Streaming and Machine Learning jobs

9 Prerequisites Basic Python or Scala required SQL is helpful but not required Spark Overview RDD Fundamentals SparkSQL and DataFrames Spark Job Execution Cluster Architectures for Spark Intro to Spark Streaming Machine Learning Basics Practical and Use cases for each Module Learning Objectives: In this module, you will hand on real time use cases as per current market Demand in the industry. Use cases Case Studies POC Projects

Big Data Syllabus. Understanding big data and Hadoop. Limitations and Solutions of existing Data Analytics Architecture

Big Data Syllabus. Understanding big data and Hadoop. Limitations and Solutions of existing Data Analytics Architecture Big Data Syllabus Hadoop YARN Setup Programming in YARN framework j Understanding big data and Hadoop Big Data Limitations and Solutions of existing Data Analytics Architecture Hadoop Features Hadoop Ecosystem