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Overview

Course Description

As humans, we are immersed in data in our every-day lives. As per IBM, the data doubles every two years on this planet. The value that data holds can only be understood when we can start to identify patterns and trends in the data. Normal computing principles do not work when data becomes huge.
There is massive growth in the big data space, and job opportunities are skyrocketing, making this the perfect time to launch your career in this space.
In this specialization, you will learn Hadoop and Spark to drive better business decisions and solve real-world problems.

Who should go for this training?

Big Data Hadoop and Spark Developer training course is best suited for IT, data management, and analytics professionals looking to gain expertise in Big Data Hadoop, including Software Developers and Architects, Analytics Professionals, Senior IT professionals, Testing and Mainframe Professionals, Data Management Professionals, Business Intelligence Professionals, Project Managers, Aspiring Data Scientists, Graduates looking to begin a career in Big Data Analytics

Who should go for this training?

Big Data Hadoop and Spark Developer training course is best suited for IT, data management, and analytics professionals looking to gain expertise in Big Data Hadoop, including Software Developers and Architects, Analytics Professionals, Senior IT professionals, Testing and Mainframe Professionals, Data Management Professionals, Business Intelligence Professionals, Project Managers, Aspiring Data Scientists, Graduates looking to begin a career in Big Data Analytics

Requirements

There are no prerequisites to take up this course. But basics of UNIX, SQL and Java would be good to learn Big Data Hadoop.

Course Syllabus

  • What is Big Data?
  • Limitations & Solutions of Big Data Architecture
  • What is Hadoop?
  • Hadoop Ecosystem
  • Hadoop 2.x Core Components
  • Hadoop Ecosystem
  • Hadoop Processing: MapReduce Framework
  • Different Hadoop Distributions
  • 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
  • Learning the working mechanism of MapReduce
  • Understanding the mapping and reducing stages in MR
  • Developing Map Reduce
  • Map Reduce Programming Model
  • Custom Input Format
  • Sequence Input Format
  • Data localization in Map Reduce
  • Question-Answer Session
  • 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
  • Hive introduction
  • Hive architecture
  • Hive vs RDBMS
  • HiveQL and the shell
  • Creation of a database, table, group by and other clauses
  • Managing tables (external vs managed)
  • Data types and schemas
  • Storing the Hive Results, Hive partitioning, and Buckets
  • Hive QL: Joining Tables, Dynamic Partitioning
  • Custom MapReduce Scripts
  • Hive Indexes and views
  • Hive Query Optimizers and Thrift Server
  • Hive UDF
  • Apache HBase: Introduction to NoSQL Databases and HBase
  • HBase v/s RDBMS
  • HBase Components and Architecture
  • HBase Run Modes
  • HBase Configuration
  • HBase Cluster Deployment
  • Question-Answer Session
  • HBase Data Model
  • HBase Shell
  • HBase Client API
  • Hive Data Loading Techniques
  • Apache Zookeeper Introduction
  • ZooKeeper Data Model and Service
  • HBase Bulk Loading
  • Getting and Inserting Data
  • HBase Filters
  • Question-Answer Session
  • Apache Sqoop introduction
  • Sqoop syntax
  • Database connection
  • Importing and exporting data
  • Performance improvement with Sqoop
  • Introduction to Flume
  • Flume syntax
  • understanding the architecture of Flume
  • Database connection
  • Importing and exporting data
  • Question-Answer Session
  • Introduction to Apache Spark
  • Apache Spark Framework
  • Playing with RDD’s
  • Using Spark Shell
  • Writing Spark Applications
  • DataFrames and DataSets
  • DataFrame Operations
  • Creating & Saving DataFrames from Data Sources
  • Transformations & Actions
  • Caching & Persisting
  • The detailed Spark SQL
  • The significance of SQL in Spark for working with structured data processing
  • Spark SQL JSON support
  • Working with XML data and parquet files
  • Creating Hive Context
  • Writing Data Frame to Hive
  • How to read a JDBC file?
  • Significance of a Spark data frame
  • How to create a data frame?
  • What is schema manual inferring?
  • Work with CSV files, JDBC table reading, data conversion from Data Frame to JDBC
  • Work with Spark SQL user-defined functions, shared variable, and accumulators
  • How to query and transform data in Data Frames?
  • How data frame provides the benefits of both Spark RDD and Spark SQL?
  • Deploying Hive on Spark as the execution engine
  • Question-Answer Session
  • Knowledge Check

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