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Course Description

Course Content

  • 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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