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Apache Spark Training in Chandigarh

Apache Spark Training in Chandigarh – Institute Chandigarh provide the best training in Hadoop Spark in Chandigarh, Mohali and Panchkula. 100% practical training with live projects.

Apache Spark developer training

Course Syllabus of Apache Spark:

Hadoop Overview

  • Lecture
    • How HDFS read/write the data
    • YARN internal architecture
    • HDFS Internal Architecture .
  • Hands-On
    • HDFS Shell Commands
    • Install Hadoop & Spark in Ubuntu
    • Configure hadoop/spark environment in Eclipse

Hive Overview

  • Lecture
    • How Hive functioning properly
    • Optimize Hive queries
    • Using Sqoop
  • Hands-On
    • Process csv, json data
    • Bucketing, Partitioning tables.
    • Import MySQL/Oracle data using Sqoop

Scala Basics

  • Lecture
    • Functional language
    • Scala Vs Java
  • Hands-On
    • Strings, Numbers
    • List, Array, Map, Set
    • Control Statements, collections
    • Functions, methods
    • Patren matching

Spark Overview

  • Lecture
    • The power of Spark?
    • Spark Ecosystem
    • Spark Components vs Hadoop
  • Hands-On
    • Installation & Eclipse configuration
    • Programs in Command line Interface & Eclipse
    • Process Local, HDFS files

RDD Fundamentals

  • Lecture
    • Purpose and Structure of RDDs
    • Transformations, Actions, and DAG
    • Key-Value Pair RDDs
  • Hands-On
    • Creating RDDs from Data Files
    • Reshaping Data to Add Structure
    • Interactive Queries Using RDDs

SparkSQL and DataFrames

  • Lecture
    • Spark SQL and DataFrame Uses
    • DataFrame / SQL APIs
    • Catalyst Query Optimization
  • Hands-on
    • Creating (CSV, JSON) DataFrames
    • Querying with DataFrame API and SQL
    • Caching and Re-using DataFrames
    • Process Hive data in Spark

Spark DataSet API

  • Lecture
    • Power of Dataset API in Spark 2.0
    • Serialization concept in DataSet
  • Hands-on
    • Creating DataSet API
    • Process CSV, JSON, XML, Text data
    • DataSet Operation

Spark Job Execution

  • Lecture
    • Jobs, Stages, and Tasks
    • Partitions and Shuffles
    • Broadcast Variables and accumulators
    • Job Performance
  • Hands-On
    • Visualizing DAG Execution
    • Observing Task Scheduling
    • Understanding Performance
    • Measuring Memory Usage
    • shared variables usage

Clustering Architecture

  • Lecture
    • Cluster Managers for Spark: Spark Standalone, YARN, and Mesos
    • Understanding Spark on YARN
    • What happened in cluster when you submit a job
  • Hands-On
    • Tracking Jobs through the Cluster UI
    • Understanding Deploy Modes
    • Submit a sample job and monitor job

Spark Streaming

  • Lecture
    • Streaming Sources and Tasks
    • DStream APIs and Stateful Streams
    • Flink Introduction
    • Kafka architecture
  • Hands-On
    • Creating DStreams from Sources
    • Operating on DStream Data
    • Viewing Streaming Jobs in the Web UI
    • Sample Flink Streaming program.
    • Kafka sample program

AWS with Spark

  • Lecture
    • AWS architecture
    • Redshift, EMR and EC2 functionalities
    • How to minimize AWS cost
  • Hands-On
    • Submit a sample jar in AWS Cluster
    • Create a cluster using EMR
    • Read/Write data from Redshift

Advanced concepts in Spark

  • Lecture
    • Memory management in Spark
    • How to optimize Spark Applications
    • Spark how to integrate with other Applications
  • Hands-On
    • Spark with Cassandra Integration
    • Alluxio/Tachyon hands on experience

Sample Spark Project

  • Lecture
    • End to end a project overview
    • Complicated problems in a project
    • Common steps in any project
  • Hands-On
    • Implement Spark SQL Mini project
    • Kafka, Cassandra, Spark Streaming project
    • Pull Twitter data and analyse the data

Important notes:

  • We asign regular work for
  • After training provide solution to that problem.
  • Minimum 3 months online support & Job Assistance
  • Training in Spark 2.x and spark 1.6.2 in Scala language
  • Excellent Materials all major spark and Scala books
  • Guide to get Cloudera/ MapR/ Databricks spark certification