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IEVISION BHANDARKAR ROAD :

Apache Spark

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Module 1: Introduction to Spark

Introduction to Spark

How Spark overcomes the drawbacks of working MapReduce 

Understanding in-memory MapReduce

Interactive operations on MapReduce 

Spark stack

Fine vs. coarse grained update 

Spark stack, Spark Hadoop YARN 

HDFS Revision 

YARN Revision

The overview of Spark and how it is better Hadoop 

Deploying Spark without Hadoop

Spark history server 

Cloudera distribution

Module 2: Spark Basics

Spark installation guide

Spark configuration 

Memory management 

Executor memory vs. driver memory 

Working with Spark Shell 

Concept of Resilient Distributed Datasets (RDD)

Learning to do functional programming in Spark 

Architecture of Spark

Module 3: Working with RDDs in Spark

Spark RDD 

Creating RDD 

RDD partitioning

Operations & transformation in RDD

Deep dive into Spark RDDs 

RDD general operations 

A read-only partitioned collection of records

Using the concept of RDD for faster and efficient data processing

RDD action for Collect,Count,Collectsmap ,Save as text files

Pair RDD functions

Module 4: Aggregating Data with Pair RDDs

Understanding the concept of Key-Value pair in RDDs 

Learning how Spark makes MapReduce operations faster 

Various operations of RDD

MapReduce interactive operations 

Fine & coarse grained update 

Spark stack

Module 5: Writing and Deploying Spark Applications

Comparing the Spark applications with Spark Shell

Creating a Spark application using Scala or Java

Deploying a Spark application

Scala built application

Creation of mutable list 

Set & set operations 

List 

Tuple

Concatenating list 

Creating application using SBT

Deploying application using Maven

The web user interface of Spark application

A real world example of Spark and configuring of Spark

Module 6: Parallel Processing

Learning about Spark parallel processing

Deploying on a cluster

Introduction to Spark partitions 

File-based partitioning of RDDs

Understanding of HDFS and data locality

Mastering the technique of parallel operations

Comparing repartition & coalesce

RDD actions

Module 7: Spark RDD Persistence

The execution flow in Spark

Understanding the RDD persistence overview

Spark execution flow & Spark terminology

Distribution shared memory vs. RDD 

RDD limitations 

Spark shell arguments

Distributed persistence

RDD lineage

Key/Value pair for sorting implicit conversion like Count By Key, Reduce By Key, Sort By Key, Aggregate By Key

Module 8: Spark Streaming & Mlib

Spark Streaming Architecture 

Writing streaming program coding

Processing of spark stream

Processing Spark Discretized Stream (DStream)

The context of Spark Streaming

Streaming transformation

Flume Spark streaming 

Request count and Dstream 

Multi batch operation 

Sliding window operations and advanced data sources 

Different Algorithms

The concept of iterative algorithm in Spark

Analyzing with Spark graph processing

Introduction to K-Means and machine learning

Various variables in Spark like shared variables 

Broadcast variables 

Learning about accumulators

Module 9: Improving Spark Performance

Introduction to various variables in Spark like shared variables

Broadcast variables 

Learning about accumulators

The common performance issues and troubleshooting the performance problems

Module 10: Spark SQL and Data Frames

Learning about Spark SQL 

The context of SQL in Spark for providing structured data processing

JSON support in Spark SQL 

Working with XML data 

Parquet files 

Creating HiveContext 

Writing Data Frame to Hive

Reading JDBC files, understanding the Data Frames in Spark 

Creating Data Frames

Manual inferring of schema

Working with CSV files

Reading JDBC tables 

Data Frame to JDBC

User defined functions in Spark SQL

Shared variable and accumulators

Learning to query and transform data in Data Frames

How Data Frame provides the benefit of both Spark RDD and Spark SQL

Deploying Hive on Spark as the execution engine

Module 11: Scheduling/ Partitioning

Learning about the scheduling and partitioning in Spark

Hash partition

Range partition

Scheduling within and around applications

Static partitioning

Dynamic sharing

Fair scheduling

Map partition with index 

The Zip

Group By Key

Spark master high availability 

Standby Masters with Zookeeper

Single Node Recovery with Local File System, High Order Functions





Industry Expert and working professional Trainers
90% Practical Oriented Training
Live Project Experience
Latest Courseware
Professional Resume Building for freshers
Special Focus on communication skills enhancement
Excellent Training Facility with Lab Room
Professionally Trained Support Staff
Dedicated HR - Recruitment Team
200+ Corporate Tieup
100% Placement Assistance

We Make Fresher, industry-ready software professionals
Hands on Project Experience exposures in the Lab session
Real Time case studies to practice
Free Technical Support after Course Completion
Back up Classes Available
LAB Facility 
Free Wi-Fi to learn subject
Latest Study Material
Fast Track and Normal Batches available

REVIEWS



Shahid Khan at

A great Training  aimed at beginners to learn Apache Spark.Faculty is very Experienced.Thank you IEVISION!

Rohan Dobriv at

This is very organized and detailed course which enable any one with/ without programming skills to understand the SPARK concepts and transformations.

Shashank Desai at

This Training will kickstart your Spark Journey. Good Training. Thank you IEVISION Team.

Akash Juthani at

Just finished the course and found it to be quite insightful and well-structured. There are plenty of hands-on exercises.Thank You IEVISION!

Nina Joseph at

The course was great and it provides a good base to proceed further in Spark complex development.

FREQUENTLY ASKED QUESTIONS

Yes, We provide 100% Job Assistance to all IEVISION students. 
A dedicated HR – Recruitment members are designated to assist you in preparing your professional resume building, guiding you on HR Interview Process, Sending your resumes to corporates and assist you till you get placed.
Since last 6 years, IEVISION Students are placed in many countries and most of the MNC companies in India.

After course completion, Participation Certificate will be awarded.  We do also support in getting the Global Certification, Please connect with our support staff and you will be assisted.

We are operating from central location in Pune. Visit Contact us www.ievision.in  
IEVISION Representative will be happy to assist you. +919604642000 & +919604647000 or email us at info@ievision.in

Yes, we do provide demonstration sessions for all Technology Courses. Demo lectures are delivered by real trainers who have years of Industry Experience. You can clear all your doubts about the training course, courseware, training approach, live projects, job placement, fees, installments and over all association.
IEVISION Trainers are working professionals, highly experienced, certified on various levels on particular technologies with hands-on industry experience. Trainers are motivated to build the strong technical capability of students to achieve their objectives in life.
Yes, IEVISION provide 100% Practical Oriented training and students will be working on minimum 2 live projects.
Batch size is kept limited for effective delivery of training program. Based on training program, 5 - 15 students are adjusted in a batch.
We do provide batch change option. Please contact our support staff for more information about upcoming batches.
Yes, IEVISION provide the latest courseware in the form on Hardcopy, PDF and PPTs. 
IEVISION facility is fully equipped with required Hardware & Software. You are allowed to use your laptop & required software shall be assisted.

If you miss any session, you can attend classes in any other running batch or next upcoming batch. Please contact our Counsellor for more information about running batches

Yes, 5% discount is provided for Lump sum payment.

Yes, IEVISION provide installment facility based.
IEVISION accept payment through various mode Online Trasfer, Cheque, Cash, Credit Card, Debit Card and Demand Draft.