Apache Spark Training

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Credo Systemz provides the Best Apache Spark Training in Chennai,Velachery. If you are worried about how to learn Apache Spark then Credo Systemz is the best option for you. Our Apache Spark course content starts from the basics of Scala which is required for Apache Spark and at the end of our Apache Spark training program in Chennai you will be working on a live Spark project.

Our Apache Spark Certification training program in Chennai is designed as 8 sections which are completely hands-on with live project training which will be helpful to enhance your career as a Certified Apache Spark Developer.

About Apache Spark Course

What is Apache Spark ?

Apache Spark is a cluster computing technology and mainly designed for fast computation. Spark Based on MapReduce and used to more type of computation process which includes more queries and stream processing. Spark is an memory cluster computing that increases processing speed of Hadoop Applications.

Benefits of Apache Spark
  • Apache Spark cluster computing technology has in memory feature.
  • Using Apache Spark you can get high data processing speed which is nearly about 100x & 10x faster for memory and disk.
  • Apache Spark provides 80 high-level operators which can be used develop parallel application easily.
  • The best part of Apache Spark is it supports multiple programming languages like Java, Scala, Python, R.
  • Spark is the best option when comparing with Hadoop with respect to cost, since in Hadoop large data center and storage is required to store the Big Data.

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Section 1: Introduction to Scala for Apache Spark

Learning Objectives – In this module, you will understand the basics of Scala that are required for programming Spark applications. You can learn about the basic constructs of Scala such as variable types, control structures, collections, and more.

  • What is Scala?
  • Why Scala for Spark?
  • Scala in other frameworks
  • Introduction to Scala REPL
  • Basic Scala operations
  • Variable types in Scala
  • Control Structures in Scala
  • Foreach loop
  • Functions, Procedures, Collections in Scala-Array
  • ArrayBuffer
  • Map, Tuples, Lists, and more.

Section 2: OOPS and Functional Programming in Scala

Learning Objectives – In this module, you will learn about object oriented programming and functional programming techniques in Scala.

  • Class in Scala
  • Getters and Setters
  • Custom Getters and Setters
  • Properties with only Getters
  • Auxiliary Constructor
  • Primary Constructor
  • Singletons
  • Companion Objects
  • Extending a Class
  • Overriding Methods
  • Traits as Interfaces
  • Layered Traits
  • Functional Programming
  • Higher Order Functions
  • Anonymous Functions and more.

Section3: Introduction to Big Data and Apache Spark

Learning Objectives – In this module, you will understand what is big data, challenges associated with it and the different frameworks available. The module also includes a first-hand introduction to Spark.

  • Introduction to big data
  • Challenges with big data
  • Batch Vs. Real Time big data analytics
  • Batch Analytics – Hadoop Ecosystem Overview
  • Real-time Analytics Options
  • Streaming Data- Spark
  • In-memory data- Spark
  • What is Spark?
  • Spark Ecosystem
  • Modes of Spark
  • Spark installation demo
  • Overview of Spark on a cluster  Spark Standalone cluster  Spark Web UI.

Section 4: Spark Common Operations

Learning Objectives – In this module, you will learn how to invoke Spark Shell and use it for various common operations.

  • Invoking Spark Shell
  • Creating the Spark Context
  • Loading a file in Shell
  • Performing basic Operations on files in Spark Shell
  • Overview of SBT
  • Building a Spark project with SBT
  • Running Spark project with SBT
  • Local mode
  • Spark mode
  • Caching overview
  • Distributed Persistence

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Section 5: Playing with RDDs

Learning Objectives – In this module, you will learn one of the fundamental building blocks of Spark – RDDs and related manipulations for implementing business logic.

  • RDDs
  • Transformations in RDD
  • Actions in RDD
  • Loading data in RDD
  • Saving data through RDD
  • Key-Value Pair RDD
  • MapReduce and Pair RDD Operations
  • Spark and Hadoop Integration-HDFS
  • Spark and Hadoop Integration-Yarn  Handling Sequence Files and Partitioner.

Section 6: Spark Streaming and MLlib

Learning Objectives – In this module, you will learn about the major APIs that Spark offers. You will get an opportunity to work on Spark streaming which makes it easy to build scalable fault-tolerant streaming applications, MLlib which is Spark’s machine learning library.

  • Spark Streaming Architecture
  • First Spark Streaming Program
  • Transformations in Spark Streaming
  • Fault tolerance in Spark Streaming
  • Checkpointing
  • Parallelism level
  • Machine learning with Spark
  • Data types
  • Algorithms– statistics
  • Classification and regression
  • Clustering
  • Collaborative filtering

Section 7: GraphX, SparkSQL and Performance Tuning in Spark

Learning Objectives – In this module, you will learn about Spark SQL that is used to process structured data with SQL queries, graph analysis with Spark, GraphX for graphs and graph-parallel computation. You will also0 get a chance to learn the various ways to optimize performance in Spark.

  • Analyze Hive and Spark SQL architecture
  • SQLContext in Spark SQL
  • Working with DataFrames
  • Implementing an example for Spark SQL
  • Integrating hive and Spark SQL
  • Support for JSON and Parquet File Formats
  • Implement data visualization in Spark
  • Loading of data
  • Hive queries through Spark
  • Testing tips in Scala
  • Performance tuning tips in Spark
  • Shared variables: Broadcast Variables
  • Shared Variables: Accumulators.

Section 8: A complete project on Apache Spark

Learning Objectives – In this module, you will get an opportunity to work on a live Spark project where you can implement the learnings from previous modules hands-on, and solve a real-time use case.Problem Statement: Design a system to replay the real time replay of transactions in HDFS using Spark.
Technologies used:

  • Spark Streaming
  • Kafka (for messaging)
  • HDFS (for storage)
  • Core Spark API (for aggregation)


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Related Trainings

Apache Spark Training in Credo Systemz – Reviews

Apache Spark Training with Placement Assistance  
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For spark I would personally suggest Credo Systemz which have the best trainers where other institutes trainers are not that experienced. He gives training in more practical form on several datasets and various possibilities which other institutes skip. The assignments he give and help he provides during the course of training is also a big plus for practicing. Also he gives training on AWS and conduct extra sessions on emerging technologies which is a bigplus for future.

Apache Spark Training Institute in Chennai  
Renuga Devi   
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I did completed my Spark training in Velachery branch,  absolute worth the money. As I’m a working professional I had timing constrains, but the sessions here are flexible and the trainers too are humble and down to earth. I will definitely suggest Credo as the best software training institute in Chennai.

Best Apache Spark Training in Chennai  
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Hi guys, I am Dilip completed my Spark training  in Feb batch, to be frank before joining here I searched many institutes to do my training in which I found these guys in Credo doing a professional job.  They arranged free demo session with the trainer that gave me much confidence in taking the session here. Overall complete professional Spark training in Chennai.

Spark Training in Chennai Velachery  
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Credo Systemz is the best institute for doing Spark training in Chennai.I did my Apache Spark Training in Credo Systemz. My trainer handled each and every topic with real time examples. In best Spark institute timings are adjustable based on my needs which i found very convenient.Thank you Credo Systemz for gave best Spark training..

Highly Recommended  
Hari Haran   
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Extremely talented, hard working trainer gave us many hands on tutorials. He has lots of experience in Spark, Hadoop, kafka, apache, AWS etc which helps a lot in understanding BigData concepts with real time examples and case studies. He is ready to help anytime and is a great mentor. I rank 5 out of 5 with no doubts.

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