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Course Features

  • Lectures 30
  • Quizzes 0
  • Duration 15 day
  • Skill level Mid Level
  • Language English
  • Students 5
  • Assessments Yes
Yuvaraj Karuppusamy

Academic Instructor

Categorires

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Course Details

Apache Spark Framework training in Coimbatore provided by the best training institute in Coimbatore (Techi4s Training Solutions Pvt Ltd) with 100% real-time,practical and placement.Apache Spark Framework Training in Coimbatore provides by Techi4s with real-time working professional which will help students and trainees to get trained in practical real-time scenarios along with theory.This training will definitely help you to complete certification and clearing interviews.Our Apache Spark Framework training focuses on giving students basic to advanced level.Our team of trainers are Technical Architects and Project Leads working in MNC's and will help in real time projects.Best Apache Spark Framework Training in Coimbatore provides By Techi4s in flexible timings.We also provide fast track,online and corporate training.Apache Spark Framework Syllabus covered full of practical examples which will help students and trainees to land up in jobs.Apache Spark Framework Project Structure,Apache Spark Framework Application Components etc with live examples..

Our training Institute is equipped with perfect environment with all required facilites and course fee is less compared to other training institutes.Our Apache Spark Framework training will be scheuled on Regular Weekdays and Weekends based on students requirements

PREREQUISITES

  • Knowledge of Scala Language is essential.

Benefits to learn Apache Spark Framework

  • Swift Processing Using Apache Spark, we achieve a high data processing speed of about 100x faster in memory and 10x faster on the disk. This is made possible by reducing the number of read-write to disk.
  • Dynamic in Nature We can easily develop a parallel application, as Spark provides 80 high-level operators.
  • In-Memory Computation in Spark With in-memory processing, we can increase the processing speed. Here the data is being cached so we need not fetch data from the disk every time thus the time is saved. Spark has DAG execution engine which facilitates in-memory computation and acyclic data flow resulting in high speed.
  • Reusability we can reuse the Spark code for batch-processing, join stream against historical data or run ad-hoc queries on stream state.
  • Fault Tolerance in Spark Apache Spark provides fault tolerance through Spark abstraction-RDD. Spark RDDs are designed to handle the failure of any worker node in the cluster. Thus, it ensures that the loss of data reduces to zero. Learn different ways to create RDD inApache Spark.
  • Real-Time Stream Processing Spark has a provision for real-time stream processing. Earlier the problem with Hadoop MapReducewas that it can handle and process data which is already present, but not the real-time data. but with Spark Streamingwe can solve this problem.
  • Lazy Evaluation in Apache Spark All the transformations we make in Spark RDD are Lazy in nature, that is it does not give the result right away rather a new RDD is formed from the existing one. Thus, this increases the efficiency of the system. Follow this guide to learn more about Spark Lazy Evaluation in great detail.
  • Support Multiple Languages In Spark, there is Support for multiple languages like Java, R, Scala, Python. Thus, it provides dynamicity and overcomes the limitation of Hadoop that it can build applications only in Java.
  • Active, Progressive and Expanding Spark Community Developers from over 50 companies were involved in making of Apache Spark. This project was initiated in the year 2009 and is still expanding and now there are about 250 developers who contributed to its expansion. It is the most important project of Apache Community.
  • Support for Sophisticated Analysis Spark comes with dedicated tools for streaming data, interactive/declarative queries, machine learning which add-on to map and reduce.
  • Integrated with Hadoop Spark can run independently and also on Hadoop YARN Cluster Manager and thus it can read existing Hadoop data. Thus, Spark is flexible.
  • Spark GraphX Spark has GraphX, which is a component for graph and graph-parallel computation. It simplifies the graph analytics tasks by the collection of graph algorithm and builders.
  • Cost Efficient Apache Spark is cost effective solution for Big dataproblem as in Hadoop large amount of storage and the large data center is required during replication.

Academic Instructor

Yuvaraj Karuppusamy Academic Instructor

He is the Professor of the Department of Computer Applications at Gobi Arts & Science College where he has been since 2013. He also currently serves as Creative Head of Techi4s Soft Solutions. During 2013-2018 he was worked as Developer,Trainer and etc.,

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