BIG DATA-SPARK

big data training in chennai with placement

big data training in chennai with placement

Our Hadoop & Spark course includes basic to advanced level and our spark course is designed to get the placement in good MNC companies in chennai as quickly as once you complete the Spark certification training course.
In a few ways, Big data Spark is like a fine wine: It shows signs of improvement with age as unpleasant edges (or flavor profiles) are smoothed out, and the individuals who hold up to devour it will presumably have a superior affair. Our Spark training chennai review is given as positive by industry experts.

Spark Administration preparing for System Administrators is intended for specialized operations work force whose employment is to introduce and keep up generation Spark groups in true. Most of the peoples looking for Spark admin training in Chennai and this is the right place for the person to learn Big data Spark.

We have designed our Spark course content and syllabus based on students requirement to achieve everyone’s career goal. In our Spark training program, you will learn Spark Course Content, Use case walkthrough, Scala Introduction,Spark Architecture, spark sql Introduction, spark sql Schema Design,spark sql Operations, cluster management, Introduction to spark,Spark in the Enterprise, Architecture, Hadoop CLI, MapReduce Programming, MapReduce Formats, Hadoop File Formats, MapReduce Design Considerations, MapReduce Algorithms, MapReduce Features, Use Case A (Long Exercise), MapReduce Testing, Hadoop Ecosystem,MapReduce Performance Tuning, Development Best Practice and Debugging, Apache Hadoop for Administrators, Hadoop Fundamentals and Architecture, Spark Ecosystems Overview, Hardware and Software requirements, Deploy Hadoop ecosystem services, Enable Security – Configure Users, Groups, Secure HDFS, MapReduce, cassandra and Hive, Manage and Monitor your cluster, Command Line Interface, Troubleshooting your cluster, Introduction to Big Data and Spark, Hadoop Overview, Apache Dataframe & Dataset Api for Developers, Overview of Spark, Spark Streaming Introduction, Spark Architecture – Building Blocks, Hive Usecase implementation – (Exercise), Advance Features, Introduction, Pig Latin Programming, Use Cases (working exercise), Advanced Features, UDFs, Best Practices and common pitfalls, Classification, Evaluation (Hands-on exercise), Clustering, Recommendation Systems.

Target Audience:
Students with a background of BE / B.Tech in CSE /IT/ ECE /EEE/E&I/IC/ Mechatronics/Msc Electronics and any other relevant streams.

Software Training is suitable for:

Software Training in Bigdata is suitable for engineering students who are from computers or electronics,mechanical domain can find an opportunity in Software Systems Development Industries. There is a growing demand for Software Engineers , Data Scientists in the Industry.

Course Goal:
Students will become an Industry-ready Software engineer by completing Software Training / Diploma in Advanced Software Technology certified course.

SHORT TERM CERTIFIED COURSE IN BIGDATA USING APACHE SPARK

Duration :  1 Month(3hrs/day)     Total : 60 Hours
Working Days : Monday to Saturday ( 10.00 AM to 3.00 PM ) 

Module 1: INTRODUCTION TO BIGDATA

  • Introduction and relevance
  • What is Bigdata ?
  • Characteristics of BigData
  • Big Data analtics in various industries like Telecom,E-Commerce,Finance etc
  • Problems with Traditional Large-Scale Systems
  • BigData Challenges
Module 2: HADOOP (BIG DATA) ECOSYSTEM
  • Motivation for Hadoop
  • Different types of projects by Apache
  • Role of projects in the Hadoop Ecosystem
  • Advantages of Hadoop
  • Limitations and Solutions of existing Data Analytics Architecture
  • Comparison of traditional data management systems with Big Data management systems
  • Hadoop Ecosystem & Hadoop 2.x core components
Module 3: SCALA BASICS
  • Functional Languages
  • Scala Vs Java
  • Strings,Numbers
  • Lists,Set,Map,Arrays
  • Control Statements,Collections
  • Functions,Methods
  • Pattern matching
Module 4: INTRODUCTION TO SPARK – GETTING STARTED
  • What is Spark and what is its purpose?
  • Components of the Spark unified stack
  • Resilient Distributed Dataset (RDD)
  • Downloading and installing Spark standalone
  • Scala and Python overview
  • Launching and using Spark’s Scala

Module 5: RESILIENT DISTRIBUTED DATASET AND DATAFRAMES

  • Understand how to create parallelized collections and external datasets
  • Work with Resilient Distributed Dataset (RDD) operations
  • Utilize shared variables and key-value pairs

Module 6: SPARK APPLICATION PROGRAMMING

  • Understand the purpose and usage of the SparkContext
  • Initialize Spark with the various programming languages
  • Describe and run some Spark examples
  • Pass functions to Spark
  • Create and run a Spark standalone application
  • Submit applications to the cluster

Module 7: SPARK CONFIGURATION, MONITORING AND TUNING

  • Understand components of the Spark cluster
  • Configure Spark to modify the Spark properties, environmental variables, or logging properties
  • Monitor Spark using the web UIs, metrics, and external instrumentation
  • Understand performance tuning considerations

Module 8: FINAL PROJECT

  • Real World Use Case Scenarios
  • Understand the implementation of Hadoop and Spark in Real World and its benefits.
  • Final project including integration various key components
  • Follow-up session: Tips and tricks for projects

CERTIFIED COURSE IN ADVANCED BIGDATA USING APACHE SPARK

Duration :  3 Month ( 3hrs/day )     Total : 180 Hours
Working Days : Monday to Saturday ( 10.00 AM to 3.00 PM  )

Module 1: INTRODUCTION TO BIGDATA

  • Introduction and relevance
  • What is Big data ?
  • Characteristics of Big Data
  • Big Data analytics in various industries like Telecom,E-commerce,Finance etc
  • Problems with Traditional Large-Scale Systems
  • Big Data Challenges
Module 2: HADOOP (BIG DATA) ECOSYSTEM
  • Motivation for Hadoop
  • Different types of projects by Apache
  • Role of projects in the Hadoop Ecosystem
  • Advantages of Hadoop
  • Limitations and Solutions of existing Data Analytics Architecture
  • Comparison of traditional data management systems with Big Data management systems
  • Hadoop Ecosystem & Hadoop 2.x core components
Module 3: OVERVIEW OF BIGDATA AND SPARK
  • MapReduce limitations
  • Spark History
  • Spark Architecture
  • Spark and Hadoop Advantages
  • Benefits of Spark + Hadoop
  • Introduction to Spark Eco-system
  • Spark Installation
Module 4: INTRODUCTION TO SCALA
  • Scala foundation
  • Features of Scala
  • Setup Spark and Scala on Unbuntu and Windows OS
  • Install IDE’s for Scala
  • Run Scala Codes on Scala Shell
  • Understanding Data types in Scala
  • Implementing Lazy Values
  • Control Structures
  • Looping Structures
  • Functions
  • Procedures
  • Collections
  • Arrays and Array Buffers
  • Map’s, Tuples and Lists
Module 6: 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 7: SPARK BASICS
  • Spark installation guide, Spark configuration
  • Memory management, executor memory vs. driver memory
  • Working with Spark Shell
  • The concept of Resilient Distributed Datasets (RDD)
  • Learning to do functional programming in Spark, the architecture of Spark
Module 8: WORKING WITH RDDS IN SPARK
  • Spark RDD, creating RDDs, RDD partitioning
  • Operations & transformation in RDD
  • Deep dive into Spark RDDs
  • The 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, Collects map, Save as textiles, pair RDD functions
Module 9: SPARK SQL AND DATA FRAMES
  • Learning about Spark SQL, the context of SQL in Spark for providing a structured data processing
  • JSON support in Spark SQL, working with XML data, parquet files
  • Creating Hive Context, writing Data Frame to Hive, reading JDBC files
  • Understanding the Data Frames in Spark
  • Creating Data Frames, manual inferring of the 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 10: SPARK DATASET API
  • Power of Dataset API in spark 2.0
  • Serialization concept in Dataset
  • Creating Dataset API
  • Processing CSV,JSON,XML,Text data
  • Dataset Operations
Module 11: SPARK JOB EXECUTION
  • Jobs,Stages and Tasks
  • Partitions and Shuffles
  • Broad Cost variables and Accumulators
  • Job Perfomence
Module 11: SPARK STREAMING
  • Need for Kafka
  • What is Kafka?
  • Core Concepts of Kafka
  • Kafka Architecture
  • Where is Kafka Used?
  • Memory Management
  • Spark With Cassandra integration
Module 12: FINAL PROJECT
  • Real World Use Case Scenarios
  • Understand the implementation of Hadoop in Real World and its benefits.
  • Final project including integration various key components
  • Follow-up session: Tips and tricks for projects