011 4907 2531 1/56 D,, Lalita Park, Laxmi Nagar, New Delhi, Delhi 110092

Big Data & Hadoop

Big Data is so much popular these days, and it is estimated that over half of the data in this world will move to Hadoop in next 3 years. So that explains the demand and need for Big Data Professionals. NI Analytics India offers interactive trending technology Big Data Hadoop analytics training with an objective to disrupt big data skills.

In this training attendees will gain practical skill set on Hadoop in detail, including its core and latest components, like HDFS, MapReduce, HBase, Sqoop, Flume, Oozie, Zoopkeeper, Spark and Storm . For extensive hands-on practice, candidates will get access to the virtual lab and several assignments and Big Data case studies. At end of the program candidates are awarded a certificate on successful completion of projects that are provided as part of the training.

  • Overview of Big Data Technologies and its role in Analytics
  • Big Data challenges & solutions
  • Data Science vs Data Engineering
  • FOUR V’s of Big Data given by Google.
  • Introduction to UNIX shell.
  • Basic Commands of UNIX
  • Create
  • Copy
  • Move
  • Delete etc.
  • Basic of JAVA Programming Language
  • Architecture JVM, JRE, JIT
  • Control Structures

  • OOP’s Concept in Java

  • String Classes/Array/Exception Handling

  • Collection Classes

  • Understanding the problem statement and challenges persisting to such large data to perceive the need of Distributed File System.
  • Understanding HDFS architecture to solve problems
  • Understanding configuration and creating directory structure to get a solution of the given problem statement
  • Setup appropriate permissions to secure data for appropriate users
  • Setting up Java Development with HDFS libraries to use HDFS Java APIs
  • What is Map Reduce.
  • Input and output formats.
  • Data Types in Map Reduce.
  • Flow of Map Reduce Jobs.
  • Wordcount In Map Reduce.
  • How to use Custom Input Formats
  • Use case for Structure Data Sets.
  • Writing Custom Classes.
  • What is HIVE.
  • Architecture of HIVE.
  • Tables in Hive with Load Functions.
  • Query Optimization.
  • Partitioning and Bucketing.
  • Joins in HIVE.
  • Indexing In HIVE.
  • File Formats in HIVE.
  • How to read JSON files in HIVE.
  • What is Sqoop.
  • Relation between SQL & Hadoop.
  • Performing Sqoop Import.
  • Incrementals and Conditional Imports
  • Performing Sqoop Export.
  • What is PIG & ETL.
  • Introduction to PIG Architecture.
  • Introduction of PIG Latin.
  • How to Perform ETL on any Kind of data(PIG Eats Everything)
  • Use cases of PIG.
  • Joins in PIG.
  • Co-grouping In PIG
  • What is HBASE.
  • Architecture of HBASE.
  • CRUD operations in HBASE
  • Retrival of HBASE Data.
  • Introduction of Apache Oozie (Scheduler tool)
  • Basic data types and literals used
  • List the operators and methods used in Scala
  • Classes of Scala
  • Traits of Scala.
  • Control Structures in Scala.
  • Collection of Scala.
  • Libraries of Scala.
  • Limitations of MapReduce in Hadoop Objectives
  • Batch vs. Real-time analytics
  • Application of stream processing
  • Spark vs. Hadoop Eco-system
  • Limitations of MapReduce in Hadoop Objectives
  • Batch vs. Real-time analytics
  • Application of stream processing
  • Spark vs. Hadoop Eco-system
  • Explain the importance and features of SparkQL
  • Describe methods to convert RDDs to
  • DataFrames
  • Explain concepts of SparkSQL
  • Describe the concept of hive integration
  • Explain the use cases and techniques of
  • Machine Learning (ML)
  • Describe the key concepts of Spark ML
  • Explain the concept of an ML Dataset, and ML
  • algorithm, model selection via cross validation



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