• Marathahalli, Bengaluru
  • 080-41750541 - 7795029900
  • apsysieeeprojects@gmail.com
  • Mon - Sat: 9:30am - 7:00pm

Bigdata & Hadoop Course

Hadoop framework is the future of huge data storage requirements. It can be used to process all types of data. It is one of the hot technologies budding in the market.

Apsys Solutions is the leading technology training institute in Bengalore providing professional trainings which are designed by keeping in mind brightest career interests of fresh graduates who will be the tomorrows innovators and leaders of the IT Industry. Our primary focus is to provide the highly industry relevant, value-added, and quality training on the latest emerging technologies.

Course Highlights

  • Expert trainers with decades of Industry Experiance
  • Focus on handson practicals
  • Convinient Batch Timings
  • VMware dump for practice purpose
  • In-depth details about Exadata machine
  • Discussion on latest version boxes
  • Monitoring tools for Exadata
  • Patching Exadata machine

Course Takeaways

  • Handson Practical Knowledge
  • Certificate from Institute
  • Recommendation and Guidance for Books
  • 3 Months Guidance & Assistance for Placement
  • Free Subscription to Technology Email Alerts
  • Guidance for Resume Preparation
  • Guidance for Interview Prepation
  • Course Material

Course Structure & Syllabus

  • Introduction to Big Data
    • What is Big Data ?
    • Examples of Big Data
    • Reasons of Big Data Generation
    • Why Big Data deserves your attention
    • Use cases of Big Data
    • Different options of analyzing Big Data
  • Introduction to Hadoop
    • What is Hadoop
    • History of Hadoop
    • How Hadoop name was given
    • Problems with Traditional Large-Scale Systems and Need for Hadoop
    • Where Hadoop is being used
    • Understanding distributed systems and Hadoop
    • RDBMS and Hadoop
  • Starting Hadoop
    • Hadoop Architecture
    • Features of Hadoop
    • Hadoop Components- HDFS, Map Reduce
    • Anatomy of File write / read
    • Introduce other components of Hadoop ecosystem
  • HDFS
    • HDFS Commands
    • Single node hadoop cluster
    • Understanding hadoop configuration files
    • Overview Of Hadoop Distributed File System
      Name nodes, Data nodes, The Command-Line Interface
    • The building blocks of Hadoop
    • Running HDFS Commands
    • Web-based cluster UI-Name Node UI, Map Reduce UI
    • Hands-On Exercise: Using HDFS commands
  • Understanding Map Reduce
    • How Map Reduce Works
      Data flow in MapReduce, Map operation, Reduce operation
    • Input and Output Formats
    • Partitions
    • Combiners
    • Schedulers
    • MapReduce Program In JAVA using Eclipse
    • Counting words with Hadoop—Running program
    • Writing MapReduce Drivers, Mappers and Reducers in Java
    • Real-world "MapReduce" problems
      Hands-On Exercise: Writing a MapReduce Program and Running a MapReduce Job
    • Java WordCount Code Walkthrough
  • Hadoop Ecosystem
    • Hive
    • Sqoop
    • PIG
  • Extended Subjects on Hive
    • Installing Hive
    • Introduction to Apache Hive
    • Getting data into Hive
    • Hive’s architecture
    • Hive-HQL
    • Query execution
    • Programming Practices and projects in Hive
    • Troubleshooting
    • Hands-On Exercise: Hive Programming
  • Extended Subjects on Sqoop
    • Installing Sqoop
    • Configure Sqoop
    • Import RDBMS data to Hive using Sqoop
    • Export from to Hive to RDBMS using Sqoop
    • Hands-On Exercise: Import data from RDBMS to HDFS and Hive
    • Hands-On Exercise: Export data from HDFS/Hive to RDBM
  • Extended Subjects on PIG
    • Introduction to Apache Pig
    • Install Pig
    • Pig architecture
    • Pig Latin - Reading and writing data using Pig
    • Hands-On Exercise: Programming with pig, Load data, execute data processing statements
  • Advanced Topics YARN
    • Hadoop 2.0, namely, YARN, Name Node High Availability, HDFS Federation, support for Windows etc.
  • Real Time Project
    • Clear explanation of real time Project by taking real time data
    • Take the data from different source systems like text files, csv files, RDBMS
    • Loading the data in to Hadoop & do some analytics using Map Reduce, HIVE & PIG

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