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Online Hadoop Training

Big Data Hadoop refers to the large and complex set of data that are difficult to process using traditional processing systems It comprises of volume of data, velocity at which the data is created, and variety in the data.

Sources of big data include social networks, telecom and mobile services, healthcare and public systems (such as Aadhaar) and machine generated data. Stock exchanges like NYSE and BSE generates Terabytes of data every day. Social media sites like Facebook generate data that are approximately 500 times bigger than stock exchanges. ACLM Big Data Hadoop Training is designed for professionals from Indirapuram, Vaishali, Kaushambi, Vasundhara, Sahibabad, noida, Ghaziabad and delhi ncr region.

Big Data Hadoop : Benefits

  • It can Store & Distribute large data across hundred on inexpensive server which operate parallel
  • It provide cost effective solution for Business
  • It provide flexibility to access new data source
  • Fast & resilient to failure

 

Topics Covered

Understanding of Big Data & Hadoop
  • Big Data, Limitation and Solution for Existing Data Analytics Architecture, Hadoop, Hadoop Features, Hadoop Ecosystem,Hadoop 2.x core components, Hadoop Storage : HDFS, Hadoop Processing: MapReduce Framework, Anatomy of File Write and Read, Rack Awareness
Hadoop Architecture and HDFS
  • Hadoop 2.x Cluster Architecture – Federation and High Availability, A Typical Production Hadoop Cluster, Hadoop Cluster Modes, Common Hadoop Shell Commands, Hadoop 2.x Configuration Files, Password-Less SSH, MapReduce Job Execution, Data Loading Techniques: Hadoop Copy Commands, FLUME, SQOOP
Hadoop MapReduce Framework – I
  • MapReduce Use Cases, Traditional way Vs MapReduce way, Why MapReduce, Hadoop 2.x MapReduce Architecture, Hadoop 2.x MapReduce Components, YARN MR Application Execution Flow, YARN Workflow, Anatomy of MapReduce Program, Demo on MapReduce
Hadoop MapReduce Framework – II
  • Input Splits, Relation between Input Splits and HDFS Blocks, MapReduce Job Submission Flow, Demo of Input Splits, MapReduce: Combiner & Partitioner, Demo on de-identifying Health Care Data set, Demo on Weather Data set
Advance MapReduce
  • Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format
Pig
  • About Pig, MapReduce Vs Pig, Pig Use Cases, Programming Structure in Pig, Pig Running Modes, Pig components, Pig Execution, Pig Latin Program, Data Models in Pig, Pig Data Types, Pig Latin : Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Pig UDF, Pig Demo on Healthcare Data set.
 Hive
  • Hive Background, Hive Use Case, About Hive, Hive Vs Pig, Hive Architecture and Components, Metastore in Hive, Limitations of Hive, Comparison with Traditional Database, Hive Data Types and Data Models, Partitions and Buckets, Hive Tables(Managed Tables and External Tables), Importing Data, Querying Data, Managing Outputs, Hive Script, Hive UDF, Hive Demo on Healthcare Data set
 Advance Hive and HBase
  • Hive QL: Joining Tables, Dynamic Partitioning, Custom Map/Reduce Scripts, Hive : Thrift Server, User Defined Functions, HBase: Introduction to NoSQL Databases and HBase, HBase v/s RDBMS, HBase Components, HBase Architecture, HBase Cluster Deployment.
Advance HBase
  • HBase Data Model, HBase Shell, HBase Client API, Data Loading Techniques, ZooKeeper Data Model, Zookeeper Service, Zookeeper, Demos on Bulk Loading, Getting and Inserting Data, Filters in HBase.
Oozie and Hadoop Project
  • Flume and Sqoop Demo, Oozie, Oozie Components, Oozie Workflow, Scheduling with Oozie, Demo on Oozie Workflow, Oozie Co-ordinator, Oozie Commands, Oozie Web Console, Hadoop Project Demo

Who Should Attend

  • Analytics Professionals, BI /ETL/DW Professionals , Project Managers, Testing Professionals, Mainframe Professionals , Software Developers and Architects , Graduates aiming to build a career in Big Data, Technical/Blog Writers

Pre-requisites

  • Core Java – For Hadoop Dev Profile, SQL – For Data Analyst/Reporting Profile

What You Need To Bring

  • Notepad, Pen/Pencil, Laptop

Key Takeaways

  • Master the concepts of HDFS and MapReduce framework, Understand Hadoop 2.x Architecture, Setup Hadoop Cluster and write Complex MapReduce programs, Learn data loading techniques using Sqoop and Flume, Perform data analytics using Pig, Hive and YARN, Implement HBase and MapReduce integration, Implement Advanced Usage and Indexing, Schedule jobs using Oozie, Implement best practices for Hadoop development, Work on a real life Project on Big Data Analytics

About Trainer

  • Having good knowledge on Big Data and Hadoop with its eco-system components (Pig, Hive, Advance Hive & HBase, Advance HBase, Sqoop)

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