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Hadoop deals with vast sums of data in multiple applications. It also supports deep analytics.

Hadoop technology is used by almost every industry to manage large volumes of data rapidly. It deals with structured and unstructured data, which is why many organizations adopt Hadoop.

We provide the best Hadoop Data Science Training in Chennai, where one can get in-depth knowledge in Data Science, Big data analytics, Big Data Platforms And Storage Options, Big Data Integration And Governance In A Multi-Platform Analytical Environment, Tools And Techniques For Analyzing Big Data and Integrating Big Data Analytics Into The Enterprise.

We, at Vikapri Training, offer one-on-one training sessions by experts. Get training on your flexible timings and hands-on live projects. Start your career in Big Data with professional certification.


  • What is Big Data?
  • Types of big data
  • Why analyse Big Data?
  • The need to analyse new more complex data sources
  • Industry use cases - Popular big data analytic applications
  • What is Data Science?
  • Data Warehousing and BI Versus Big Data
  • Popular patterns for Big Data technologies
  • Types of Big Data analytical workloads
  • Streaming data analytics at high velocity
  • Exploratory analysis of multi-structured data
  • Complex analysis of structured data
  • Graph analytics
  • Challenges when managing and analysing big data
  • Key components in a Big Data Analytics environment
  • Preserving existing BI/DW investments
  • The Big Data Extended Analytical Ecosystem
  • The new multi-platform analytical ecosystem
  • Beyond the data warehouse - Hadoop NoSQL and analytical RDBMSs, NewSQL DBMSs
  • Key Value stores, Document DBMSs, Column Family DBMSs and Graph databases
  • An introduction to Hadoop and the Hadoop Stack
  • HDFS, MapReduce, Pig & Hive
  • Hadoop 2.0 Spark Framework
  • SQL on Hadoop options
  • The Big Data Marketplace
  • Hadoop distributions – Cloudera, Hortonworks, MapR, IBM BigInsights, Microsoft HD Insight, PivotalHD
  • Big Data Appliances – Oracle Big Data Appliance, IBM PureData System for Hadoop, HP HAVeN, Teradata Aster Discovery Server,
  • NoSQL databases, e.g. Datastax, Neo4J, Yarcdata, MongoDB, Riak
  • Analytical databases and DW appliances, e.g. Teradata, Exasol, IBM PureData, Oracle Exadata, SAP HANA, Kognitio, Actian ParAccel
  • Analytical appliances – SAS LASR, MicroStrategy PRIME
  • The Cloud deployment option – Microsoft Windows Azure, IBM, Amazon Elastic MapReduce, Altiscale Data Cloud
  • Creating a multi-platform analytical ecosystem
  • Types of Big Data
  • Connecting to Big Data sources, e.g. web logs, clickstream, sensor data, and multi-structured content
  • Supplying consistent data to multiple analytical platforms
  • Loading Big Data – what’s different about loading HDFS, Hive & NoSQL Vs analytical relational databases
  • Change data capture – what’s possible
  • Data warehouse offload
  • Tools for ELT processing on Hadoop – The Enterprise Data Refinery
  • ETL tools Vs Pig Vs self-service DI/DQ
  • Dealing with data quality in a Big Data environment
  • Parsing unstructured data
  • Governing data in a Data Science environment
  • Joined up analytical processing from ETL to analytical workflows
  • The impact of data scientist and end user self-service DQ/DI – Paxata, Trifacta, MS Excel, MicroStrategy
  • Mapping discovered data of value into your DW and business vocabulary
  • Big data audit, protection and security – Dataguise, IBM Guardium, Protegrity
  • Data Science projects
  • Creating Sandboxes for Data Science projects
  • Options for analysing unstructured content – Text analytics, custom MapReduce code and MapReduce developer tools
  • Using R as an analytical language for Big Data
  • Text analysis and visualisation, Sentiment analysis and visualisation
  • Clickstream analysis and visualisation
  • Analysing big data using MapReduce BI Tools and applications for Hadoop, e.g. Datameer, Karmasphere, Platfora, IBM Customer Insight
  • Exploratory graph analysis and visualisations
  • Using search to analyse multi-structured data
  • Creating search indexes on multi-structured data
  • Building dashboards and reports on top of search engine indexed content
  • The integration of search with traditional BI platforms
  • Guided analysis using multi-faceted search
  • The marketplace: Apache Solr, Attivio, Cloudera Search, Connexica, DataRPM, HP IDOL, IBI WebFocus Magnify, IBM Watson Explorer, LucidWorks, Microsoft, Oracle Endeca Quid, Splunk
  • Analysing Big Data using Self-Service BI Tools, e.g. Tableau, QlikView, Spotfire, SAS Visual Analytics, MicroStrategy, SAP Lumira
  • SQL connectivity initiatives to Big Data – e.g. Impala, Hive, Stinger, Shark on Spark, HawQ, IBM BigSQL, CitusDB JethroData, Splice Machine
  • Big data analytics – query performance enablers
  • Managing stream computing in a Big Data environment
  • Tools and techniques for streaming analytics
  • Integrating Big Data platforms with traditional DW/BI environments – what’s involved
  • Integrating stream processing with Hadoop and Analytical DW Appliances
  • Integrating Hadoop with DW Appliances and Enterprise Data Warehouses
  • Tying together front end tools
  • Options for implementing multi-platform analytics
  • Cross-platform analytical workflows
  • The role of Data Virtualisation in a Big Data environment
  • Multi-platform optimisation



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