Best Hadoop Data Science Training in Chennai-VikapriTraining

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Curriculum

Course Details

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.

Lessons 1: AN INTRODUCTION TO BIG DATA

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

Lessons 2: AN INTRODUCTION TO BIG DATA ANALYTICS

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

Lessons 3: BIG DATA PLATFORMS AND STORAGE OPTIONS

The new multi-platform analytical ecosystem
Beyond the data warehouse - Hadoop NoSQL and analytical RDBMSs, NewSQL DBMSs
NoSQL 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

Lessons 4: BIG DATA INTEGRATION AND GOVERNANCE IN A MULTI- PLATFORM ANALYTICAL ENVIRONMENT

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

Lessons 5: TOOLS AND TECHNIQUES FOR ANALYSING BIG DATA

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

Lessons 6: INTEGRATING BIG DATA ANALYTICS INTO THE ENTERPRISE

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