Future of Data - Big Data

Future of Data - Big Data

• 1.
  Future of Data: Big Data Shankar Radhakrishnan Cognizant
• 2.
  Topics  How didwe get here ?  Data Explosion  Big Data  Big Data in an Enterprise  Big Data Platform - Hadoop  Big Data Adoption Q&A
• 3.
  How did weget here? Familiar World Data Integration Problems  EDW  Datamarts Data Processing Problems  Familiar Problems Data warehouse Storage Management Performance Problems Limitations out of Complexity New World  Newer type of data to integrate  Increase in volume  Newer analytical requirements
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  Data Explosion
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  Newer Interests  SocialIntelligence  DBIM, Sentiment Analysis, Social Customer Care  Predictive Analytics  Propensity, Price Elasticity, Anti-Fraud Analytics  Segmentation Insights  Funnel Analysis, Behavioral Patterns, Cohort Analysis  Mobile Analytics  Ad-Targeting, Geo-spatial Analytics
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  Categories  Structured Data  Enterprise Data (CRM, ERP, Data Stores, Reference Data)  Semi-structured Data  Machine Generated Data (Sensor Data, RFIDs)  Unstructured Data  Social Data (Comments, Tweets), Blog posts
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  Big Data Volume Complexity Big Velocity Data Variety “Big Data” refers to high volume, velocity, variety and complex information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making
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  Big Data Platforms •Data Integration o Informatica, Infosphere o talenD, Pentaho, Karmasphere, Apache Sqoop, Apache Flume • Database Framework o Hadoop (Distributions: Cloudera, Hortonworks, MapR) o Hbase o Hive • NoSQL Databases o MongoDB, CouchDB • Machine Data Processing o Splunk, Mahout • Text Analytics o Clarabridge, Lexanalytics
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  Big Data inan Enterprise Big Data Big Data ETL Sources Platform Datamarts ETL Analytical Datamarts Applications Datamarts Data ETL Data warehouse Sources
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  Hadoop - Ecosystem
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  Big Data :Adoption Drivers Cluster Distributed Platform Storage Scalable Process Availability Performance Data Augmented Integration Data Possibilities Processing TCO Ecosystem Actionable ROI Insights
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  Big Data –Adoption Scenarios  Replatforming to Big Data (Hadoop, MapR)  Archival Solution (Hadoop)  Offloading Data warehouse, EDW (Hadoop, Hive)  Social Media Integration  Machine Data Analysis (Splunk, Mahout)  Complex Analytical Requirements (Hbase)
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  Q&A