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Future of Data : Big Data   Shankar Radhakrishnan        Cognizant
Topics How did we get here ? Data Explosion Big Data Big Data in an Enterprise Big Data Platform - Hadoop Big Data A...
How did we get here?Familiar World                                           Data Integration Problems   EDW   Datamart...
Data Explosion
Newer Interests Social Intelligence   DBIM, Sentiment Analysis, Social Customer Care Predictive Analytics   Propensity...
Categories Structured Data  Enterprise Data (CRM, ERP, Data Stores, Reference Data) Semi-structured Data  Machine Gene...
Big Data                                         Volume                      Complexity                                   ...
Big Data Platforms• Data Integration   o Informatica, Infosphere   o talenD, Pentaho, Karmasphere, Apache Sqoop, Apache Fl...
Big Data in an Enterprise Big Data            Big Data            ETL Sources             Platform                        ...
Hadoop - Ecosystem
Big Data : Adoption Drivers                   Cluster         Distributed    Platform          Storage      Scalable      ...
Big Data – Adoption Scenarios Replatforming to Big Data (Hadoop, MapR) Archival Solution (Hadoop) Offloading Data wareh...
Q&A
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Future of Data - Big Data

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Transcript of "Future of Data - Big Data"

  1. 1. Future of Data : Big Data Shankar Radhakrishnan Cognizant
  2. 2. Topics How did we get here ? Data Explosion Big Data Big Data in an Enterprise Big Data Platform - Hadoop Big Data AdoptionQ&A
  3. 3. How did we get 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
  4. 4. Data Explosion
  5. 5. Newer Interests Social Intelligence  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
  6. 6. 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
  7. 7. Big Data Volume Complexity Big Velocity Data Variety“Big Data” refers to high volume, velocity, variety and complex information assets thatdemand cost-effective, innovative forms of information processing for enhanced insightand decision making
  8. 8. 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
  9. 9. Big Data in an Enterprise Big Data Big Data ETL Sources Platform Datamarts ETL Analytical Datamarts Applications Datamarts Data ETL Data warehouse Sources
  10. 10. Hadoop - Ecosystem
  11. 11. Big Data : Adoption Drivers Cluster Distributed Platform Storage Scalable Process Availability Performance Data Augmented Integration Data Possibilities Processing TCO Ecosystem Actionable ROI Insights
  12. 12. 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)
  13. 13. Q&A
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