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5 reasons to Augment Your Data Warehouse with Hadoop Webinar


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This was a joint webinar with Hadoop solutions provider Orzota, Inc. that explained 5 key reasons to augment existing Data Warehouses with Hadoop. It highlighted a real world case study of a Fortune 100 company that led a pioneering effort at augmenting its Teradata based Enterprise Data Warehouse (EDW) with Hadoop.

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5 reasons to Augment Your Data Warehouse with Hadoop Webinar

  1. 1. 5 Reasons to Augment Your Enterprise Data Warehouse with Hadoop Date: 1st October 2015 Presenters: Nirav Shah Marketing Head CIGNEX Datamatics Bharath Mundlapudi Co-founder & CTO Orzota, Inc.
  2. 2. 2 CIGNEX Datamatics: Established in 2000, India, US & UK 8#1 14 Open Source Products Open Source Consultants Pure Play Open Source Services Company Open Source Implementations Global Offices Open Source Community Contributions Open Source Books Authored Business Engagement Platforms 13+ 5+ 5000+500+ 500+ Portals, Content & Collaboration Portals Enterprise Integration Identity Relationship Management Enterprise Content Document & Web Content Management Learning/Knowledge Management Imaging and Scanning - OCR/Digitization Enterprise & NLP Search BPM/Workflow E-Commerce B2B B2C Internet of Things (IoT) Big Data Analytics Data Integration Information Delivery Data Analysis Open Source Consulting Application Modernization OpeRA™ - Open Source Readiness Assessment Managed Application & Platform Services Business Engagement Platforms Big Data Platform – Panoramyx™ IoT Platform – Vitalstatistyx™ Digital Employee Engagement Platform – DEEP™ Reputation Management Platform – RMP™ Franchise Management Platform – FMP™ Concept 5k – The PoC Lab Pure Play Open Source Consulting Company
  3. 3. Bharath Mundlapudi is Co-founder, President & CTO of Orzota. Bharath was part of the initial team at Yahoo! that built Hadoop. He has extensive advisory experience (both strategy and technical) in Big Data technologies and Data Science solutions for various verticals – Finance, Retail, Manufacturing, and Tech. Prior to that, he was an architect in the Data Science group at Netflix and the Java team at Sun Microsystems. Presenter Profile
  4. 4. 4 Orzota Inc.: Established in 2012, Silicon Valley, CA & Chennai, India – Use Case Analysis – Data Architecture, Design and Augmentation – Exploratory and Predictive Analytics – Vendor Evaluation – PoCs and Implementation – Performance Optimization Big Data Management Services Clients Orzota is a Big Data solutions company that provides technology enabled services to help businesses accelerate their big data projects. It has a team of skilled data scientists, architects, and engineers have created solutions for customers in a wide variety of industries.
  5. 5. • Typical Enterprise Data Architecture • Augment Proprietary EDW with Hadoop – Pain-points of EDW – Use Cases – Solution • Case Study: Top 10 banks in the US – Solution architecture – Approach & Best Practices: Augmenting Teradata with Hadoop – Challenges – Benefits • Q & A5 Webinar Topics
  6. 6. 6 Typical Enterprise Data Architecture Transactional Systems (OLTP) Customer Relationship Management (CRM) Enterprise Resource Planning (ERP) Data Warehouse BI and Reporting Tools ETL Staging Data Mart Data Mart ETL
  7. 7. 7 Emerging Enterprise Data Architecture Efficient Analytical & Operational Processing Replication across multiple data centers for 99.999% uptime (<10 mins / yr) Scale on demand at reduced TCO Global Application with Geography specific Data Millions of reads & writes Agile Application Rollouts Next Generation Enterprise Data Warehouse
  8. 8. 8 Typical EDW Pain Points – The 5 Reasons 1 2 3 Inability to handle Unstructured Data RDBMS and MPP stores are not designed to handle variety Excessive Resource Use ETL and other jobs conflict with analytics use Wasted Storage Less than 20% of data is hot (actively used) 4 5 Inefficient Backups Backup to tape is slow and expensive Restores can cause significant disruption Expensive Disaster Recovery Full Data Warehouse at DR site is expensive
  9. 9. Solution: Hadoop • Open Source Apache Project – Framework for large scale data processing – Uses commodity servers – Massive scalability – Distributed and fault-tolerant – Most dominant big data platform • Distributed and Supported by: 9
  10. 10. 10 Solution: Augment your Proprietary EDW with Hadoop Backups Tape backups slow and expensive Backup to Hadoop is low cost. Recovery is fast. Examples Performance ETL, apps and analytics requirements compete Offload ETL and other apps to Hadoop. Reduce Primary EDW usage Storage Capacity ~20% of data is hot Move warm and cold data to Hadoop ROI Expensive and Proprietary Hadoop costs 2-10% of EDW & More.. Proprietary EDW Hadoop EDW Cloudera, Hortonworks & MapR
  11. 11. 11 EDW Augmentation with Hadoop ?? ?? ?? ?? ?? ?? Transactional Systems (OLTP) Customer Relationship Management (CRM) Enterprise Resource Planning (ERP) Hadoop Cluster Teradata Data Warehouse BI and Reporting ToolsBig Data Analytics Unstructured Data
  12. 12. 12 Hadoop Use Cases Network Failure Prediction Single View of “X” X= Customer, Employee, Partner Churn Analysis Fraud Detection Risk Modeling Data Lake Search Quality Targeted Marketing Recommendation Engine Operational Analysis
  13. 13. Case Study Top 10 Bank in US Hadoop Augmentation 13
  14. 14. Client Overview 14 • A major bank in US wanted to off-load proprietary EDW to Hadoop – Challenge • Current system couldn’t scale to support new business use cases • Upgrades would cost hundreds of millions of dollars – Objective • Avoid EDW upgrade and reduce on-going maintenance cost • Scale the data architecture based on need
  15. 15. Solution Architecture 15 Hadoop Teradata Data movement Data models Data verification Data quality ETL Process Data models Data verification ETL Process Data Engineers Data Scientists Data Analysts Mainframe Scheduler Data models Data movement
  16. 16. 16 Approach – Augmenting EDW with Hadoop Define – Business objective(s) – Use Case(s) – Migration strategy Architect – Solution Architecture – Select Right Technology Execute – Implement – Deploy – Verify & Test
  17. 17. 17 Key EDW Architecture Design to Production Challenges Business – Use-case Discovery – Value from Data – Project Management – Technology Strategy – Talent – Process Technical – Structuring Data – Functional Gaps – Floating Point Computation – Source of Truth – Key Management – Verification, Integrity & Quality – Getting the right data architecture
  18. 18. • Performance – Improved SLA for many workloads • Capacity – Improved capacity of EDW • Leverage open source technology advancements – Development tools, Advanced ML libraries etc. • Lower TCO • Commodity hardware for Hadoop at very low cost to off-load expensive EDW • Open Source technology reduced licensing costs and vendor dependency • Accelerated speed to development with out-of-box features 18 Benefits Delivered
  19. 19. Thank you Contact Us Sales: | Jobs – | Others –
  20. 20. 20 Take a Quick Assessment for FREE Test Drive Big Data Analytics Engage us for Proof-of-Concept (PoC) @ US5K Q & A 2 Hour FREE Consultation for all attendees Email us @ Contact Us @