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By Group 19: Yuefeng Hou , Ankit Kinra

The Netezza Data Appliance: A Platform for High Performance Data Warehousing Based on - Too many cooks spoil the data warehouse broth Cut your staffing costs By Eira Hayward ( http :// www.theregister.co.uk/2011/11/16/data_warehouse_staffing ). By Group 19: Yuefeng Hou , Ankit Kinra.

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By Group 19: Yuefeng Hou , Ankit Kinra

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  1. The Netezza Data Appliance: A Platform for High Performance Data WarehousingBased on - Too many cooks spoil the data warehouse broth Cut your staffing costs By Eira Hayward (http://www.theregister.co.uk/2011/11/16/data_warehouse_staffing) By Group 19: YuefengHou, AnkitKinra

  2. Costs of Data Warehousing • Hardware, software, and administration • Hidden Costs: business units outside the IT organization • Getting data in and out, not productive, keep the surrounding business intelligence environment going

  3. Challenges of Data Warehousing • Analyzing billions of data points and petabytes of data through a sea of ambiguity • A huge amount of cost • Inefficient use of manpower • Preventing analysts from trying new things • Waste opportunity, cost time and resources, and put the company at risk

  4. Netezza Data Warehouse and Analytics Appliance • Highly flexible and robust warehouse • Merges the traditional big data approaches, coping with both structured and unstructured data • Simplifies the database environment • Resource can be used in a more productive manner • Implementation and ownership costs are much lower

  5. Netezza Data Warehouse and Analytics Appliance • Atechnology foundation able to sustain performance as more users run increasingly complex workloads and as data volumes continue to grow • Simple, reliable, and immediate • Able to handle almost incomprehensible workloads without complexity getting in the way

  6. Netezza Architecture Principles • Processing close to data source • Platform for advanced analytics • Appliance Simplicity • Flexible configurations and extreme scalability.

  7. Integration Example for Netezza • A typical monthly task takes about two weeks involves running financial prediction models on millions of customers when performed manually. • Creation of C/C++ task reduces the time on parallel Netezza architecture. • Reduces time but increases debugging and setting up time. • Solution?

  8. Integration Example for Netezza (Cont.) • SAS Suite operations inside Netezza. • Scoring accelerator removes manual coding and has benefits of scalability and high performance. • Loading of data can be handled via partner application suite called Kalido which has many Automated Load Routines which simplify the loading tasks.

  9. Relation with course • Data Warehouse in chapter 29 • OLAP in chapter 29

  10. References • Netezza Blogs by Thomas Dinsmore (URL:http://thinking.netezza.com/blog/netezza-and-sas-integration-best-practices) • The Netezza Data Appliance Architecture: A Platform for High Performance Data Warehousing and Analytics by Phil Francisco (URL http://www.netezza.com/documents/whitepapers/Netezza_Appliance_Architecture_WP.pdf ) • Too many cooks spoil the data warehouse broth Cut your staffing costs By Eira Hayward (URL http://www.theregister.co.uk/2011/11/16/data_warehouse_staffing ) • Much Ado about Loading by John Evans (http://blog.kalido.com/ado-loading/)

  11. Questions ?

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