1. A Fast Growing Market

2. Interesting New Players Lyzasoft

9. The Topic of Data

10. Data Is Critical Where do we store all this data? – Relational? – NoSQL? – Hadoop?

11. Relational Is Still Very Important A wide variety of choices – Oracle, IBM, Microsoft, Sybase (SAP) – PostgreSQL, MySQL – Columnar Database (Infobright) New emerging Players – VoltDB – NimbusDB – Akiban

12. Designed For Analytics Best Fit Dynamic Analytics Good Fit Static Analytics Not A Fit Heavy OLTP Primary Use Case Many ad hoc queries Near real-time response Fast data load speeds Big Data / Fast Queries Wide Tables Aggregates: COUNT, SUM, etc. Deep Compression Rapid Deployment / Ease of Use Limited mixed workloads End of day reports Some changing data Simple JOINs Wide range of tool options Batch load feeds Heavy transactions Many stored procedures Heavy referential integrity Zero downtime Query Types Analytic-intensive queries Standard data types Limited JOINs Mixed workload queries Data mart-type BI queries SQL standard queries Lots of insert/deletes Frequent changes Updates across tables Example Query Average clicks per visit Total number of visits Total visit time Total bounce rate UNIQUE values Largest sales made Number of customers in region Sales numbers by region Average selling price by rep Num items sold by product New customers in month Products not sold Top selling product UPDATE balance in account(s) Abandon current cart DELETE all accounts over 1 year old INSERT new accounts 12

13. The Database for Analytic Applications

14. Challenging Times More online activity more web data Growth of mobile more call data, web data Servers/networks lots of log/event data More data With increasing value in the details  Target individual customers  Identify micro-segments  Find security threats  Identify fraud “Enterprise data growth over the next 5 years is estimated to be 650%.” Gartner

15. Analytic Infrastructure Requirements Handles large data volumes with less cost and complexity Meets business users needs – Fast query response – static and ad hoc queries – Fast access to new data – Access to detailed data, not just aggregates Takes less IT time – Easy to implement – No complex hardware configuration – No index creation, data partitioning or manual tuning Lower cost 15

16. Designed For Analytics Best Fit Dynamic Analytics Good Fit Static Analytics Not A Fit Heavy OLTP Primary Use Case Many ad hoc queries Near real-time response Fast data load speeds Big Data / Fast Queries Wide Tables Aggregates: COUNT, SUM, etc. Deep Compression Rapid Deployment / Ease of Use Limited mixed workloads End of day reports Some changing data Simple JOINs Wide range of tool options Batch load feeds Heavy transactions Many stored procedures Heavy referential integrity Zero downtime Query Types Analytic-intensive queries Standard data types Limited JOINs Mixed workload queries Data mart-type BI queries SQL standard queries Lots of insert/deletes Frequent changes Updates across tables Example Query Average clicks per visit Total number of visits Total visit time Total bounce rate UNIQUE values Largest sales made Number of customers in region Sales numbers by region Average selling price by rep Num items sold by product New customers in month Products not sold Top selling product UPDATE balance in account(s) Abandon current cart DELETE all accounts over 1 year old INSERT new accounts 16

17. What is Unique about Infobright?  Uses intelligence, not hardware, to drive query performance:  Creates information about the data (metadata) upon load, automatically  Uses metadata to eliminate or reduce the need to access data to respond to a query  The less data that needs to be accessed, the faster the response  What this means to you:  No need to partition data, create/maintain indexes or tune for performance  ad hoc queries are as fast as static queries, so users have total flexibility  ad hoc queries that may take hours with other databases run in minutes; queries that take minutes with other databases run in seconds 17

18. Infobright Technology: Key Concepts 1.Column orientation 2.Data packs and Compression 3.Knowledge Grid 4.Optimizer 18

19. 1. Column vs. Row Orientation Employee_IDJobDeptCity 1ShippingOperationsToronto 2ReceivingOperationsToronto 3AccountingFinanceBoston 1ShippingOperationsToronto 2ReceivingOperationsToronto 3AccountingFinanceBoston 1ShippingOperationsToronto 2ReceivingOperationsToronto 3AccountingFinanceBoston Data stored in rows Data stored in columns 1ShippingOperationsToronto 2ReceivingOperationsToronto 3AccountingFinanceBoston 1ShippingOperationsToronto 2ReceivingOperationsToronto 3AccountingFinanceBoston 19

20. Project requirements  Executive dashboard / reporting tool with flexible reporting options for business users with multiple levels of detail  Required ability to consolidate large volumes of data from multiple sources  Request had been outstanding for over a year – needed solution that could be implemented quickly, at low cost, without central IT effort Customer Example: Austin Energy Austin Energy: 9 th largest public power utility in the US 20

21. Austin Energy Results 21

22. Saving Time for Business Users and IT – Fastest time to value – Download in minutes, install in minutes – No indexes to create – Simple hardware – Minimal administration – No indexes, no data partitioning – Self-tuning and self-managing – Eliminate or reduce aggregate table creation – Outstanding performance – Designed for analytics – Fast query response against large data volume – High speed parallel loader 22

23. Getting Your Database On The Cloud Lots of options – Do it yourself – Pursue a hosting environment – Use solutions like RightScale