Multi-terabyte Data Warehouses on MYSQL? Absolutely!

1. Multi-terabyte Data Warehouses on MYSQL? Absolutely!

2. Agenda • Data Warehousing Today • Traditional Data Warehouse Solutions • A New Approach to Multi-Terabyte Data Warehouses

3. A Look at the Market • The worldwide database market was $18.8 billion in 2006 • 11.1 Billion for OLTP • 7.7 Billion for Data warehousing • 41% of the database budget is spent on data warehousing • Data warehousing is the number one area of CIO spend in North America in 2007 and 2008 • The data warehousing market is growing twice as fast as the OLTP database market

4. BI is Not Just for the Boardroom • BI started as a strategic, decision-support tool, used to create canned reports by executives and analysts to guide the ship • Today, BI is mission critical, and serves users across the enterprise, used to support not only traditional analytics, but also daily, operational decision making • These changes in use have brought changes in infrastructure requirements • Traditional RDBMS have trouble making the grade

5. How is the Data Warehouse different? • Many queries - all very different, unpredictable, and always changing • Queries are very complex - lots of joins, group bys, where clauses • Focus on history over many days, months, years • Interface to user and many different Business Intelligence tools OLTP Data Warehouse • Many simple transactions of exactly the same type • A lot of tuning (data model, indexes, partitions) in order for the specific transaction to perform well • Focus on current data • Scaling thru massive hardware and multiple copies of the database (eg. Online gaming systems have 1 database instance for every customer) • Interface to an application, often custom

6. The Data Warehousing Challenge “Volume of the world’s data doubles every three years. Ninety-two percent of new information is stored in magnetic media...organizations face a simple problem: what to do with all the data.” Industry Research “Collecting and analyzing information that enables your organization to better lead, decide, measure, manage and optimize its overall efficiency is a major financial and competitive differentiator. The faster an enterprise can gather and use relevant information, the faster it will be able to reduce costs and increase profits.” Gartner

7. The Problem: Data Warehouses are Strained Data Volume Complexity Trouble + = Users are asking more complex questions Data is aggregated and deleted Data is archived and not usable Complex queries are blocked Complex queries don’t perform Data is growing exponentially

8. What do the current limitations mean for Stakeholders? • Users • Do not get access to data they need; • Queries run too slowly; • Are not allowed to think creatively – ask new and different questions; • Are told to wait for months for what they want in minutes • IT • Besieged with requests for new data sources; • Feature creep and changing requirements straining resources; • Analytic system maintenance tuning affect support for operational systems; • Executives • CIOs face service level complaints and rising IT costs; • Business unit leaders without analytic data fail to achieve objectives;

10. About Infobright

11. Data Warehousing: Part of the Problem 10 10 Clickstream and log files 0101010101010101010101010101 10 10 0101010101010101010101010101 10 10 101 101 101 Existing data warehouse 0101010101010101010101010 0101010101010101010101010 10 10 10 01 01 01 External Sources 0101010101010101010101 0101010101010101010101 0 1 1 1 0 1 0 1 1 10 0 10 0 10 01 0 01 10 1 10 01 1 01 10 01 1 0 0 01 10 0 10 101 10 101 101 1 10 1 0 10 0 1 1 101 101 10 101 1010 0 10 1 . 1 0 010 01 0 1 010 0 1010 1 0 1 01 1 0 01 0 0101 10 101 0 01 0 101 0 More Kinds of Output Needed by More Business Users More Data & Data Sources • I/O intensive, write centric • Labor intensive, heavy indexing and partitioning • Hardware intensive: massive storage; big servers Traditional Data Warehousing

12. Real Life Example “Data is the difference. The difference between a campaign that meets your objectives vs. one that blows them away. Between paying $9 vs. $60 for a new customer. Between predicting what sites to advertise on vs. knowing you’ve put the right message in front of the right person no matter what site they are on.”

13. Desired Queries: • #1 - Campaign Effectiveness: • Goal was to determine the optimum number of times to show an ad to get best results • Actual query example: analyzed 2 billion rows of campaign frequency by date, to look at 5 campaigns in order to determine how many times a user saw each campaign. • #2 - User Demographics by Campaign: • Counts users by different demographic categories • Very wide range of possible results across varying range of rows • Two actual query examples: • User entered incorrect campaign number. Search was performed against 1.3 billion rows in user campaign aggregate table and the result was a null set • Largest campaign (highest results returned) where 89 million rows (11% of entire table) in user campaign were selected and joined to 57 million rows in the user dimension table

14. Traditional Data Warehouse Approach • Identify the reporting requirements • Determine the data needed • Design the data warehouse: • Extract-Transform-Load • Data Model (Logical and Physical) • Canned reports and BI tools …then • Revise the model as reporting requirements change and data grows: • Add indexes • Partition data to improve performance • Restrict users!

15. Traditional Data Warehouse Approach • Results: • Software costs well known and predictable but... • Management and support costs spiral: • Partitioning strategies • Indexing strategies • Additional data marts • More hardware • Business user satisfaction declines as restrictions are placed on: • Adhoc query capabilities • Volume of historical data that can be queried • Time lag between business requirement and system delivery • With this particular client, their systems were unable to handle this volume of data so they couldn’t run these queries at all!

16. Market Evolution All-purpose RDBMS Resource intensive, lots of DBA time Divide and conquer on lots of hardware (MPP) Nothing to address underlying issues Extending Database Concepts Incremental improvements, still inflexible Radical New Approach Hardware Advances Database Advances Traditional Work Harder Work Smarter Data Warehouse Innovator Working Smarter Innovation

17. What to Look for in a New Approach • New Approach • Leverages column approach • Automatically creates structures that: • finds needed data • responds to all queries, • are always ready • Has small footprint • Uses existing infrastructure • Is easy to setup and maintain Clickstream and log files 0101010101010101010101010101 0101010101010101010101010101 10 101 Existing data warehouse 0101010101010101010101010 0101010101010101010101010 10 01 External Sources 10 0101010101010101010101 0101010101010101010101 101 0 10 1 10 10 10 0 101 01 01 1 10 10 0 01 1 01 10 0 1 0 10 1 10 101 01 0 10 1 01 1 01 1 0 0 01 10 10 0 01 10 101 101 1 01 1 101 01 1 1010101010101010101 10 1 101010101010101010101010 0 10101010101010101010101010101 The Analytic Data Warehouse

19. A New Approach: Introducing Brighthouse Working Smarter, Not Harder Scalable solution without scaling IT Clickstream and log files 0101010101010101010101010101 • Better Analytics • Faster Response • Decreased IT Burden • Smaller Footprint 0101010101010101010101010101 10 101 Existing data warehouse 0101010101010101010101010 0101010101010101010101010 10 01 External Sources 10 0101010101010101010101 0101010101010101010101 101 0 10 1 10 10 10 0 101 01 01 1 10 10 0 01 1 01 10 0 1 0 10 1 10 101 01 0 10 1 1 01 01 1 0 0 01 10 10 0 10 101 101 101 1010101010101010101 1 101010101010101010101010 1 10101010101010101010101010101 The Infobright Analytic Data Warehouse

20. How Brighthouse Works Smarter • Smarter architecture: • Load data and go • No indices or partitions to build / maintain • Knowledge Grid created automatically as data loaded • Up to 40:1 compression reduces storage • Open architecture leverages off-the-shelf hardware Knowledge Grid—statistics and metadata “describing” the super-compressed data Data Packs—Data stored in manageably sized, highly compressed data packs Data compressed using algorithms tailored to data type Brighthouse

21. How Brighthouse Works Smarter Queryreceived by Brighthouse Optimizeriterates over the Knowledge Grid Only the data packs needed to resolve the query are decompressed Often query results can be determined from the Knowledge Grid alone Knowledge Grid Data Packs Brighthouse

22. Brighthouse is Easy on IT 0101010101010101010101010101 0101010101010101010101010 0101010101010101010101 10 0101010101010101010101 0101010101010101010101010 0101010101010101010101010101 10 10 BI Connectors 10 101 10 10 101 101 10 01 10 10 01 01 1 0 1 0 1 1 10 0 0 1 1 10 0 01 0 10 10 1 01 01 10 1 01 0 01 10 1 10 0 01 101 10 0 10 1 10 0 1 Existing Data Warehouses Clickstream/Logfiles • No strain on IT: • No need for physical data modeling • Run on standard hardware • Works with existing BI and ETL platforms • MySQL “wrapper” • No need to learn new database system • Leverage mature tools External Sources ETL Platform Connector

23. BrightHouse Architecture and MySQL • MySQL selected due to: • mature connectors, tools, resources • interconnectivity and certification with BI Tools • commercial OEM license protects our IP • most broadly used open-source DB (12 million users). • Benefits • Greatly improved time to market • Development focused on competitive differentiators • Sell to MySQL customers experiencing scalability problems

24. Real Life Example: Results with Brighthouse

25. Customer Query Response TimeResults vs. Oracle Oracle Time: 136 sec Brighthouse 3.0 Time: 16.8 sec

26. Query Speed as Volume of Data Grows Impact of Additional Data on Query Times Increases as data volume grows Average Response Time (in Secs) Brighthouse Performance Advantage • Queries were moderately complex, with at least two table joins and two or more where clauses • Tables were indexed • Response time represents the average response time of queries

27. Brighthouse Load Time RemainsConstant Data Load Times as Volume Increases Savings in processing time during load over conventional databases Load Time (in Secs) Brighthouse load time stays constant Volume (Rows in Millions) • Comparison of load to a single table. Data was loaded in 10 million row chunks • Table had a single index

29. Brighthouse is Fast • Brighthouse is designed specifically to quickly run complex queries on large data sets • The Knowledge Grid’s small chunks of highly compressed data are fast and easy to manipulate • Knowledge Grid optimizer iteratively optimizes query execution plan • Only data packs needed to answer query are opened; Often query results can be determined from the Knowledge Grid alone • Users enjoy fast response times no matter how complex or spontaneous their query “Each month we process and analyze data generated by 20 billion online transactions,”...We are pleased by Brighthouse’s performance and the fact that we now can get answers to questions we want to ask. --Ola Udén, CTO of TradeDoubler

30. Brighthouse is Flexible • Brighthouse ensures changing and complex analytic requirements are supported with fast response times • Knowledge Grid is built on-the-fly, creating a layer of statistics and metadata across all columns and rows • Knowledge Grid obviates the need for Indexing, data partitioning, or other physical data structures • Data no longer needs to be off-loaded or archived • Users can ask any question of all the data "Brighthouse allows us to do very complex analyses on over 30 terabytes of data” -- Jay Webster, General Manager, BlueLithium

31. Brighthouse is Simple • Brighthouse eliminates the complications, cost and disruption IT teams must endure to support complex queries • No DBA resources required to build indices and data partitions in response to user requirements • No complicated performance variables to tune; “No Knobs” • Leverages MySQL ease of use, connectivity, and supported BI tools • Runs on off-the-shelf hardware • Reduced complexity frees IT resources and significantly lowers lifetime TCO

32. How does Brighthouse impact TCO? • Hardware footprint 20 to 50 times smaller • Fewer DBA resources required • 40 – 60% reduction in one-time build • Up to 90% reduction in ongoing support • Support for existing infrastructure • Load and Go • Improved SLAs – immediate response vs weeks or months ETL and Data Changes PhysicalModeling Software Hardware Tuning

33. Contact Us • poc@infobright.com • 416.596.2483, x. 225 • Download Claudia Imhoff paper: • http://www.infobright.com Prove it! RAPID START: • Call us– we’ll walk you through a a few questions to mutually determine if our technology is a good fit. • Agree on process– e.g. your place or ours? • Load and go – Load your data, run your queries • Summarize results – performance, compression, load times • Next steps –did we prove it?