1. CS 345: Topics in Data Warehousing Thursday, September 30, 2004

2. Review of Tuesday’s Class Roles of Information Technology –1. Automate clerical work –2. Decision support OLAP vs. OLTP –Different query characteristics –Different performance requirements –Different data modeling requirements –OLAP combines data from many sources High-level course outline –Logical Database Design –Query Processing –Physical Database Design –Data Mining

3. Outline of Today’s Class Data integration Basic OLAP queries –Data cubes –Slice and dice, drill down, roll up –MOLAP vs. ROLAP –SQL OLAP Extensions: ROLLUP, CUBE Star Schemas –Facts and Dimensions

4. Loading the Data Warehouse Source Systems Data Staging AreaData Warehouse (OLTP) Data is periodically extracted Data is cleansed and transformed Users query the data warehouse

5. Terminology: ETL ETL = Extraction, Transformation, & Load Extraction: Get the data out of the source systems Transformation: Convert the data into a useful format for analysis Load: Get the data into the data warehouse (…and build indexes, materialized views, etc.) We will return to this topic in a couple weeks.

6. Data Integration is Hard Data warehouses combine data from multiple sources Data must be translated into a consistent format Data integration represents ~80% of effort for a typical data warehouse project! Some reasons why it’s hard: –Metadata is often poor or non-existent –Data quality is often bad Missing or default values Multiple spellings of the same thing (Cal vs. UC Berkeley vs. University of California) –Inconsistent semantics What is an airline passenger?

7. Federated Databases An alternative to data warehouses Data warehouse –Create a copy of all the data –Execute queries against the copy Federated database –Pull data from source systems as needed to answer queries “lazy” vs. “eager” data integration Data WarehouseFederated Database Query Answer Query Extraction Rewritten Queries Answer Source Systems Source Systems Warehouse Mediator

8. Warehouses vs. Federation Advantages of federated databases: –No redundant copying of data –Queries see “real-time” view of evolving data –More flexible security policy Disadvantages of federated databases: –Analysis queries place extra load on transactional systems –Query optimization is hard to do well –Historical data may not be available –Complex “wrappers” needed to mediate between analysis server and source systems Data warehouses are much more common in practice –Better performance –Lower complexity –Slightly out-of-date data is acceptable

9. Two Approaches to Data Warehousing Data mart: like a data warehouse, but smaller and more focused Top-down approach –First build single unified data warehouse with all enterprise data –Then create data marts containing specialized subsets of the data from the warehouse Bottom-up approach –First build a data mart to solve the most pressing problem –Then build another data mart, then another –Data warehouse = union of all data marts In practice, not much difference between the two Our book advocates the bottom-up approach

10. Data Cube Axes of the cube represent attributes of the data records –Generally discrete-valued / categorical –e.g. color, month, state –Called dimensions Cells hold aggregated measurements –e.g. total $ sales, number of autos sold –Called facts Real data cubes have >> 3 dimensions JulAugSep CA OR WA Red Blue Gray Auto Sales

11. Slicing and Dicing JulAugSep CA OR WA Red Blue Gray Red Blue Gray JulAugSep CA OR WA Blue JulAugSep CA OR WA Blue JulAugSep Total

12. Querying the Data Cube Cross-tabulation –“Cross-tab” for short –Report data grouped by 2 dimensions –Aggregate across other dimensions –Include subtotals Operations on a cross-tab –Roll up (further aggregation) –Drill down (less aggregation) CAORWATotal Jul453330108 Aug503642128 Sep383140109 Total133100112345 Number of Autos Sold

13. Roll Up and Drill Down CAORWATotal Jul453330108 Aug503642128 Sep383140109 Total133100112345 Number of Autos Sold CAORWATotal 133100112345 Number of Autos Sold CAORWATotal Red402940109 Blue453137113 Gray484035123 Total133100112345 Roll up by Month Number of Autos Sold Drill down by Color

14. “Standard” Data Cube Query Measurements –Which fact(s) should be reported? Filters –What slice(s) of the cube should be used? Grouping attributes –How finely should the cube be diced? –Each dimension is either: (a) A grouping attribute (b) Aggregated over (“Rolled up” into a single total) –n dimensions → 2 n sets of grouping attributes –Aggregation = projection to a lower-dimensional subspace

15. Full Data Cube with Subtotals Pre-computation of aggregates → fast answers to OLAP queries Ideally, pre-compute all 2 n types of subtotals Otherwise, perform aggregation as needed Coarser-grained totals can be computed from finer-grained totals –But not the other way around

16. Data Cube Lattice Total State MonthColor State, Month State, Color Month, Color State, Month, Color Drill Down Roll Up

17. MOLAP vs. ROLAP MOLAP = Multidimensional OLAP Store data cube as multidimensional array (Usually) pre-compute all aggregates Advantages: –Very efficient data access → fast answers Disadvantages: –Doesn’t scale to large numbers of dimensions –Requires special-purpose data store

18. Sparsity Imagine a data warehouse for Safeway. Suppose dimensions are: Customer, Product, Store, Day If there are 100,000 customers, 10,000 products, 1,000 stores, and 1,000 days… …data cube has 1,000,000,000,000,000 cells! Fortunately, most cells are empty. A given store doesn’t sell every product on every day. A given customer has never visited most of the stores. A given customer has never purchased most products. Multi-dimensional arrays are not an efficient way to store sparse data.

19. MOLAP vs. ROLAP ROLAP = Relational OLAP Store data cube in relational database Express queries in SQL Advantages: –Scales well to high dimensionality –Scales well to large data sets –Sparsity is not a problem –Uses well-known, mature technology Disadvantages: –Query performance is slower than MOLAP –Need to construct explicit indexes

20. Creating a Cross-tab with SQL SELECT state, month, SUM(quantity) FROM sales GROUP BY state, month WHERE color = 'Red' Grouping Attributes Measurements Filters

21. What about the totals? SQL aggregation query with GROUP BY does not produce subtotals, totals Our cross-tab report is incomplete. CAORWATotal Jul453330? Aug503642? Sep383140? Total???? Number of Autos Sold StateMonthSUM CAJul45 CAAug50 CASep38 ORJul33 ORAug36 ORSep31 WAJul30 WAAug42 WASep40

22. One solution: a big UNION ALL SELECT state, month, SUM(quantity) FROM sales GROUP BY state, month WHERE color = 'Red‘ UNION ALL SELECT state, "ALL", SUM(quantity) FROM sales GROUP BY state WHERE color = 'Red' UNION ALL SELECT "ALL", month, SUM(quantity) FROM sales GROUP BY month WHERE color = 'Red‘ UNION ALL SELECT "ALL", "ALL", SUM(quantity) FROM sales WHERE color = 'Red' Original Query State Subtotals Month Subtotals Overall Total

23. A better solution “UNION ALL” solution gets cumbersome with more than 2 grouping attributes n grouping attributes → 2 n parts in the union OLAP extensions added to SQL 99 are more convenient –CUBE, ROLLUP SELECT state, month, SUM(quantity) FROM sales GROUP BY CUBE(state, month) WHERE color = 'Red'

24. Results of the CUBE query StateMonthSUM(quantity) CAJul45 CAAug50 CASep38 CANULL133 ORJul33 ORAug36 ORSep31 ORNULL100 WAJul30 WAAug42 WASep40 WANULL112 NULLJul108 NULLAug128 NULLSep109 NULLNULL345 Notice the use of NULL for totals Subtotals at all levels

25. ROLLUP vs. CUBE CUBE computes entire lattice ROLLUP computes one path through lattice –Order of GROUP BY list matters –Groups by all prefixes of the GROUP BY list GROUP BY ROLLUP(A,B,C) A,B,C (A,B) subtotals (A) subtotals Total GROUP BY CUBE(A,B,C) A,B,C Subtotals for the following: (A,B), (A,C), (B,C), (A), (B), (C) Total

26. ROLLUP example Total State MonthColor State, Month State, Color Month, Color State, Month, Color SELECT color, month, state, SUM(quantity) FROM sales GROUP BY ROLLUP(color,month,state)

27. Logical Database Design Logical database design is the topic for the next two weeks. Logical design vs. physical design: –Logical design = conceptual organization for the database Create an abstraction for a real-world process –Physical design = how is the data stored Select data structures (tables, indexes, materialized views) Organize data structures on disk Three main goals for logical design: –Simplicity –Expressiveness –Performance

28. Goals for Logical Design Simplicity –Users should understand the design –Data model should match users’ conceptual model –Queries should be easy and intuitive to write Expressiveness –Include enough information to answer all important queries –Include all relevant data (without irrelevant data) Performance –An efficient physical design should be possible

29. Another perspective… From a presentation at SIGMOD*: Three design rules: –Simplicity *by Jeff Byard and Donovan Schneider, Red Brick Systems

30. Star Schema Sales Date Product Store Promotion Fact table Dimension tables

31. Dimension Tables Each one corresponds to a real-world object or concept. –Examples: Customer, Product, Date, Employee, Region, Store, Promotion, Vendor, Partner, Account, Department Properties of dimension tables: –Contain many descriptive columns Dimension tables are wide (dozens of columns) –Generally don’t have too many rows At least in comparison to the fact tables Usually < 1 million rows –Contents are relatively static Almost like a lookup table Uses of dimension tables –Filters are based on dimension attributes –Grouping columns are dimension attributes –Fact tables are referenced through dimensions

32. Fact Tables Each fact table contains measurements about a process of interest. Each fact row contains two things: –Numerical measure columns –Foreign keys to dimension tables –That’s all! Properties of fact tables: –Very big Often millions or billions of rows –Narrow Small number of columns –Changes often New events in the world → new rows in the fact table Typically append-only Uses of fact tables: –Measurements are aggregated fact columns.

33. Comparing Facts and Dimensions Narrow Big (many rows) Numeric Growing over time Wide Small (few rows) Descriptive Static Facts Dimensions Facts contain numbers, dimensions contain labels

34. Four steps in dimensional modeling 1.Identify the process being modeled. 2.Determine the grain at which facts will be stored. 3.Choose the dimensions. 4.Identify the numeric measures for the facts.

35. Grain of a Fact Table Grain of a fact table = the meaning of one fact table row Determines the maximum level of detail of the warehouse Example grain statements: (one fact row represents a…) –Line item from a cash register receipt –Boarding pass to get on a flight –Daily snapshot of inventory level for a product in a warehouse –Sensor reading per minute for a sensor –Student enrolled in a course Finer-grained fact tables: –are more expressive –have more rows Trade-off between performance and expressiveness –Rule of thumb: Err in favor of expressiveness –Pre-computed aggregates can solve performance problems

36. Choosing Dimensions Determine a candidate key based on the grain statement. –Example: a student enrolled in a course –(Course, Student, Term) is a candidate key Add other relevant dimensions that are functionally determined by the candidate key. –For example, Instructor and Classroom Assuming each course has a single instructor!