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Presentation first presented to the IEEE Computer Society Chapter, New Orleans Section
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Typical Business "Design"
Typical Goal Scenario
What success still looks like - version 1
What success still looks like - version 2
What success should look like
Glossary
Data warehouse
Bill Inmon - "A warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process" - typically associated with top-down design
Ralph Kimball - "A copy of transaction data specifically structured for query and analysis." - Typically associated with bottom-up design
Glossary (2)
ETL
Extract...Transform...Load
Shorthand for any number of ways of getting the data into the warehouse.
Sometimes it's really transform...extract...load, sometimes it's extract...load...transform...load.
Key things are to have a strategy and principles for when data is changed/cleaned/conformed/exceptions reported.
Glossary (3)
FactsDimensions
Conformed DimensionsSlowly Changing Dimensions
Granularity
Dimensionally modelled data is mostly associated with Kimball.
Huge advantages in analyzing large amounts of data.
Modelling is problematic, but not nearly as hard as normalizing a non-normalized database.
Glossary (4)
Business IntelligenceSingle version of the truth
These are relatively meaningless, but they point to the problem trying to be solved:
Get good decision support information to the business - every business is different, and there isn't a silver bullet
Eliminate, as much as possible, the ability for users to generate inconsistent information from the same data
Glossary (5)
Data MartSilos
Silos are mini-data warehouses that are specialized to a subject area - typically from a bottom-up approach.
Data Marts are the components of a data warehouse in the top-down design, the building blocks of a data warehouse in a bottom-up design.
Typically, you cannot really do JUST top-down or JUST bottom-up. The reality is always hybrid, because you have to look forward to enterprise-level integration.
Glossary (6)
Operational Data StoreEnterprise data warehouse
ODS is a place where data is combined before load. Sometimes there are services performed off this. Typically, the data model has not changed dramatically from the original operational source systems, but it is (another) copy of the data.
EDW is an Inmon term which means that the data warehouse covers the enterprise in an integrated fashion. It is mainly used to distinguish from a data warehouse which does not cover the entire enterprise.
Glossary (7)
OLTPOLAP
OnLine Transaction Processing: Typical online systems, may maintain coherent temporal history, may overwrite themselves when data is changed, usually modelled in third normal form or better, Entity-Relationship modeling.
OnLine Analytical Processing: Fast analysis of multi-dimensional data - generally refers to tools running against dimensional data warehouses because the dimensions are explicit - often precalculated "cubes" are created
Normal Form
Facts and Dimensions
Dimensional Modelling
Facts:Usually scalar quantitiesTypically can be:SUM, AVG, etc.
Modelling:View all data as either facts or dimensionsDetermine the nature of the changes in the dimensionsThen divide up dimensions for convenience - based on usage/data patternsCombination of art and science
Topics
Conformed dimensionsNULLsJunk DimensionsToo Few DimensionsToo Many DimensionsParallel ETL
Conformed Dimensions
Reduces the learning curveAllows models to be combinedAccount number padding, e.g.
NULLs
Usually represent unknowns
Big problem for users in face of model evolution
If you have a derived stat/measure like customer.allfees = customer.latefees + customer.nsffees
Model starts out like-latefees money NOT NULL-nsffees money NOT NULL
Now we branch out into mailbox rental:-customer.rentalfees NULL (or NOT NULL?)
customer.allfees = customer.latefees + customer.nsffees + customer.rentalfees
Handle with a view, or populate old data with 0
Best Practices
There are tons of top ten lists of tips and keys to success in articles and books. I will give you my top two.
Incremental Delivery – Show successes early, win people over, prove concepts and approach
Proactively Manage Quality - Test thoroughly and automate – Testing is usually considered important, but people don’t approach it systematically. Round-trip the data, know the dimensional behavior with benchmarking, automate exception reporting and make sure false positives don’t make the warning system ignored. Get confidence by showing the tests are working. Add tests as defects are found, documenting expectations.
Worst Practices
Again, there are plenty of online tips – every one of the best practices has a corresponding anti-practice, but these are my top two.
Avoid understanding the data, the business motivations, or the details because there are far too many feeds of data coming into the warehouse. Avoid looking ahead to how the data will be used because you shouldn’t change the ETL process to accommodate expectations or provide services.
Handle every model the same way, so the data warehouse is consistent, even if some models are awkward and difficult for users to use and difficult to change over time as the business evolves.
Some things to keep in mind
Terminology is confusing and inconsistent – only your architecture matters – keep eyes open to approaches, but terminology is not as important as conventions chosen matching environment desired.
Overriding concern is practicality – get the information into users hands, this will drive the need for more information and guide you into managing the data.
Decoupling produces a lot of redundancy: Source->Flat File->EDI gateway->Stage->DW – understand where the redundancy can be removed, and where decoupling is the goal.
Performance Issues
Cleansing/Manipulation:Y/N, M/F, Codes – standardize in the ETL, use data types efficientlyIndexes – keep end goals in mind, index according to expected usageSARG'able, leading zeros/spaces – look for consistency where posible to avoid data manipulation prior to joins, this helps index usagePartitioning tables – for sliding windows of data retention, partitioning the tables allows old data to be dropped off the end of the fact tables fairly easily
Application Logic
Shared work should be pushed into ETL when:Not likely to changeExpensiveEverybody needs it
Examples:Trivial - Scaling to convention (rates)Intermediate - Simple calculations (Patient Age)Marginal - Interest rates, risk ratings
Alternative to marginal cases - generate additional facts, either in their own fact tables or as late arriving facts
When a DW is shared, lots more applications to worry about
Links
http://delicious.com/caderoux1/ieee-nola-dw
http://www.speakerrate.com/caderoux
