The document discusses agile approaches to data warehousing. It defines a data warehouse as a place to store consolidated, cleansed data from across a company to serve as a single source of truth. It advocates adopting agile principles like rapid delivery and embracing changes. Engineering disciplines like design patterns, ETL automation, and unit testing are presented as ways to achieve agility while maintaining quality. The speaker promotes these approaches and will present on related topics at an upcoming conference.

1. Agile Data Warehousing
From Start to Finish
Presenter: Davide Mauri, Architect & Mentor, SolidQ
Moderator: Alex Whittles

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5. Davide Mauri
 SolidQ Mentor
 Board of Directors, SolidQ Italy
 Microsoft SQL Server MVP
 Works with managers to build effective,
tailor-made BI solutions for customers
@mauridb

6. Agile Data Warehousing
From Start to Finish
Davide Mauri, Architect & Mentor, SolidQ

7. Agenda
What is a DWH, really?
Agile: the only way to succeed
Engineering the DWH
ETL Design Patterns
ETL Automation
Testing

8. What is a DWH, really?

9. The Data-Driven Age

10. Isn’t the DWH and “old” thing?
Big Data, In Memory and all the new stuff, can’t just replace
the Data Warehouse?
The answer would be “yes”, if a DWH would be a simple
“container” of data.
But it’s much more than this.

11. What is a DWH, really?
In this new era, data is like water.
Who will ever drink from
untested, untrusted,
uncertified data?

12. What is a DWH, really?
Would a manager or a decision maker, take a decision
based on data of which he doesn’t know the source, the
integrity and the correctness?

13. What is a DWH, really?
The Data Warehouse is the place where managers and
decision makers will look for
• Correct
• Trusted
• Updated
Data in order to make a
conscious decision

14. What is a DWH, really?
The answer is now easy:

15. What is DWH, really?
A place to store consolidated data coming from the whole
company
A place where cleanse, verify and certify data
A place where historic data is stored
A place that holds the single version of truth (if there is one!)
Forms the core of a BI solution
User friendly Data models, designed to make data analysis
easier

16. Modern Data Environment
Master
Data
EDW
Data Mart
Big Data
Unstructured
Data
BI Environment
Analytics Environment
Structured
Data Data Scientist
Decision Maker

17. Agility: the only way to succeed

18. EDW: Reality Check
EDW is the trusted container of all company data
It cannot be created in “one day”
It has to grow and evolve with business needs.
It will never be 100% complete

19. The story so far

20. Adapt to Survive
“50% of requirements change in the first year of a BI
project”
Andreas Bitterer, Research VP, Gartner

21. Agile Principles
Small design upfront. Prototype.
Delivery quickly, Deliver frequently.
Users are part of the development team!
Feedback is a key part of the success
They’ll grow with the solution and the solution will grow with them
Embrace Changes!
http://agilemanifesto.org/principles.html

22. Agile Challenges
Delivery Quickly and Fast
 Challenge: keep high quality, no matter who’s doing the work
Embrace Changes
 Challenge: don’t introduce bugs. Change the smallest part
possible. Use automatic Testing to preserve and assure data
quality.

23. Engineering the DWH

24. Engineering the solution
To be Agile, some engineering practices needs to be included in
our work model
Agility != Anarchy
Engineering:
 Apply well-known models
 Define, Apply & Enforce rules
 Automate and/or Check rules application
 Measure
 Test
2

25. Engineering the solution
Favor Kimball Approach (for user-facing models)
 Dimensional Modeling
 Fact & Measures
 Dimensions
Use views to introduce abstraction layers
 Reduce the “friction” between layers (source / stage / dwh / dm)
 Apply the “Information Hiding Principle”

26. Engineering the solution
Define & Force the application of well-known ETL patterns
 SCD1 / SCD2
 Incremental / Partition Load
Divide Et Impera
 At least two SSIS solutions
 many small SSIS Packages
 5 Databases (STG, CFG, LOG, MD, DWH)

27. Design Pattern
“A general reusable solution
to a commonly occurring
problem within a given
context”

28. Design Pattern
Generic ETL Pattern
 Partition Load
 Incremental/Differential Load
Generic DWH/BI Design Pattern
 Slowly Changing Dimension
 SCD1, SCD2, ecc.
 Fact Table
 Transactional, Snapshot, Temporal Snapshot

29. Design Pattern
Specific SQL Server Patterns
 Change Data Capture
 Change Tracking
 Partition Load
 SSIS Parallelism

30. ETL Automation

31. No Monkey Work!
Let the people think and let
the machines do the
«monkey» work.

32. Invest on Automation?
Faster development
 Reduce Costs
 Embrace Changes
Less bugs
Increase solution quality and make it consistent throughout
the whole product

33. Hi-Level Vision
Technical Process
Technical Process
ETL ETL
STG
OLTP DWH
ETL
Business Process

34. ETL Phases
«E» and «L» must be
 Simple, Easy and Straightforward
 Completely Automated
 Completely Reusable
«E» and «L» have ZERO value in a DWH Solution
 Should be done in the most economic way

35. Automation Tools
PowerShell / .NET
 Supported by SMO & SSIS API
 Microsoft creates platforms not only products!
BIML – BI Markup Language
 From Varigence
 Free with BIDS Helper
 Full support with MIST

36. Metadata
Metadata is needed in order to make automation a
repeatable process
 Source to Staging Info
 Staging to DWH info
 Dimension Keys
 Dimension & Fact Table relationship
Extended Properties + SQL Server DMVs help to maintain
metadata coherent

37. Unit Testing

38. Unit Testing
Data MUST be tested.
It’s like water, remember?
If trust is lost, DWH is an
#epicfail

39. Unit Testing
Before releasing anything data in the DW must be tested.
User has to validate a sample of data
 (e.g.:total invoice amount of January 2012)
That validated value will become the reference value
Before release, the same query will be executed again.
 If the data is the expected reference data then test is green
 otherwise the test fails

40. Unit Testing
Of course test MUST be automated when possible
 Visual Studio
 NUnit extensions
 NBI
 BI.Quality
What to test?
 Aggregated results
 Specific values of some «special» rule
 Fixed bugs/tickets
4

41. The perfect BI process & architecture
Iterative!

42. Questions?

43. Like What You Heard?
Davide will be presenting at PASS Summit 2014!
 PreConference:
 Agile Data Warehousing: Start to Finish
 General Session:
 Agile BI: Unit Testing and Continuos Integration
Use discount code 24HOP14
to save $200!
@mauridb

44. Coming up next …
DAX Formulas in Action
Alberto Ferrari

45. Thank You for Attending