1. Analysis Services in times of Continuous Integration

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4. Gabi Münster g.muenster@oh22.net @SQLMissSunshine
BI consultant at oh22data AG since 2010 Experience with SQL Server since version 2000 Main topics: SSAS (Multidimensional/Tabular), SSRS, MDS Speaker at chapter meetings, SQL Saturdays, Pass Summit, SQL Bits
@SQLMissSunshine

5. Agenda Motivation - Some words about CI Motivation - CI Best practices
Challenges for CI in BI
A possible approach
Continous deployment
Demo and a warning – It‘s c# coding time
Summary

6. Motivation - Some words about CI
Continuous Integration (CI) is a process well-known and often practiced in software development. It‘s aim is to prevent integration problems at a very early stage of development. Originally it covers the part of merging development versions of all developers into a single version and testing the validity of the combined version. But today CI is often used as a synonym for the complete process of guaranteeing integrity of the current version througout the whole environment.

7. Motivation - CI Best practices
Maintain a code repository
Automate the build
Support by tools like Jenkins, Travis CI, GoCD
Make the build self-testing
And log the results
Everyone commits to the baseline every day
Daily check in by all developers and merge into a single version
Every commit (to baseline) should be built
Implement automated build & deploy processes triggered by check-in

8. Motivation - CI Best practices
Keep the build fast
Errors should be identifiable quickly
Test in a clone of the production environment
The closer the environment to the productive system the more reliable are the tests
Make it easy to get the latest deliverables
Involve testers and stakeholders
Everyone can see the results of the latest build
Make build and test results visible
Automate deployment
Analogously to automated build
Supported by tools like Octopus Deploy

9. Challenges for CI in BI (I)
Data layer, Business Logic and Frontend
different projects or technologies
Integration tests over complete environment
Data models prohibit parallel development
XML based models barely suitable for merging
Little support by standard tools
High change frequency for requirements, especially during first phases of a project

10. Challenges for CI with Analysis Services (I)
Maintain a code repository
Developing with SSDT allows for integration of the projects into Source Control (e.G. TFS or GIT)
Not commonly used at the moment
Recommendable
Automate the build
Today: Manually out of SSDT or by script
Make the build self-testing
Not common yet
Tests from technical perspective could be deploy and process
Unit tests (e.g. nBI)

11. Challenges for CI with Analysis Services (II)
Everyone commits to the baseline every day
Parallel development in SSAS is tricky.
Merge in SSAS Tabular possible, especially with the new data model
Merge in SSAS Multidimensional not recommendable
But either way even the single developer should commit each day 
Every commit to baseline should be built
Implement automated build & deploy processes triggered by check-in
E.G. using MSBuild
Support by tools like Jenkins, Travis CI, GoCD

12. Challenges for CI with Analysis Services (III)
Keep the build fast
Builds of SSAS projects normally are, tests might not be 
Test in a clone of the production environment
Keeping environments up-to-date containing all elements of the BI landscape might get hard
Usage of Azure environments might help
Make it easy to get the latest deliverables
Keep versions available for QA and business approvers
The smaller the change sets, the easier the test
Automatic deployment of the complete environment?

13. Challenges for CI with Analysis Services (IV)
Everyone can see the results of the latest build
No difficulties
Automate deployment
Analogously to automated build
Big improvement e.g. for large OLAP databases: Do small tests by processing dimensions and small portions of the cubes for first test, automatic deployment will take care of e.g. a nightly full deployment
Supported by tools like Octopus Deploy

14. A possible approach Code repository
Integrate your SSDT project into Visual Studio Online
Automatic build
VS online support for existing templates or individual build processes
Automatic tests
Create tests with nBI and integrate those into your build process
Automatic deployment
Use results from your build to deploy to your other environments
And how?

15. Continuous deployment
Build processes generate artifacts you can use for deployment You can use scripting languages like PowerShell or C# to deploy those artifacts With C# you can also investigate existing databases. This will allow you do a kind of schema compare. A very advanced example of this is the BISM Normalizer from Christian Wade: This will allow you to do a partial deployment as well.

16. How to deploy your deploy your build artifacts using c#
How to deploy your deploy your build artifacts using c#? First trials of a BI person – It‘s coding time 
Demo

17. Summary
Several reasons like a different way of requirement handling and a slightly different project lifecycle makes integration of standard software development approaches into BI sometimes quite complex. Support by software vendors is also still not on a satisfactory level. So applying CI approaches to your BI projects will require a lot of effort and development on your side. But more and more tools and technologies are helping us with this and improving your standard processes as well as the quality and stability of your development is in my opinion a worthy goal to pay that price.

18. Some useful links AMO for multidimensional databases: ASSL
AMO Concepts and AMO Model
ASSL
Analysis Services Scripting Language (ASSL for XMLA)
AMO for Tabular (TOM)
Introduction to the Tabular Object Model (TOM) in Analysis Services AMO
TMSL
Tabular Model Scripting Language (TMSL) Reference
Schema Compare for Tabular: BISM Normalizer

19. Some useful links Continuous Integration Testing for BI Build Tools
Testing for BI
Build Tools
Deployment Tools

20. Any questions?

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