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Azure DevOps for the
Data Professional
Presented by
Sarah Dutkiewicz
Cleveland Tech Consulting, LLC
Agenda
What is Azure DevOps?
Why should Data Professionals Care?
Benefits of Source Control
Look into Azure DevOps features
SQL Server Data Tools (SSDT)
More In-Depth Look at a Project
Scenario, Part 1
Databases need scripted
We need to see who is changing what and when
Scenario, Part 2
We have a new
person!
New person’s
name doesn’t
work with the
current system.
OneName II
What is this about?
High Level Overview
Allows build and release automation using pipelines
Can break projects into small, deliverable units
Can track work against those units
Can store versions of code using Git
Why Should Data Professionals Care?
DevOps
Benefits for Data Professionals
Track SQL work against work items – explains value to
the client
Schema comparison – easy generation of incremental
change scripts
Code review and change tracking through source
control
Automate deployment processes
Tooling Used
Visual Studio 2019 Community Edition
SQL Server 2017 Developer Edition
Azure DevOps Server Express locally
Can use Azure DevOps in the Cloud for individuals and
small teams, in addition to enterprise options
Source Control
Why Source Control?
Change tracking
Accountability
Versioning
Code review
Repository
AdventureWorks-Local
Folders structured by
schema
Includes database objects
Includes permissions and
extended properties
Sample Commit History
What Changed?
Linking Work to Tasks
Tasks and Links to Work
Code Reviews
Features of
Key Groupings
Overview
Boards
Repos
Pipelines
Overview Section
Dashboards of overall project views
Includes a Wiki
Boards
Work Items
Boards
Backlogs
Sprints
Queries
Estimate
Work Items
Repos
Files
Commits
Pushes
Branches
Tags
Pull Requests
Pipelines
Builds
Releases
Library
Task groups
Deployment
groups
Builds vs Releases
Builds
Compile the code
Build the DACPACs,
change scripts, etc.
Tied to repos
Releases
Deploy the code
Update databases
with DACPACs,
change scripts, etc.
Can trigger releases
based off of builds
Releases
Release Tasks
Deployment Groups
Seen with Release pipelines
Used for deploying to particular resources
Can be used for deploying to Azure or to local
resources
SQL Server Data Tools (SSDT)
SSDT Versions
Visual Studio 2019
Database Projects - Data Storage and Processing workload
AS, IS, RS – extensions
Visual Studio 2017
Database Projects - Data Storage and Processing workload
AS, IS, RS – SSDT 2017 download
SSDT Standalone
Still available, but recommended to use Visual Studio
Database Project
Example:
AdventureWorks2017
Includes:
Tables
Views
Stored Procedures
Triggers
… and more
Ways to Start a Database Project
From scratch
Import from database connection
Import from script
Import from DACPAC
Starting Source of Truth
Which to start with?
Typically, Production – what does the world know today?
Can build change scripts or migrations to go from prod to
dev
Schema compare can help with that too
AdventureWorks Demo
AdventureWorks Demo Plan
Starting with base solution
Explore a use case not thought of
Alter table
Alter data for the use case
Once scripted in a Database
project, let the code be the
source of truth.
Final Words of Advice
Build
often.
If multiple people on the project,
schema compare often.
Generate Script gives you more
control of what’s being run.
Learn More
Azure DevOps and SQL
DevOps with SQL Server:
https://www.microsoft.com/en-us/sql-
server/developer-get-started/sql-devops/
Azure DevOps, SSIS, and Git:
https://andyleonard.blog/2019/03/azure-devops-ssis-
and-git-part-0-getting-started/
Understanding Git
Git Immersion: http://gitimmersion.com/
Git on Codecademy:
https://www.codecademy.com/learn/learn-git
GitHub Guides: https://guides.github.com/
Axosoft’s Gitflow Tutorial:
https://blog.axosoft.com/gitflow/
Any Questions?
Twitter: @sadukie
LinkedIn:
https://linkedin.com/in/sadukie
Email:
sarah@cletechconsulting.com

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Azure DevOps for the Data Professional

Editor's Notes

  1. Presented February 2, 2020 at SQL Saturday #930 Cleveland, OH