When the unused become used: Using SQL Data Catalog to spot the pitfalls of a data classification process

“The surprise is that you continue to be surprised.”
Jill A. Davis

But what surprise am I alluding to? Of course it is the surprise that “all of a sudden” you have sensitive data in tables where there ought not be!

What I mean is; we have a fantastic process in place: we have completely classified our entire environments, developers move changes to classification up-stream just like schema changes and we have an automatic process to check and update classifications as part of our pipeline.

But as part of this process we “descoped” everything that was non-sensitive, including (crucially) empty tables. Now empty tables are a common occurrence and most people I speak to have a bunch of them, for a multitude of reasons; they’re using an ERP/CRM and aren’t using some of the modules, some of them are older and are gradually being phased out etc.

What happens over time however, is that some tables which we originally weren’t using suddenly start to populate with data, and others which were in-scope now suddenly aren’t. But because this isn’t technically a schema change and we can’t necessarily rely on the developers to know when this change will happen – how do we catch it?

The easiest thing to do in this instance is an automated check – write once, run many times. As we’ve seen from the many, many blog posts before this the answer is pretty straight forward – SQL Data Catalog’s PowerShell module once again saves the day!

(You can find the full documentation on the PowerShell module and a cmdlet reference here in the documentation.)

The Process

The most obvious thing to do is to re-scan our DB so Catalog is up to date and then fetch all of our columns first into a collection:

# Connect to SQL Data Catalog
Connect-SqlDataCatalog -AuthToken $authToken -ServerUrl $serverUrl

# Quickly refresh the scan of the Instance to get accurate row counts
Start-ClassificationDatabaseScan -FullyQualifiedInstanceName $instanceName -DatabaseName $databaseName | Wait-SqlDataCatalogOperation

# Get all columns into a collection 
$allColumns = Get-ClassificationColumn -instanceName $instanceName -databaseName $databaseName

Each of the columns returned by Data Catalog here has a number of properties that we can use:

The properties of each column object returned by the SQL Data Catalog PowerShell

All of these properties can be useful for something (and have been in the past) – in this case though the properties we care about are the id, the tags collection (name property) and the tableRowCount. The reason being that id uniquely identifies our columns so can be used to easily sort and filter on without having to match on text, the tags collection means we can search for any columns that do or do not have the Out of Scope – Unused tag and the tableRowCount lets us know if the table is or is not populated with data.

Its then fairly easy to filter these down:

# Collection for columns that are on empty tables

$emptyTableColumns = $allColumns | Where-Object { $_.tableRowCount -eq 0 }

# Collection for columns that are tagged as empty

$ColumnstaggedAsEmpty = $allColumns | Where-Object { $_.tags.name -eq $unusedTag }

# Collections for columns both tagged as empty but not, and empty columns not tagged as such

$ColumnsNowInUse = $ColumnstaggedAsEmpty | Where-Object { $_.id -notin $emptyTableColumns.id}

$ColumnsMissingClassification = $emptyTableColumns | Where-Object { $_.id -notin $ColumnstaggedAsEmpty.id}

You can easily visualize these in Data Catalog itself too:

Columns that are on empty tables, regardless of tag
Columns that are tagged as Out of Scope Unused regardless of if they have data or not

We can already see a difference in the lists – if we run the entire PowerShell however (which you can find right here in my GitHub, and I’ll include it at the end) then we actually find those differences out:

The PowerShell result: 2 classified as empty columns now with data, 3 columns not classified as out of scope, now on empty tables

So we can correct these by classifying the 3 now empty columns as now Out Of Scope – Unused, and we can change the 2 now-populated columns to be In Scope:

Suppliers is now in use, and corrected to be In-Scope
The missing Out Of Scope – Unused tags are corrected on the empty columns

…and now when we run the PowerShell again we should find everything is rectified:

Everything has been corrected, now no columns are incorrectly labelled or missing labels

Ongoing

Now we have the script we can run to identify these issues, you could implement it on the back-end of the DevOps process described by the blog post or video linked at the start of this blog post – and this would allow you to constantly be sure that your view of the Production database is accurate and up to date.

The key to a good classification process as part of on-going governance and an up-to-date view of your structured data estate is minimizing the manual effort that has to go into maintaining this process, which with the help from the Data Catalog PowerShell module – we can do easily (if we wanted a process that would go out of date after 2 weeks, we could have just used MS Excel…)

Happy Classifying!

SQL Change Automation and GitLab CI/CD (a.k.a. Oh this is fun on Windows)

“You never know what you can do until you try, and very few try unless they have to.”
C.S. Lewis

Well I don’t have to, but many of the people I speak to on a daily basis are moving into GitLab, so it’s about time I tried it! You can find here testament to the mistakes I make as I try to set up a full end-to-end database change management process with SQL Change Automation and GitLab.

Will it all work perfectly? I don’t doubt that everything will fall over at some point, but let’s see how we get on all the same, and hopefully if you’re setting up this same pipeline, you’ll be able to avoid the errors and failings I inevitably cause! So here we go!

Jan 2021 Edit: Hey guess what!? There’s a video of this too! Check it out on YouTube.

ready come on GIF

Let’s set up a GitLab Project (and rename the default branch)

Naturally, I didn’t have a GitLab account, so I had to set one up. I’m assuming that if you’re using it already or you’ve just started using it you’re taking advantage of the more business features but I’ve just stuck with the good ol’ free account for now! It was remarkably simple, sign up, email address, confirm and here we are:

Ok there is something very cool I like about setting up a new project, can you tell what it is?

You can completely set up a new blank project but they have templates, you can import projects OR, and I love this, you can setup a full CI/CD pipeline from another repo! Having done this before in Azure DevOps it was not easy, let me tell you. It really seems like Azure DevOps hates you for setting up CI/CD from an external repo, even though it has plenty of helpful ways of doing so!

So I initialized my repository with a README and updated it:

Don’t ever say I’m not descriptive enough!

The first thing I did was a renamed my default branch to ‘trunk’ by going to branches, creating the new branch and then in Settings > Repository changing it to the default and then swapping out the protected status with the outdated master:

Then finally delete the old default in Repository > Branches:

Excellent. Now it’s time to clone trunk onto my machine as we will need the local repository to put our change automation project in!

I created a folder called GitLab test and cloned the mostly empty repository into it:

Simple!

Create a new SQL Change Automation project and push it to trunk

In SSMS I opened up the most recent version of SQL Change Automation an created a new project called “DoggosAreCoolDB” using a copy of a Dev database I had lying around from a previous demonstration (BlogsDotRedgate):

Then I created my baseline as a migration script against the up-stream copy, BlogsDotRedgate_Integration, because who has access to Prod for this? Am I right? *cough & shifty eyes* not me!

I successfully generated my baseline and a change script (I added a column to a table, nice and simple) and then committed them to my local repo, and pushed! Forget branching, today isn’t about that, we’re just PUSHING TO TRUNK, WOO-HOO!

Setting up the CI/CD Pipeline

Now that we have our project and migrations in GitLab we can build out a pipeline! So first stop I went straight to CI/CD > Pipelines and was presented and I hit “Get Started”:

They immediately throw you into a Quick Start “Help” style guide which is immediately a little un-intuitive but surprisingly helpful if you read the whole thing. Effectively we need a YAML file called .gitlab-ci.yml that will store our pipeline as code telling it how and where to build, and we need a runner to actually fire up and execute these steps.

In my experience with some other CI/CD tools, it’s been advantageous to actually create the Runner / Agent first on the machines you’re going to be using, so as I just have my laptop to do this on, I will set one up on there! I found the full documentation for a Windows Runner here, and followed it just so I would have it available.

The GitLab Runner was up and running in my services but I’ll be darned if I can see them anywhere in GitLab…

confused britney spears GIF

Aha! So it turns out after a bit of digging that you need to register the runner specifically using the CI/CD section on the project settings, that was probably my bad for not reading the documentation thoroughly but my counter-argument… who actually does? So I issued the register command, applied tags and a description and chose my runner type, I chose shell because I need to be able to run PowerShell on the machine (I’ll need the SQL Change Automation PowerShell components available on the machine where the Build and Deployment are happening of course):

I’ve got the runner on the machine, I’m using an instance of SQL Server to build against, now I just need the YAML file (fortunately GitLab has full documentation for how to structure this as well!)

So I can build my project I’m going to need to know where the repo is cloned to during the process (i.e. to find the .sqlproj file) so by taking a look I managed to find a list of environment variables that can be used in the YAML file, just to be sure though, I created and committed the most basic YAML file that would just echo back the location of the cloned files:

stages:
  - DatabaseBuild

Build:
  stage: DatabaseBuild
  tags: 
   - sql
  script: 
   - echo $env:CI_PROJECT_DIR

After this let me know the environment variable worked correctly and the build pipeline was being fired up correctly on my private runner, I tried something a little more ambitious, building the .sqlproj file using the cmdlet reference from the SQL Change Automation documentation for help:

stages:
  - DatabaseBuild

Build:
  stage: DatabaseBuild
  tags: 
   - sql
  script: 
   - $projectPath = $env:CI_PROJECT_DIR
   - $project = "$projectPath\DoggosAreCoolDB\DoggosAreCoolDB.sqlproj"
   - $validatedProject = $project | Invoke-DatabaseBuild -TemporaryDatabaseServer "Data Source=PSE-LT-CHRISU\"

and we successfully built a database!

All that’s left to do now is 2 things:

  1. Create a NuGet package as part of the CI build
  2. Release the database changes to the target DB

I’m still using the same machine for the release portion too, so naturally I can use the same runner for this, if you have other servers you’re deploying to you will of course need additional runners.

We can very easily extend what we already have in our YAML file by just telling the process to create and export a new build artifact – I’m going to name it the same as everything else, and then append the BuildId to the end of the file so we always get something unique:

stages:
  - DatabaseBuild

Build:
  stage: DatabaseBuild
  tags: 
   - sql
  script: 
   - $projectPath = $env:CI_PROJECT_DIR
   - $project = "$projectPath\DoggosAreCoolDB\DoggosAreCoolDB.sqlproj"
   - $validatedProject = $project | Invoke-DatabaseBuild -TemporaryDatabaseServer "Data Source=PSE-LT-CHRISU\"
   - $buildArtifact = $validatedProject | New-DatabaseBuildArtifact -PackageId DoggosAreCool.Database -PackageVersion 1.$env:CI_JOB_ID
   - $buildArtifact | Export-DatabaseBuildArtifact -Path "$env:CI_PROJECT_DIR\Export"
  artifacts:
    paths:
     - $env:CI_PROJECT_DIR\Export\DoggosAreCool.Database.1.$env:CI_JOB_ID.nupkg
    expire_in: 1 week

You’ll notice how I’m exporting the NuGet package to the project directory and then uploading it, this is so that we’ll have access to it to release but also so that we can use the artifacts argument in our YAML to upload the file and make it a downloadable package through the GitLab interface (if you go to that SPECIFIC job):

Whilst we’re on a roll here (and things haven’t gone wrong for a while) I’m going to add 2 additional stages ALL AT ONCE to both “Create a Database Release Artifact” and “Deploy from a Database Release Artifact” using, once again, the SQL Change Automation PowerShell cmdlets.

Woo-Hoo! I’m invincible!

I broke it.

Can you see what I did wrong? The error is:

New-DatabaseReleaseArtifact : The specified value for the Source parameter is neither a valid
41database connection string nor a path to an existing NuGet package file or scripts folder:
42'CI_PROJECT_DIR\Export\DoggosAreCool.Database.1.725147351.nupkg'

So 2 fun things. 1 – I forgot to highlight there was an environment variable at one point, so it was just looking for the name of the variable in the path and 2) it keeps erroring out saying my NuGet file isn’t a NuGet file, weird right?

On further inspection it is yet another mistake I made. I’m using the job ID to name the NuGet package, which means when it tries to find the file it’s 2 steps ahead because each stage is counted as a different job! Duh!

sylvester stallone facepalm GIF

A few quick changes should hopefully sort this out! I’m going to put the instance of the pipeline ID in ($env:CI_PIPELINE_ID) and see if that makes a difference!

Wait. Did it just say the pipeline ran? SUCESSFULLY? That’s exactly what it said! We can verify that this actually happened as well by checking the DatabaseDeploymentResources folder for the Release Artifact to Integration:

And everything is there! Note you won’t have a changes.html report just yet because this is the first time we’ve successfully deployed to Integration, however if we run 1 more change through (I’ll add a stored procedure):

Boom.

Prince Harry Mic Drop GIF

Now of course we can add additional stages to this, for manual intervention or to promote to other environments, but I’m going to call it a win here and retire (until the next post) gracefully. I’m sure you’re all wondering what my final YAML file looked like too – well (counterintuitively) I’ve popped it all into GitHub for you and pasted it below. Enjoy!

stages:
  - DatabaseBuild
  - CreateRelease
  - DeployToIntegration

Build:
  stage: DatabaseBuild
  tags: 
   - sql
  script: 
   - $projectPath = $env:CI_PROJECT_DIR
   - $project = "$projectPath\DoggosAreCoolDB\DoggosAreCoolDB.sqlproj"
   - echo "Building project $project"
   - $validatedProject = $project | Invoke-DatabaseBuild -TemporaryDatabaseServer "Data Source=PSE-LT-CHRISU\"
   - $buildArtifact = $validatedProject | New-DatabaseBuildArtifact -PackageId DoggosAreCool.Database -PackageVersion 1.$env:CI_PIPELINE_ID
   - echo "Exporting artifact to $env:CI_PROJECT_DIR\Export"
   - $buildArtifact | Export-DatabaseBuildArtifact -Path "$env:CI_PROJECT_DIR\Export"
  artifacts:
    paths:
     - $env:CI_PROJECT_DIR\Export\DoggosAreCool.Database.1.$env:CI_PIPELINE_ID.nupkg
    expire_in: 1 week

CreateRelease:
  stage: CreateRelease
  tags: 
   - sql
  script: 
   - $integrationDB = New-DatabaseConnection -ServerInstance "PSE-LT-CHRISU\" -Database "BlogsDotRedgate_Integration"
   - $buildArtifact = "$env:CI_PROJECT_DIR\Export\DoggosAreCool.Database.1.$env:CI_PIPELINE_ID.nupkg"
   - echo "Creating Release Artifact for DoggosAreCuteDB - check C:\DatabaseDeploymentResources\DoggosAreCuteInc\ReleaseArtifacts\$env:CI_PIPELINE_ID\Integration for more information"
   - $releaseArtifact = New-DatabaseReleaseArtifact -Source $buildArtifact -Target $integrationDB
   - $releaseArtifact | Export-DatabaseReleaseArtifact -Path "C:\DatabaseDeploymentResources\DoggosAreCuteInc\ReleaseArtifacts\$env:CI_PIPELINE_ID\Integration" -Format Folder

Integration:
  stage: DeployToIntegration
  tags: 
   - sql
  script: 
   - $integrationDB = New-DatabaseConnection -ServerInstance "PSE-LT-CHRISU\" -Database "BlogsDotRedgate_Integration"
   - echo "Deploying changes to Integration"
   - Import-DatabaseReleaseArtifact -Path "C:\DatabaseDeploymentResources\DoggosAreCuteInc\ReleaseArtifacts\$env:CI_PIPELINE_ID\Integration" | Use-DatabaseReleaseArtifact -DeployTo $integrationDB