Classification by design: Including data cataloging in a DevOps process

“The most powerful tool we have as developers is automation.”
Scott Hanselman

It is no secret that I love to talk about data protection, specifically from the perspective of structured data. When we talk about database development practices, we often find ourselves talking about 3 things most often:

  • Source Control
  • Continuous Integration and Continuous Delivery/Deployment (CI/CD)
  • Estate Monitoring

Some people refer to this as “DataOps“, others refer to it as “DevDataOps” but in reality, it’s all DevOps guys. This may be an unpopular opinion (and if it clashes with yours please forgive me, it’s just my opinion) but just because a certain niche area hasn’t been specifically called out within a subset of DevOps doesn’t mean you have to invent your own term for it!

Now this leads me on to DevSecOps, or as I like to call it… More secure DevOps.

rimshot GIF

No but seriously this is a slightly different case – DevSecOps is like DevOps but fortified with security from the ground up. There’s a fantastic article and diagram of this on Plutora from Mark Robinson of how this looks (below) and if you haven’t read his article I would definitely go and give it a read!

DevSecOps Diagram

Good DevOps practice is a combination of different things working together, bringing the right mentality, the principles, processes and amazing tools at our disposal like automation but this all includes security from the ground up too. DevOps is about putting those principles and practices in place to strengthen the pipeline, so why don’t we treat security in the same way?

Take, for example, 3 pieces of legislation that have been very much in the spotlight:

The controller shall implement appropriate technical and organisational measures for ensuring that, by default, only personal data which are necessary for each specific purpose of the processing are processed.
GDPR (Europe) Art. 25 “Data protection by design and by default”

Processing agents shall adopt security, technical and administrative
measures
able to protect personal data from unauthorized accesses and accidental or unlawful situations of destruction, loss, alteration, communication or any type of improper or unlawful processing.

– LGPD (Brazil) Chapter VII, Art. 46 “Security and Secrecy of Data”

“A Controller or Processor is required to implement appropriate technical and organisational measures to demonstrate that Processing is performed in accordance with this Law…”
DIFC LAW NO. 5 OF 2020 (Dubai) Part 2D, Art. 14 (2) “Accountability and notification”

There’s a common running theme here and although lots of global legislation will either allude to, or directly tell you ways you can be compliant and what some of these “organizational” and “technical” measures are, it’s still pretty blurry.

How do we know what we can do? How do we know what “default” and “design” mean in this context? Well, we build it into the DevOps process.

Now I could sit here forever and talk about why transforming your database development, deployment and provisioning processes allows us to be more secure, but that’s a lot of material and it might have to come in chunks! So what we’re going to focus on today is as the title suggests: Data Classification and Cataloging.

Why is Cataloging important?

Cataloging structured data is incredibly important because it can be one of the first steps we take to securing sensitive Personally Identifiable Information (PII) or Protected Health Information (PHI) wherever it exists across our database environments. It allows us to make strengthened, contextual decisions about the data we hold including how we treat it in pre-Production, how long we retain it for and which systems and processes consume it.

But the most important part of this is simply: it tells us where the risk is.

Read through any of the most recent data protection laws and you will notice that a few things come up quite a lot including “Data Protection Impact Assessment“, or DPIA. Effectively if you can assess the risk of processing activities you can more readily answer the data protection questions and challenges you may face.

Knowing where your data resides can be the first step to helping you assess this risk, and to more readily answer your own data questions. If you want to read more about Cataloging specifically and why it is useful, you can read more about it on my previous blog here.

Where does Cataloging fit into DevOps?

This one is simple to answer. Once you have fully classified your entire estate, you’re not done. No, if you’re a development house or indeed even a single developer – if you are making any schema changes to the tables holding that sensitive data, you’re never done.

The reason for this is that Cataloging is an evergreen activity – if you update tables by removing columns, adding columns, splitting tables, adding tables… anything! Well then you need to be ready to make sure that you are:

a) Prepared and equipped with knowledge of the tables you’re working on and if this is a high risk activity.

b) Updating classification information to reflect the new “truth”, i.e. if you’re adding a column that will collect people’s Twitter handles, then that column should be classified as sensitive, and this should be reflected the moment it is deployed to Production.

So it is important to have the correct people working on this, with the right knowledge, preparation and processes and using the correct tools ensuring that those updates are persisted properly and securely through your deployment pipelines.

Huh… people, processes and tools… That sounds familiar!

The Process: SQL Data Catalog, SQL Change Automation and Azure DevOps

For this little experiment of mine I used Redgate’s SQL Change Automation (Migrations First approach in SQL Server Management Studio) and SQL Data Catalog to both develop & deploy and classify/categorize respectively, and for simple version control and orchestration of this pipeline I opted for Azure DevOps (with SQL Change Automation CI/CD plugins):

NOTE: Heads up, all of the code I used for this can be found right here in my GitHub, feel free to have at it!

Step 1) Setup SQL Change Automation project with an Azure DevOps Git Repo and then create the YAML file to build it, and create a deployment process to Acceptance and the Production.

Ok – that’s a bit of a mouthful and a massive ask. There’s a lot of things there you have to be familiar with, but we don’t have time to go into right here. BUT fortunately if you checkout Redgate University right here, my blog post on using Change Automation with Azure DB and Redgate’s Product Learning section, you’ll be a Database DevOps ‘Whizz-Kid’ in no time!

Fast forward a little and I had my example databases, VCS and pipeline all up and running:

Step 2)The “Theory”: This is where things get interesting. So we have an example pipeline set up and we are able to completely deploy all the way through to “Production” so let’s talk theory.

In SQL Data Catalog I have covered both my Production and Acceptance Databases:

Now, in development we don’t make changes directly to Production, so why should Classification be any different? Now how you adapt the above code is up to you, feel free to split it, move it around, incorporate it into Pull Requests if you want to… But I’m going with a bit more of a simple situation.

Situation: Developer makes a change in Development, which gets committed, reviewed and merged o the main branch, resulting in a build and a deployment, in this case to Acceptance and then it is later deployed to Production.

Now, by Acceptance we should only have the “good work”, i.e. all of our testing is shifted left within DevOps so Acceptance is basically the last stop before Production. Therefore we should classify the work we have done on Acceptance, crucially, before it gets to Production and starts gathering sensitive data, and then copy this classification up on deployment.

Ideal: We should have no columns on Production that have not been classified.

Step 3) – In Practice: Fortunately it’s very easy to automate a lot of these steps with SQL Data Catalog utilizing it’s PowerShell cmdlets and REST API. The cmdlets are fully documented and very easy to use (docs here). This allows us to easily scan, classify and copy classifications up to other databases, but we’ll also need to do some checks and report if there are discrepancies, as part of the deployment pipeline that can be investigated.

  • Are there any columns on Acceptance that aren’t classified but have been deployed to Production? (failure to comply with process)
  • Are there any columns on Production that have not been classified? (classification drift)
  • Are there any unclassified columns on Acceptance that have not yet been deployed to Production (for pipeline hygiene purposes)

The other part of this ‘fun’ is reporting what has been changed in the same process. Now fortunately SQL Change Automation spits out a Changes.json file with its Release Artifacts and we can steal that away and find out how many tables have been created or changed in this release and report that back so we can correlate what has been done and what is missing:

So actually getting this up and running is just going to require 3 things:

  1. The PowerShell script from GitHub (or your own personalized variant) as a step in your production deployment
  2. Data Catalog available and pointed at Acceptance and Production (or your versions of these environments)
  3. Variables set in Azure DevOps to fill the gaps (e.g. Where is Data Catalog? Whats my PowerShell Auth token? What are my Acceptance and PROD DBs called? etc.)

3 is the last step there so you’ll need something like this to run the script:

  • DatabaseDeploymentJSON – where the JSON file will be with the latest changes in the Prod release
  • DataCatalogAuthToken – Your PowerShell Auth token from Settings in Data Catalog
  • DataCatalogUrl – The full URL to your Data Catalog installation, missing the “\” at the end (ending :15156)
  • ExportPath (Optional) – I specified the path for my Database Deployment Resources to save typing it out in the Redgate plugins
  • ProdDB / StageDB – As you would expect, the Production and Acceptance/Staging DBs you’re deploying to/from
  • ProdInstance / StageInstance – As above, except the instance the Database are located on

In the variables above the Instance and DB names are purely used within Data Catalog, so there’s no need to worry about anything happening to the actual databases themselves!

Once you’ve run through the deployment pipeline a couple of times and the changes.json file is being produced, you can go ahead and copy the script into an inline PowerShell script step in your release and you should find it will fire to life! I simulated an example by modifying my Contacts table and my Articles table, adding 1 column each and deploying both to Acceptance. I then classified just 1 of these in Acceptance in Data Catalog:

and then approved the deployment to Production and tada!

Ok you probably can’t make all that out, but it effectively says:

(Information) Table dbo.Articles was modified in this deployment.
(Information) Table dbo.Contacts was modified in this deployment.

That much we knew!

1 column(s) with classifications were discovered on VoiceOfTheDBA Acceptance that are not classified in VoiceOfTheDBA Production:
dbo.Articles.TestingPineapple

Excellent, we classified that one so it gets copied up and we can verify that in data catalog against Production:

and finally, we get a warning about Production now containing unclassified columns:

(Alert) The following columns have been discovered on VoiceOfTheDBA Production that require classification:

dbo.Contacts.TestingPineapple

You should classify these columns in VoiceOfTheDBA Acceptance prior to the next deployment.

Just as we expected. Success!

Happy Tom And Jerry GIF

Conclusion

Classification and categorization belongs as part of DevOps, if you expect the context for your business decisions around data to remain evergreen and informed then it cannot sit on the shoulders of one or two people to support it, and it cannot live in a manually updated Excel sheet or document.

By including it within the DevOps process, not only do you add an additional layer of security but you also make it an automated, team activity that can be audited, checked and easily kept up to date.

Is this DevSecOps? Well… not really no. Is this a more secure approach to Database DevOps? Absolutely! Happy DevOpsing!

(SQL) Change ALL the Azure SQL Database Automation!

“But I can hardly sit still. I keep fidgeting, crossing one leg and then the other. I feel like I could throw off sparks, or break a window–maybe rearrange all the furniture.”
Raymond Carver

I understand that starting off a blog about Azure SQL Database with the above quote is a little weird, but honestly I’m _really_ excited about what I’m about to tell you.

***Note before starting: This blog post assumes you’re familiar with the concepts of Database Source Control, CI and CD, Azure SQL Database and pipelines within Azure DevOps, otherwise here be dragons.***

I am a huge fan of SQL Change Automation – mostly because of the migrations functionality. In my mind it represents an ideal workflow for making complex SQL Server database changes. If you’re not sure about the different models (State, Migrations, Hybrid), take a look at my blog post from last week here! But until this time it has had one thing that I could not easily do with it… Platform as a Service, Azure SQL DB.

Now don’t get me wrong, SQL Change Automation could easily deploy to Azure SQL Database but I had a problem. The words:

Chris how do we benefit from the migrations approach and put the shadow database and build db in Azure SQL too? We don’t have any local instances or VMs we can use for this and Dev, Test and Prod are all in PaaS!”

elicited this response:

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But. No. Longer.

Now for those of you who don’t know, the _SHADOW_ database that SQL Change Automation creates is effectively a schema and static data only copy of your database, and it is dropped and built each time you verify, to ensure that all of the migrations run successfully and you can effectively check your work and shift the build left (!!), before you even check into source control.

This shadow database and the build database shared one thing in common and that was that you couldn’t build them in Azure SQL DB, which left 2 choices:

  • Use an instance of SQL Server. Developer for the shadow locally maybe; a VM in Azure or on-prem hosted instance for building
  • [For build specifically] Use localDB. Not advisable if your database contains any objects not supported by localDB because (juuuust in case you didn’t know) it is SQL Server Express.

But on May 12th 2020 (and I only found out about this like 2 weeks ago) the SQL Change Automation team at Redgate released version 4.2.20133 of the plugin for SSMS which included a few super cool things like additional Azure SQL support and the Custom Provisioning Scripts feature.*

excited excitement GIF

Now this is great because not only can we now easily create SQL Clones to be used as the development source (and I’ll blog about THAT a little later) but of course you can use it to use an Azure SQL DB for the shadow AND to use a persistent Azure SQL DB for the CI build as well!

Now unfortunately Kendra kinda beat me to the punch here and she produced a fabulous 3 part video series you can watch on using SQL Change Automation solely with Azure SQL DB, and you can view those here if you don’t want to see me try it out:

Getting set up

The first thing I did was make sure that I had all of the necessary environments to try this out – I created 3 Azure SQL Databases to mimic Development, Build and Production environments on 2 separate Prod and Non Prod Azure SQL Servers. I ran the DMDatabase prep scripts (you can find these here) to setup Dev_Chris and Production, but left BuildDB empty.

Next It was time to create my project, so I hopped over into Azure DevOps and created a new project, initialized it with a README and then Cloned it down onto my local environment:

Everything was ready to go so it was time to create my project!

Setting up SQL Change Automation in SSMS

*cough* or if you’re me, update it first because you’re on a REALLY old version *cough*

Then I hit “Create a New Project” and it allowed me to just specify the connection string to the Dev Azure SQL DB and the project location was the checked-out local repo:

Didn’t change any of the options because I’m a rebel and I didn’t feel like filtering anything out! But of course now comes the fun bit… the baseline. I chose my production Azure SQL DB as it’s my only upstream DB at this point, and it’s time to hit “Create Project”.

…and Huzzah! It’s worked and we’re all good!

excited andrew garfield GIF by The Academy Awards

Now… that’s actually not the best bit! The reason why Andrew there is clapping so hard? Well that little piece of magic has happened in the background! A Shadow database has actually been created for me against my azure server automatically! This is done by using the connection string that is used for dev!

Now… one thing to check, and I didn’t think to do this, but you can specify the connection string in the SQL Change Automation user file but I just left mine for a bit not realizing it created an Azure SQL DB for the Shadow that was CONSIDERABLY higher tier than my dev environment (bye bye Azure credit!), but fortunately I was able to scale it down quickly to basic and that has stuck, but be warned!

So I did what all ‘good devs’ would do now… I committed and pushed my initial commit directly to my main branch! (Don’t tell my boss!)

and safely sat my Database in Azure DevOps:

Setting up the build and deployment stages

This bit was actually just as easy. I used to hate YAML but thanks to a certain (wonderful) Alex Yates I jumped in anyway and it turned out to be just fine!

I created a new basic YAML file within Azure DevOps (and used the assistant to just auto populate the Redgate defaults, if you don’t know YAML or what it can do already, there’s a really good MS article here) and committed it to the main branch again (whoopsie) and the only component was the SQL Change Automation plugin I pulled in from the Azure DevOps marketplace, and I configured the build to target my “nonprod” server and the Build DB I had created previously.

On saving and running the pipeline succeeded!

All that was left to do was to create a Release Pipeline. So naturally, I jumped straight in and created a new pipeline, and I started with an empty job and called it Production*note* make sure you also choose your Build artifact before configuring your release stage too by clicking the Add an Artifact option!:

I added the SQL Change Automation: Release step to the agent job (note because this is all hosted, I’m using an Azure DevOps hosted agent to do this step):

Now you’ll need to add 2 stages (both the SQL Change Automation: Release plugin) at this point, a “Create Release” and a “Deploy from Database Release Artifact” because one will look at the target and figure everything out for you, and you’ll be able to review exactly what will be deployed, and the other will actually _do_ the deployment:

From here you just have to specify the options available, like in this wonderful walk-through here from the fabulous Chris Kerswell of DBAle fame! For me, this was simply targeting my Production Azure SQL Database.

You’ll definitely want to use the project variables to pick up the right package, and also leave the export path blank in both steps for now:

You can Clone the step by right clicking instead if you want to which will preserve all the connections you’ve already provided! Then once it’s all pointed at the right place, save and queue the release!

And of course, we were successful:

and then finally with a couple of triggers set to automatically build and deploy I made a change to my Contacts table in my Azure SQL Dev DB and a few minutes later, thanks to Azure DevOps and Redgate SQL Change Automation the very same change appeared in Production, with no reliance on anything other than Azure SQL DB and SQL Change Automation:

Before the DevOps process on Dev, ready for a migration to be generated
After: Automatic post-build deployment of the new column to the Production Azure SQL Database

Conclusion

If you have all of your databases in Azure SQL Database**, fear not because SQL Change Automation to the rescue! You can very easily set up and configure a pipeline in Azure DevOps or indeed any pipeline of your choice, but it’s never been easier to persist development changes all the way through to Production in a low risk, incremental, “DevOps” way!

—NOTES—

*An important word from the release notes: Note that it is still generally recommended to locate the shadow database locally where possible as that will usually result in a faster database connection. The default CreateDatabase.sql and DropDatabase.sql scripts can be altered to improve performance or implement custom provisioning logic.

**If you have all of your Databases in Azure and you need them masked for Dev/Test too, check out this previous blog post in which I outlined how to do that using Azure DevOps too!