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How to Use CI/CD to Arrange Your Snowflake Database Scripts with dbt in Azure DevOps

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by Venkatesh Sekar

I afresh wrote about the charge aural our Snowflake Billow Abstracts Barn applicant abject to accept a SQL-centric abstracts transformation and DataOps solution. In my antecedent post, I stepped through how to actualize tables application custom achievement with Snowflake.

Continuing in that vein, I was afresh asked by a chump to accommodate a aisle for them to do database DevOps for Snowflake. In general, database DevOps has complex absolutely a bit of complication and advancing tweaking to try and get it right. There are some accoutrement accessible in the bazaar today including:

But, added than Sqitch, they don’t abutment Snowflake yet, although, with the bulk of drive that Snowflake has in the market, I apprehend they will accommodate abutment in the not too abroad future.

Having acclimated dbt as a abstracts transformation and Jinja template-based tool, I was absorbed to see if it could potentially be the key to advice alleviate database DevOps for Snowflake.

As acclaimed above, I was able to actualize the ‘persistent_table’ achievement which provided an acknowledgment for creating ‘source tables’ in DBT, and accepting done that I abutting developed a simple CI/CD action to arrange database scripts for Snowflake with dbt in Azure DevOps.

Stay with me and I’ll footfall you through how to bureaucracy dbt to arrange the scripts. As always, the cipher is accessible in my git repo venkatra/dbt_hacks.

Here is a glimpse into the accoutrement and solutions that I am application to accomplish this happen…

dbt is a command band apparatus based on SQL and is primarily acclimated by analysts to do abstracts transformations. In added words, it does the ‘T’ in ELT.

It facilitates autograph modular SQL Selects and takes affliction of dependencies, compilation, and achievement in run time.

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Azure Devops provides developer casework to abutment teams in planning work, accommodating on cipher development, and architecture and deploying applications.

Organizations beyond industries await on Snowflake for their billow abstracts warehousing needs — net new abstracts warehouses, migrations from bequest DW accessories (Netezza, Teradata, Exadata, etc.), and migrations from acceptable Hadoop and Big Abstracts platforms (Hive, HBase, Impala, Drill, etc.). Our audience are additionally application Snowflake for high-value band-aid areas such as Security Analytics and Billow Visibility Monitoring.

Snowflake is absolutely relational ANSI SQL billow abstracts barn and allows you to advantage the accoutrement that you are acclimated to and accustomed with while additionally accouterment burning elasticity, per additional consumption-based pricing, and low administration aerial beyond all 3 above clouds — AWS, Azure, and GCP (GCP is in clandestine preview).

The Continous Integration (CI) action is accomplished application Azure Pipelines aural Azure DevOps. This activity is about invoked afterwards the cipher has been committed, and the activity tasks about handle:

In the case of a database scripts book there isn’t a abundant accord of validation that can be done, added than the following:

Snowflake currently does not accept a apparatus that validates the calligraphy afore execution, but it can validate during deployment, so in the Body appearance I about do these checks:

Given the set of all scripts, it’s capital to actuate which scripts were added or updated. If these scripts can’t be identified, you will end up re-creating the absolute database, schema, etc. which is not desired.

To break this issue, I’ll use the Azure DevOps Python API. Going through the docs, you will see altered REST endpoints and actuate abundant advice on what was committed, back it was committed, and who committed it, etc.

The python calligraphy IdentifyGitBuildCommitItems.py has been developed in acknowledgment to this. Its sole purpose is to get the account of commits that is allotment of the accepted body and their artifacts (the files that were added/changed). Once articular it would address them into a book ‘ListOfCommitItems.txt’ during execution.

I’ll analysis the after-effects in the beneath sections.

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During the advance of development, the developer ability accept created scripts for table conception as able-bodied as developed transformation models, markdown documentation, carapace scripts, etc. The ‘ListOfCommitItems.txt’ that was created beforehand would accommodate all of these scripts. Note that if a book was committed assorted times, the calligraphy will not de-dup the commits.

To accumulate things modular, the calligraphy FilterDeployableScripts.py was created. Its responsibilities are to:

The body activity is a alternation of accomplish and tasks:

These are captured in azure-build-pipeline.yml

The afterward screenshot highlights the account of artifacts that get appear by the build. It additionally provides a sample achievement of ‘ListOfCommitItems.txt’ which was captured in the antecedent run.

Notice that the ‘DeployableModels.txt’ book contains alone the CONTACT table analogue file, and ignores all added files that are not meant to be run.

Now booty a attending at the abutting screenshot from a altered body run — during this body run we saw the following:

Again, the ‘DeployableModels.txt’ book contains alone the ADDRESS table analogue book and is not anxious with any added files that are not meant to be run.

A Continuous Deployment (CD) action is accomplished with Azure Absolution Pipelines. The activity we are alive on is geared appear the absolute deployment to a specific snowflake environment, e.g. Snowflake Development Environment.

The “Stage” area is usually specific to the ambiance in which the deployment needs to happen. It consists of the afterward tasks as apparent below:

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This is a Bash assignment with inline cipher below:

This is a Bash assignment with inline cipher below:

This sets up the assorted env configurations bare for dbt and acclimated as allotment of the execution.

The ‘ENV_’ are variables that will be commissioned at run time. They charge to be authentic in the capricious area as below:

Upon release, the table will be created in Snowflake. Here is a screenshot of the acknowledged run and the logs of the DBT_RUN task:

and beneath is the antiquity in Snowflake:

Keep these limitations in apperception back leveraging dbt for CI/CD with database objects…

I achievement you’ll try to carbon this simple CI/CD action to arrange database scripts for Snowflake with dbt in Azure DevOps. While there are some limitations, the abeyant is there to add amount to your abstracts ops pipelines.

You should additionally analysis out John Aven’s contempo blog column (a adolescent Hashmapper) on Application DBT to Execute ELT Pipelines in Snowflake.

If you use Snowflake today, it would be abundant to apprehend about the approaches that you accept taken for Abstracts Transformation, DataOps, and CI/CD forth with the challenges that you are addressing.

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