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DevOps and DataOps for Snowflake with DBT and Azure DevOps

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by John L. Aven and Prem Dubey

ELT — Extract, Load, and Transform has become added accepted over the aftermost few years. This approach, in part, has been apprenticed by the growing acceptance of billow abstracts warehouses, such as Snowflake which our audience are application not alone for new billow abstracts warehouses, but additionally as the top SaaS, cloud-native band-aid to drift from bequest abstracts warehouses such as Netezza, Teradata, Exadata, and acceptable Big Abstracts platforms.

Furthermore, with the accretion acceptance of DevOps and DataOps practices to access acceleration and abate risk, it has become adorable to be able to accommodate the modification and deployment of changes in a abstracts barn through a CI/CD activity technology such as Jenkins or Azure DevOps.

With that mind, we will do our best in this cavalcade to accommodate you with:

While Snowflake provides an incomparable billow abstracts warehousing acquaintance with a multi-cluster, accumulated abstracts architectonics that separates accumulator from compute (focus on billow abstracts warehousing), DBT is a game-changing access to managing ELT workloads (orchestration and administration of processing pipelines). DBT provides a altered akin of DataOps functionality that enables Snowflake to do what it does able-bodied while abstracting this charge abroad from the billow abstracts barn service.

DBT brings the software engineering apple to the SQL-savvy ELT developer. It treats SQL queries as models — aka a SELECT calligraphy aural a package. All models — in DBT — represent tables and angle aural the abstracts warehouse. These models can be versioned, and their action and abstracts can be activated — abundant as one would address assemblage tests for a allotment of software code. The models are affiliated by references to anatomy a DAG (Directed Acyclic Graph) — a actual accepted accretion archetypal begin in abounding accepted data-centric accoutrement (Spark, Airflow, Tensorflow, …). A DAG represents the adjustment of concern execution, as able-bodied as the birth of abstracts as generated through the models.

Everything in DBT is one of three types of files — .sql, .yml, .md. Working in about-face order:

After installing DBT via pip (or as we prefer, pipenv) a new activity is created with ‘dbt init’.

A DBT activity can accept abounding altered ‘modules’, and a cardinal of these are accent below:

This agenda will accommodate the .sql files that accomplish up the ELT pipeline(s). They are consistently a baddest annual and aftereffect in the conception of a table, appearance or brief (temp) table of the abstracts aback executed. They anatomy a arrangement of beheading stages by referencing added models.

These are .sql files that are acclimated to assassinate custom tests on data. For example, if you appetite to accomplish abiding a allotment of ethics in a assertive cavalcade is aural a assertive range, you would address a archetypal that would validate this acceptance on the consistent model.

These are .sql files that are templatized with Jinja. The abstraction is for these models to be awful reusable (think DRY — Don’t Repeat Yourself) and are important in a abounding CaC solution. In a software engineering solution, these are the functions acclimated to body bigger solutions — and sometimes they too are alleged macros.

These are models which do not run aback you arrange the project. Instead, they are advised to be ‘on-demand’ queries that are best frequently associated with specialized angle or tables acclimated in analytics solutions — such as a abstracts science pipeline.

These are markdown files (with adapted decorations — see official documentation) that are acclimated to certificate models. These are alternative — but should be acclimated in any assembly environment.

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These are run logs. They are generated aback a ‘dbt run’ command is executed.

Either aback compiling, active or architecture documentation, these are created. They accommodate all of the meta agreeable and accumulated SQL code.

Not all apparatus apparent aloft accept been acclimated in the demo, nor are all types of functionality addressed. If you would like to apperceive added about the functionality accurate in DBT amuse advertence the official documentation.

To use DBT on Snowflake — either locally or through a CI/CD pipeline, the active apparatus should accept a profiles.yml aural the ~/.dbt agenda with the afterward agreeable (appropriately configured). The ‘sf’ contour beneath (choose your own name) will be placed in the contour acreage in the dbt_project.yml.

In this section, we’ll booty a attending at the alternation of transformations — these can be begin aural this git repo.

The botheration we are acclamation uses the TPC-H criterion datasets. The ambition is to actualize an analytics appearance of the abstracts with aggregations of:

These three ethics will be accumulated into a table with columns absolute the afterward information: Supplier Name, Supplier Annual Balance, Chump Annual Balance, Allotment Accessible Quantity, Accumulation Amount for anniversary Allotment and Supplier Pair, Allotment Name, Allotment Manufacturer, Allotment Brand, Allotment Type, Allotment Size, Nation, Region, Market Segment and Chump Name.

To accomplish the adapted appearance of the data, assorted approaches could be taken. But, in adjustment to authenticate the functionality of the pipeline, we do so as follows:

SELECTs_suppkey AS supplier_key,s_name AS supplier_name,s_nationkey AS nation_key,s_acctbal AS account_balance,ps_availqty AS available_quantity,ps_supplycost AS supply_cost,p_partkey AS part_key,p_name AS part_name,p_mfgr AS part_manufacturer,p_brand AS part_brand,p_type AS part_type,p_size AS part_size,p_container AS part_container,p_retailprice AS part_retail_priceFROM supplier_parts

2. average_account_balance_by_part_and_supplier — compute an boilerplate accession of the supplier annual antithesis by allotment and supplier

SELECTr_name AS region,n_name AS nation,n_nationkey AS nation_key,c_name AS customer_name,c_acctbal AS account_balance,c_mktsegment As market_segmentFROM customer_nation_region

3. average_supply_cost_and_available_quantity_per_part — compute the boilerplate accession of the accumulation amount and accessible abundance by allotment for the supplier

4. supplier_parts_aggregates — accompany the assorted aggregations aback to the supplier_parts table

5. customer_nation_region — accompany the CUSTOMER, NATION and REGION tables by the NATIONKEY

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SELECTr_name AS region,n_name AS nation,n_nationkey AS nation_key,c_name AS customer_name,c_acctbal AS account_balance,c_mktsegment AS market_segmentFROM customer_nation_region

6. average_acctbal_by_nation — compute an accumulated boilerplate of the chump annual antithesis over nation field

7. customer_nation_region_aggregates — accompany the accumulated annual antithesis aback to the customer_nation_region table

8. analytics_view — accompany of customer_nation_region_aggregates to supplier_parts_aggregates. Conceptually, this is to be acclimated for either a BI Dashboard or a Abstracts Analytics/Science workstream.

The aloft alternation of transformations anatomy the DAG (Directed Acyclic Graph) below. This graph, as apparent below, demonstrates the lineage, or ancestry of the alternation of transformations. This is a advantageous decision to accept the alternation of ‘transformations’ that resulted in the assorted tables/views throughout the transformation process.

Filters are activated throughout the assorted stages in adjustment to agreement the adapted columns are present.

Steps 1, 2, 3, 5 and 6 actualize as brief tables with accomplish 4 and 7 as tables, and 8 as a view. And, although the band-aid isn’t advised to be deployed from your console, you could download the antecedent cipher and via the afterward commands actualize all of the affidavit and arrange the affidavit server.

In the spirit of bringing software engineering practices to the SQL-centric ELT abstracts warehousing world, any avant-garde access would be abridged after automating the testing and deployment action and amalgam with a CI/CD process. In accession to bounded execution, we’ll analysis the ELT activity (DBT) that has been chip with Azure DevOps.

To run the activity locally, you can use Pipenv to bureaucracy DBT and the scripts in the Pipfile to assassinate assorted tasks. Pipenv is the “new kid on the python block” that brings the best of all the packaging worlds to Python. It creates a basic ambiance while managing dependencies and scripts application a Pipfile.

Below are the accomplish to bureaucracy Pipenv and the accurate commands for this project:

To set up CI/CD for this project, you can use Jenkins or Azure DevOps. We are activity to altercate Azure DevOps here, but for Jenkins, booty a attending at the Jenkinsfile in the Jenkins annex of the repo.

Azure DevOps is the rebranded and cloud-native adaptation of Visual Studio Aggregation Casework (VSTS). It is a accumulating of casework for DevOps enablement, and it currently offers:

To get started with Azure DevOps, you’ll charge an Azure account. Once you accept that setup, login to the Azure aperture and chase for Azure DevOps in the chase tab at the top. Once redirected to the Azure DevOps page, you’ll actualize an organization. After creating the organization, you can actualize a project, and allure added associates of your aggregation to the activity for collaboration.

To automate the flow, you can deeply move the ~/.dbt/profiles.yml in the abettor area the deployment will be running. Go to Library in Pipelines and add upload your profiles.yml as a defended file. You can download this book while active the deployment.

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Azure Activity food the activity as cipher in the azure-pipelines.yml book which uses YAML action reference.

Let’s booty a attending at our azure-pipelines.yaml

The activate alembic is acclimated to ascertain the branches which you plan to deploy.

The basin alembic is the abject VM angel we are activity to use — Azure Pipelines currently supports images for MacOS, Ubuntu, and Windows.

If we appetite to run our deployment in a docker container, we can use the alembic block to ascertain the name of the angel and arguments.

Steps are the assorted stages of the pipeline, and you can accept assorted accomplish in the pipeline. Next, you will charge to download the profiles.yml book from the Library to the abettor container. Use DownloadSecureFiles assignment to download the book — you charge download it in your Azure DevOps Alignment afore you can use it. Once you accept downloaded the file, move it to the ~.dbt agenda as profiles.yml so the DBT on abettor can aces it up.

You can either accept DBT prepackaged in your docker container, or if you appetite to use the latest version, you can aloof download it as allotment of the deployment action application pip install dbt . You charge to set /home/vsts_azpcontainer/.local/bin to $PATH as pip installs dbt in this agenda by default.

Once all of this is done, go to the activity agenda and assassinate the appropriatedbt commands. It is appropriate to accumulate compile, test, and run in altered stages in that adjustment so that the activity break if there is an error.

While dbt is about invoked from the command line, it is accessible to acceptation and adjure dbt as a Python module. You can additionally use prefect, Airflow, dbt Cloud, and added chart accoutrement to assassinate the activity on custom schedules for your dbt project. Since, dbt has an API for Python, you can additionally run it as a serverless action with Azure Functions or AWS Lambda.

More avant-garde DataOps approaches could be implemented — e.g. confined the dbt affidavit server. However, back activity abundant added would absorb architecture out basement as code, we’ll save that for a after post.

Hopefully, we’ve helped access your compassionate of the capabilities of DBT as a apparatus for DataOps, accustomed you a bright aisle for creating an ELT Abstracts Activity in Snowflake, and approved automating activity deployment with Azure DevOps.

Usingdbt as a key allotment of your DataOps band-aid to architecture and assassinate your ETL processes in Snowflake brings SQL abstracts pipelines to the software engineering apple of DevOps.

If you’d like added abetment with Snowflake, DevOps, or DataOps, Hashmap offers a ambit of enablement workshops and appraisal services, billow clearing services, and consulting annual bales as allotment of our Snowflake annual offerings — we would be animated to assignment through your specific requirements — amuse ability out.

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