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Running DBT for old transform project

This document describes how to setup and execute common DBT tasks for both DBT projects:

This document will present the alternatives for both projects.

Note that transformations are gradually migrated to mattermost-analytics project. New data transformations must be implemented in the mattermost-analytics project.

Prerequisites

  • Docker & docker compose
  • Snowflake access with proper roles (ask the team which role to use)

Setup

Copy .dbt.env.example to .dbt.env. Edit the file and replace placeholder with appropriate values.

DBT via docker

There are two make targets for starting a shell with dbt and its dependencies preinstalled.

For snowflake-dbt project run:

make dbt-bash

For mattermost-analytics project run:

make dbt-mattermost-analytics

Both options allow using dbt directly from a bash shell.

To test that everything is working as expected, try generating dbt docs:

dbt docs generate -t prod

If the output looks similar to:

...

07:28:30  Found 289 models, 10 tests, 0 snapshots, 0 analyses, 407 macros, 0 operations, 15 seed files, 263 sources, 0 exposures, 0 metrics
07:28:30  
07:29:00  Concurrency: 8 threads (target='prod')
07:29:00  
07:32:04  Done.
07:32:04  Building catalog
07:44:19  Catalog written to /usr/app/target/catalog.json

then the setup is working.

Overriding profile

Profile overriding might be useful if you want to experiment with the project structure. Day-to-day operations shouldn't require it.

All dbt commands that are part of the Makefile use by default profiles under transform/snowflake-dbt/profile. This directory can be overriden by setting DBT_PROFILE_PATH environment variable:

DBT_PROFILE_PATH=/path/to/profile make dbt-bash

Note that DBT_PROFILE_PATH can use either absolute or relative paths. For relative paths, the base is the build directory of this project.

Common DBT operations

The following examples describe common operations in the development lifecycle. For more detailed documentation, please check DBT's documentation.

All commands require that they are executed in a properly configured dbt shell. Check the instructions above for details.

Compile models

To compile the models from DBT to SQL statements, use the command.

dbt compile -t prod

This command doesn't run anything against the database. It should be useful whenever

The resulting SQL files will be available under target/compiled/mm_snowflake.

It is also possible to compile specific models by using the proper selectors. The following command will compile only models with tag "nightly":

dbt compile --select tag:nightly -t prod

Run project against production

Running against production from your local machine is not advised. Changes performed to production database won't be tracked. It also increases the risk of accidentally running code that hasn't been approved. However, there are cases where manual execution is required (i.e. triggering full refresh). Please treat with caution.

To run the project against production, use the dbt run command. The following example will run all models tagged with nightly tag.

dbt run --select tag:nightly -t prod

Building docs and serving them locally

At the home of the repo run:

For snowflake-dbt project run:

make dbt-docs

This command will generate the docs and serve them at http://localhost:8081.

For mattermost-analytics project, connect to the shell (i.e. via make dbt-mattermost-analytics) and run the following command:

dbt docs generate -t prod && dbt docs serve --port 8081

Docs are also available in Cloud DBT as artifacts of each build.