dbt for Snowflake Demonstration Project

Dependencies

This project depends on the following two data sets

• SNOWFLAKE_SAMPLE_DATA that is available by default in all new Snowflake accounts
• Cybersyn Financial & Economic Essentials
  • It is available for free in the Snowflake Data Markeplace
  • If named other than "CYBERSYN_FINANCIAL__ECONOMIC_ESSENTIALS", update the database name in sources.yml

How to get started

Install Python and Create a Virtual Environment For dbt

• We generally recommend Anaconda or Miniconda to create an isolated environment just for dbt. A dbt-conda-env.yml file has been provided so you can set up this environment and switch to it with:

  conda env create -f dbt-conda-env.yml
  conda activate dbt

• You can also use venv with the open source version at Python.org

  • Unix/macOS - Setting it up and verifying which python you are now using:

    python3 -m pip install --user virtualenv
    python3 -m venv dbt
    source dbt/bin/activate
    which python
    python3 -m pip install -U dbt-core dbt-snowflake

  • Windows - Setting it up and verifying which python you are now using:

    py -m pip install --user virtualenv
    py -m venv dbt
    .\dbt\Scripts\activate
    where python
    py -m pip install -U dbt-core dbt-snowflake

Set Up Your Editor

• Most dbt users edit their dbt scripts with Microsoft's free editor, VSCode
  • Download VSCode
  • From the Extensions screen (icon looks like Tetris) you should install two extensions
    • "Snowflake"
    • "dbt Power User" (which will also add "vscode-dbt")
  • In the Explorer, right click in the background and "Add folder to workspace" to add where your dbt project will be located.
  • On Windows, you will want to change the default terminal to "Command Prompt". Under File -> Preferences -> Settings, search for "windows terminal" and scroll down to where it says the default is "null" and change that to "Command Prompt".
  • You will want to set the default intepreter to your new "dbt" environment using these instructions from Microsoft.

Set Up Your Snowflake Account

• Create a target schema in Snowflake that you want to deploy your dbt demo into
• Add the Knoema Economy Data Atlas and Snowflake Sample Data to your account if necessary

Update Configuration For Your Account and Test Execution

• Copy the sample profiles.yml file to your ~/.dbt/ folder and update it with your credentials and target DB/schema
• From the root folder, run dbt deps to download modules from the dbt hub
• Run dbt build --full-refresh and troubleshoot any errors such as missing objects or permission issues

Cheatsheet of most common dbt commands

• dbt deps - download 3rd party packages (necessary for this project before build)
• dbt build - both compile and then run all models & associated tests
• dbt build --full-refresh - have incremental models run as a full reload
• dbt build --select modelname - will only compile/run modelname
• dbt build --select +modelname - will compile/run modelname and all parents
• dbt build --select modelname+ - will compile/run modelname and all children
• dbt build --select +modelname+ - will compile/run modelname, and all parents and children
• dbt build --select @modelname - will compile/run modelname, all parents, all children, AND all parents of all children
• dbt build --exclude modelname - will compile/run all models except modelname
• dbt compile - compile all models but do not execute them
• dbt run - run all models & tests
• dbt seed - create or refresh small tables from .csv seed files
• dbt clean - clear your logs and compiled scripts (can fix issues)
• dbt docs generate - refresh the documentation for your project
• dbt docs serve - open this documentation in your browser

Additional commands and details are available in dbt's documentation

Project features

• How to nest models:
  • DIM_ORDERS
  • DIM_CURRENT_YEAR_ORDERS
  • DIM_CURRENT_YEAR_OPEN_ORDERS
• Snowflake commands in a pre-hook:
  • DIM_CALENDAR_DAY
• Materializations:
  • LKP_EXCHANGE_RATES (table)
  • LKP_CUSTOMERS_WITH_ORDERS (ephemeral)
  • DIM_CUSTOMERS_SHARE (secure view)
  • FACT_ORDER_LINE (incremental fact)
  • DIM_CUSTOMERS, DIM__CUSTOMERS (incremental dim)
  • DIM_CUSTOMERS_TYPE2 (snapshot)
• Source data quality tests:
  • sources.yml
• Model data quality tests:
  • schema.yml
• Features available in dbt_project.yml
  • run-start/run-end hooks
  • logging before and after modules
  • default materializations by folder path
  • Snowflake features - copy_grants, secure views, warehouse
  • schemas for models
• Macro examples:
  • snowflake_surrogate_key
  • copy_log_to_snowflake
  • create_masking_policies
• Jinja expressions:
  • Q1_FACT_PRICING_SUMMARY_REPORT_QUERY
  • Q2_MINIMUM_COST_SUPPLIER_QUERY
  • Q3_SHIPPING_PRIORITY_QUERY
  • Q4_ORDER_PRIORITY_CHECKING_QUERY

Resources

• Learn more about dbt in the docs
• Free on-demand training
• Additional Packages
• Create PK, UK, and FK in Snowflake using dbt Constraints
• Snowflake Guide - Accelerating Data Teams with dbt Core & Snowflake
• Snowflake Guide - Accelerating Data Teams with dbt Cloud & Snowflake
• Snowflake Guide - Data Engineering with Apache Airflow, Snowflake & dbt