dbt_salesforce
dbt-expectations
| dbt_salesforce | dbt-expectations | |
|---|---|---|
| 1 | 10 | |
| 38 | 969 | |
| - | 2.3% | |
| 8.3 | 6.5 | |
| 3 months ago | 5 days ago | |
| Shell | Shell | |
| Apache License 2.0 | Apache License 2.0 |
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dbt_salesforce
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How is the Salesforce connector still so awful.
Fivetran -> warehouse -> DBT cloud / Salesforce package-> warehouse -> bi tool
dbt-expectations
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Dbt tests vs Soda SQL
Have not used Soda, but dbt indeed is pretty good especially when adding dbt-expectations
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Data-eng related highlights from the latest Thoughtworks Tech Radar
dbt-expectations
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Data Quality Dimensions: Assuring Your Data Quality with Great Expectations
I highly.. highly.. recommend the dbt-expectations extension from Catologica for dbt. It's a port of Great Expectations, except you can quickly thunk it in your schema.yml's and have it run as part of your dbt test process. Super powerful and it's prevented us from shipping bad data many times.
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Managing SQL Tests
I'm used to utilising dbt and defining my tests there (along with dbt-utils or https://github.com/calogica/dbt-expectations): I simply add a list item to a column definition and can already define a great number of tests without having to copy code. I can even extend the pre-defined using generic tests. Writing custom tests also integrates nicely. Additionally it's very convenient to tag tests or define a severity. The learning curve for a business engineer is almost flat as long as they know some SQL.
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What are some Data Quality check related frameworks for datasets ranging from 100GB to 1TB in size?
Use dbt's testing functionality during your transformations with catalogica/dbt-expectations (Great Expectations framework ported to dbt)
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Great Expectations is annoyingly cumbersome
Check out dbt-expectations https://github.com/calogica/dbt-expectations
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CI/CD in data engineering - help a noob
There are certain things I would like to add such as data quality, I can use something like dbt great expectations, but I am not sure how much more I should force it before getting an airflow setup..
- How do you query and quality check data produced in intermediate steps in analytics pipeline?
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ETL Pipelines with Airflow: The Good, the Bad and the Ugly
[dbt Labs employee here]
Check out dbt-expectations package[1]. It's a port of the Great Expectations checks to dbt as tests. The advantage of this is you don't need another tool for these pretty standard tests, and can be early incorporated into dbt workflows.
[1] https://github.com/calogica/dbt-expectations
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Unit testing SQL in DBT
Also check out dbt-expectations that is a port of Great Expectations that greatly expands the configurable (non-assert) tests.
What are some alternatives?
dbt-project-evaluator - This package contains macros and models to find DAG issues automatically
dbt-utils - Utility functions for dbt projects.
dbt-bigquery-gitpod-config - A gitpod config for using dbt-bigquery in vscode
dbt-oracle - A dbt adapter for oracle db backend
dbt_salesforce_source - Fivetran's Salesforce source dbt package
materialize - The data warehouse for operational workloads.
dbt_stripe - Data models for Stripe built using dbt.
Scio - A Scala API for Apache Beam and Google Cloud Dataflow.
dotfiles - Collection of my dotfiles
NVTabular - NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.
dbt_google_ads - Fivetran data transformations for Google Ads built using dbt.
cuetils - CLI and library for diff, patch, and ETL operations on CUE, JSON, and Yaml