NVTabular
dbt-expectations
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| NVTabular | dbt-expectations | |
|---|---|---|
| 1 | 10 | |
| 1,006 | 939 | |
| 1.4% | 3.3% | |
| 5.5 | 6.7 | |
| 5 days ago | 29 days ago | |
| Python | Shell | |
| Apache License 2.0 | Apache License 2.0 |
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NVTabular
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ETL Pipelines with Airflow: The Good, the Bad and the Ugly
If you have GPUs, NVTabular outperforms most of the frameworks out there: https://github.com/NVIDIA/NVTabular
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?
Scio - A Scala API for Apache Beam and Google Cloud Dataflow.
dbt-utils - Utility functions for dbt projects.
cuetils - CLI and library for diff, patch, and ETL operations on CUE, JSON, and Yaml
dbt-oracle - A dbt adapter for oracle db backend
cascade - Lightweight and modular MLOps library targeted at small teams or individuals
materialize - The data warehouse for operational workloads.
federeco - implementation of federated neural collaborative filtering algorithm
daggy
powershap - A power-full Shapley feature selection method.
dbt-fal - do more with dbt. dbt-fal helps you run Python alongside dbt, so you can send Slack alerts, detect anomalies and build machine learning models.