pandera
dbt-expectations
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| pandera | dbt-expectations | |
|---|---|---|
| 7 | 10 | |
| 3,007 | 947 | |
| 5.2% | 4.1% | |
| 9.1 | 6.6 | |
| 3 days ago | 6 days ago | |
| Python | Shell | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pandera
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Unit testing functions that input/output dataframes?
I use Pandera, so I just need to define the expected input/output schemas (i.e. column names, types, and constraints on them), and Pandera automatically generates fake data for the unit tests, and validates the result: https://github.com/unionai-oss/pandera
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Great Expectations is annoyingly cumbersome
Please DM me! Or we can discuss in this issue which I just created: https://github.com/unionai-oss/pandera/issues/1042
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Data validation for dashboards
In my opinion for simple data validation tasks the best solution is always Pandera.
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Show HN: Pandera 0.8.0 – validate pandas, dask, modin, and koalas dataframes
* adds support for mypy static type-linting if you need that extra type safety
Repo: https://github.com/pandera-dev/pandera
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Pandera 0.8.0: Schema Validation for Pandas, Dask, Modin, and Koalas DataFrames. Oh, and also out-of-the-box Pydantic and Mypy support :)
Repo: https://github.com/pandera-dev/pandera
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How heavily do you use Great Expectations?
pandera
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?
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
dbt-utils - Utility functions for dbt projects.
Schematics - Python Data Structures for Humans™.
dbt-oracle - A dbt adapter for oracle db backend
jsonschema - An implementation of the JSON Schema specification for Python
materialize - The data warehouse for operational workloads.
pointblank - Data quality assessment and metadata reporting for data frames and database tables
Scio - A Scala API for Apache Beam and Google Cloud Dataflow.
swifter - A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
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.
sweetviz - Visualize and compare datasets, target values and associations, with one line of code.
cuetils - CLI and library for diff, patch, and ETL operations on CUE, JSON, and Yaml