data-diff
dbt-unit-testing
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data-diff | dbt-unit-testing | |
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20 | 7 | |
2,842 | 404 | |
3.0% | 4.5% | |
9.4 | 7.7 | |
14 days ago | 11 days ago | |
Python | Shell | |
MIT License | MIT License |
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data-diff
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How to Check 2 SQL Tables Are the Same
If the issue happen a lot, there is also: https://github.com/datafold/data-diff
That is a nice tool to do it cross database as well.
I think it's based on checksum method.
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Oops, I wrote yet another SQLAlchemy alternative (looking for contributors!)
First, let me introduce myself. My name is Erez. You may know some of the Python libraries I wrote in the past: Lark, Preql and Data-diff.
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Looking for Unit Testing framework in Database Migration Process
https://github.com/datafold/data-diff might be worth a look
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Ask HN: How do you test SQL?
I did data engineering for 6 years and am building a company to automate SQL validation for dbt users.
First, by “testing SQL pipelines”, I assume you mean testing changes to SQL code as part of the development workflow? (vs. monitoring pipelines in production for failures / anomalies).
If so:
1 – assertions. dbt comes with a solid built-in testing framework [1] for expressing assertions such as “this column should have values in the list [A,B,C]” as well checking referential integrity, uniqueness, nulls, etc. There are more advanced packages on top of dbt tests [2]. The problem with assertion testing in general though is that for a moderately complex data pipeline, it’s infeasible to achieve test coverage that would cover most possible failure scenarios.
2 – data diff: for every change to SQL, know exactly how the code change affects the output data by comparing the data in dev/staging (built off the dev branch code) with the data in production (built off the main branch). We built an open-source tool for that: https://github.com/datafold/data-diff, and we are adding an integration with dbt soon which will make diffing as part of dbt development workflow one command away [2]
We make money by selling a Cloud solution for teams that integrates data diff into Github/Gitlab CI and automatically diffs every pull request to tell you the how a change to SQL affects the target table you changed, downstream tables and dependent BI tools (video demo: [3])
I’ve also written about why reliable change management is so important for data engineering and what are key best practices to implement [4]
[1] https://docs.getdbt.com/docs/build/tests
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Data-diff v0.3: DuckDB, efficient in-database diffing and more
Hi HN:
We at Datafold are excited to announce a new release of data-diff (https://github.com/datafold/data-diff), an open-source tool that efficiently compares tables within or across a wide range of SQL databases. This release includes a lot of new features, improvements and bugfixes.
We released the first version 6 months ago because we believe that diffing data is as fundamental of a capability as diffing code in data engineering workflows. Over the past few months, we have seen data-diff being adopted for a variety of use-cases, such as validating migration and replication of data between databases (diffing source and target) and tracking the effects of code changes on data (diffing staging/dev and production environments).
With this new release data-diff is significantly faster at comparing tables within the same database, especially when there are a lot of differences between the tables. We've also added the ability to materialize the diff results into a database table, in addition to (or instead of) outputting them to stdout. We've added support for DuckDB, and for diffing schemas. Improved support for alphanumerics, and threading, and generally improved the API, the command-line interface, and stability of the tool.
We believe that data-diff is a valuable addition to the open source community, and we are committed to continue growing it and the community around it. We encourage you to try it out and let us know what you think!
You can read more about data-diff on our GitHub page at the following link: https://github.com/datafold/data-diff/
To see the list of changes for the 0.3.0 release, go here: https://github.com/datafold/data-diff/releases/tag/v0.3.0
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data-diff VS cuallee - a user suggested alternative
2 projects | 30 Nov 2022
- Compare identical tables across databases to identify data differences (Oracle 19c)
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How to test Data Ingestion Pipeline
For data mismatches, check out data-diff https://github.com/datafold/data-diff
- Data migration - easier way to compare legacy with new environment?
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Show HN: Open-source infra for building embedded data pipelines
Looks useful! Do you have a way to validate that the data was copied correctly and entirely? If not, you might want to consider integrating data-diff for that - https://github.com/datafold/data-diff
dbt-unit-testing
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The SQL Unit Testing Landscape: 2023
If you use dbt for transformations Dbt Unit Testing (https://github.com/EqualExperts/dbt-unit-testing) is getting some attention (https://www.thoughtworks.com/radar/languages-and-frameworks?blipid=202304042)
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Data-eng related highlights from the latest Thoughtworks Tech Radar
dbt-unit-testing
- I'm not getting it...what's the point of DBT?
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Ask HN: How do you test SQL?
We use this and take an example-based tests approach for any non-trivial tables: https://github.com/EqualExperts/dbt-unit-testing
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SQL should be your default choice for data engineering pipelines
> How do you test some SQL logic in isolation?
I do this using sql
1. Extracting an 'ephemeral model' to different model file
2. Mock out this model in upstream model in unit tests https://github.com/EqualExperts/dbt-unit-testing
3. Write unit tests for this model.
This is not different than regular software development in a language like java.
I would argue its even better better because unit tests are always in tabular format and pretty easy to understand. Java unit tests on other hand are never read by devs in practice.
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Unit testing with dbt
I haven't done it yet but there are some popular blogs as well as a DBT package someone created.
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Modern Data Modeling: Start with the End?
> I really don’t understand the communities obsession with unwieldy tools like DBT.
It lets me write test first sql transforms. I never thought TDD sql would be possible. My sql is so much more readable with common logic extracted into ephmeral models. I practice same method to write clear code to write sql, eg: too many mocks = refactor into separate model ( class) .
I think DBT made this possible with refs that can be swapped out with mocks. This is the awesome library I am using https://github.com/EqualExperts/dbt-unit-testing
What are some alternatives?
datacompy - Pandas and Spark DataFrame comparison for humans and more!
sqlglot - Python SQL Parser and Transpiler
cuallee - Possibly the fastest DataFrame-agnostic quality check library in town.
dbt-expectations - Port(ish) of Great Expectations to dbt test macros
sqeleton
sqlx - 🧰 The Rust SQL Toolkit. An async, pure Rust SQL crate featuring compile-time checked queries without a DSL. Supports PostgreSQL, MySQL, and SQLite.
great_expectations - Always know what to expect from your data.
SS-Unit - A 100% T-SQL based unit testing framework for SQL Server
soda-core - :zap: Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io
hash-db - Experimental distributed pseudomultimodel keyvalue database (it uses python dictionaries) imitating dynamodb querying with join only SQL support, distributed joins and simple Cypher graph support and document storage
Preql - An interpreted relational query language that compiles to SQL.
spark-style-guide - Spark style guide