data-diff
testcontainers-dotnet
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data-diff | testcontainers-dotnet | |
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20 | 16 | |
2,842 | 3,534 | |
3.0% | 3.2% | |
9.4 | 9.0 | |
14 days ago | 6 days ago | |
Python | C# | |
MIT License | MIT License |
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.
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
testcontainers-dotnet
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Integration tests with AWS S3 buckets using Localstack and Testcontainers
Testcontainers
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Integration Tests with In Memory DB vs Real DB on Docker
Like others said, it's better to test with an actual database. I recommend using Testcontainers (https://dotnet.testcontainers.org), you can even create multiple instances so your tests can run in parallel independently.
- Unit Testing
- Running untrusted (user-provided) Python code on ASP.NET/C# backend
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Integration tests for AWS serverless solution
To launch a container in code we will use Testcontainers. Testcontainers is a library that is built on top of the .NET Docker remote API and provides a lightweight implementation to support your test environment in all circumstances. This library supports pre-defined packages for containers or you can use your .dockerfile. We will use a pre-defined package for LocalStak. LocalStack is a cloud service emulator that runs in a single container for AWS service. LocalStack supports a growing number of AWS services.
- If i want to do testing CRUD should I use in memory or just do integration test where I use a seperate database?
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Do you guys mock everything in your Unit Tests?
Bogus - For creating fake data Verify - Snapshot testing for .NET MELT - For testing ILogger usage Stryker - Mutation Testing for .NET TestContainers - run docker programmatically in integration tests
- Testes de integração com containers
- What C# tools would you like to use that don't exist today?
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Ask HN: How do you test SQL?
.NET Shop using SQL Server here, but I think something similar to what we do can apply to any stack. We use TestContainers [1] to spin up a container with SQL Server engine running on it. Then use FluentMigrator [2] to provision tables and test data to run XUnit integration tests against. This has worked remarkably well.
[1] https://dotnet.testcontainers.org/
What are some alternatives?
datacompy - Pandas and Spark DataFrame comparison for humans and more!
NUnit - NUnit Framework
cuallee - Possibly the fastest DataFrame-agnostic quality check library in town.
SpecFlow - #1 .NET BDD Framework. SpecFlow automates your testing & works with your existing code. Find Bugs before they happen. Behavior Driven Development helps developers, testers, and business representatives to get a better understanding of their collaboration
dbt-unit-testing - This dbt package contains macros to support unit testing that can be (re)used across dbt projects.
testcontainers-go - Testcontainers for Go is a Go package that makes it simple to create and clean up container-based dependencies for automated integration/smoke tests. The clean, easy-to-use API enables developers to programmatically define containers that should be run as part of a test and clean up those resources when the test is done.
sqeleton
testcontainers-python - Testcontainers is a Python library that providing a friendly API to run Docker container. It is designed to create runtime environment to use during your automatic tests.
great_expectations - Always know what to expect from your data.
Docker.DotNet - :whale: .NET (C#) Client Library for Docker API
soda-core - :zap: Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io
ephemeral-mongo - EphemeralMongo is a set of three NuGet packages wrapping the binaries of MongoDB 4, 5 and 6 built for .NET Standard 2.0.