data-drift
data-drift | mask-json-field-transform | |
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7 | 2 | |
301 | 0 | |
3.0% | - | |
9.5 | 3.8 | |
3 months ago | 7 months ago | |
HTML | Java | |
GNU General Public License v3.0 only | 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.
data-drift
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Open-Source Observability for the Semantic Layer
Think of Datadrift as a simple & open-source Monte Carlo for the semantic layer era. The repo is at https://github.com/data-drift/data-drift
Datadrift started as an internal tool built at our former company, a large European B2B Fintech. We had data reliability challenges impacting key metrics used for financial and regulatory reporting.
However, when we tried existing data quality tools we where always frustrated. They provide row-level static testing (eg. uniqueness or nullness) which does not address time-varying metrics like revenues. And commercial observability solutions costs $manyK a month and brings compliance and security overhead.
We designed Datadrift to solve these problems. Datadrift works by simply adding a monitor where your metric is computed. It then understands how your metric is computed and on which upstream tables it depends. When an issue occurs, it pinpoints exactly which rows have been updated and introducing the change.
You can also set up alerting and customise it. For example, you can decide to open and assign an Github issue to the analyst owning the revenue metric when a +10% change is detected. We tried to make it easy to customise and developer friendly.
We are thinking of adding features around root cause analysis automation/issues pattern analysis to help data teams improve metrics quality overtime. We’d love to hear your feature requests.
Datadrift is built with Python and Go, and licensed under GPL. Our docs are here: https://github.com/data-drift/data-drift?tab=readme-ov-file#...
Dev set up and demo : https://app.claap.io/sammyt/drift-db-demo-a18-c-ApwBh9kt4p-0...
We’re very eager to get your feedback!
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Would learn Go to contribute to an OS project ? Or should I stick to python ?
I have already started working on it, I started in Go for some part, but I needed python to deploy a Pypi lib. Now its hybrid, and I prefer working with go 😬 but the most rational thinking leads to python.
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Ask HN: Dear startup founders, what have you developed in-house?
We used static testing framework like great expectations but that was not enough. We did not have the budget for the big data observability players like Monte Carlo, so we kept it simple.
Repo if interested: https://github.com/data-drift/data-drift
(Disclaimer: I am focusing full time on this project to see if it's an interesting business opportunity. It's 100% open-source -- feedback welcome!)
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Show HN: Lineage X Snapshot Tooling
https://app.data-drift.io/42527392/Lucasdvrs/dbt-datagit/ove...
You can "technically" install it by yourself, but tbh our focus are on the features, not the adoption. If you are interested it takes roughly 1 hour to configure (choose the data you want to observe, run a python function, install a Github app, add a configuration file), contact us.
The repo: https://github.com/data-drift/data-drift
Roast me
- Non-moving data is a journey
- “Non moving data” is like “Bug free”, it's a lie
mask-json-field-transform
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Ask HN: Dear startup founders, what have you developed in-house?
I wrote a kafka connect single message transform (SMT) to remove PII fields embedded in json messages:
https://github.com/ferozed/mask-json-field-transform
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Introducing `mask-json-field` Single Message Transform for Kafka Connect
GitHub: ferozed/mask-json-field-transform
What are some alternatives?
lakeFS - lakeFS - Data version control for your data lake | Git for data
ai-pr-reviewer - AI-based Pull Request Summarizer and Reviewer with Chat Capabilities.
soda-core - :zap: Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io
lightdash - Self-serve BI to 10x your data team ⚡️
tellery - Tellery lets you build metrics using SQL and bring them to your team. As easy as using a document. As powerful as a data modeling tool.
OpenMetadata - Open Standard for Metadata. A Single place to Discover, Collaborate and Get your data right.
fullnamematchscore-go - Generates a match score of two person names from 0-100, where 100 is the highest, on how closely two individual full names match. The scoring is based on a series of tests, algorithms, AI, and an ever-growing body of Machine Learning-based generated knowledge
routerino - Tiny, SEO-focused router for React websites
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
re_data - re_data - fix data issues before your users & CEO would discover them 😊