fullnamematchscore-go
data-drift
fullnamematchscore-go | data-drift | |
---|---|---|
2 | 7 | |
0 | 301 | |
- | 3.0% | |
4.3 | 9.5 | |
8 months ago | 3 months ago | |
Go | HTML | |
MIT License | GNU General Public License v3.0 only |
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.
fullnamematchscore-go
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Identify Inconsistent Yet Matching Company Names in a Dataset or Data Table within Databricks using DataFrames
by Interzoid Team
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Go Package Added for Name Match Scoring
Here is the Go Package on Github: github.com/interzoid/fullnamematchscore-go
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
What are some alternatives?
aqueduct - Aqueduct is no longer being maintained. Aqueduct allows you to run LLM and ML workloads on any cloud infrastructure.
lakeFS - lakeFS - Data version control for your data lake | Git for data
go-dataframe - A simple package to abstract away the process of creating usable DataFrames for data analytics. This package is heavily inspired by the amazing Python library, Pandas.
soda-core - :zap: Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io
goro - A High-level Machine Learning Library for Go
lightdash - Self-serve BI to 10x your data team ⚡️
awesome-data-centric-ai - Open-Source Software, Tutorials, and Research on Data-Centric AI 🤖
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.
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
OpenMetadata - Open Standard for Metadata. A Single place to Discover, Collaborate and Get your data right.
mask-json-field-transform
routerino - Tiny, SEO-focused router for React websites