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Data-drift Alternatives
Similar projects and alternatives to data-drift
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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soda-core
:zap: Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io
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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.
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OpenMetadata
Open Standard for Metadata. A Single place to Discover, Collaborate and Get your data right.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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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
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better data-drift alternative or higher similarity.
data-drift reviews and mentions
Posts with mentions or reviews of data-drift.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-01-16.
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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
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A note from our sponsor - SaaSHub
www.saashub.com | 29 Apr 2024
Stats
Basic data-drift repo stats
7
298
9.5
2 months ago
data-drift/data-drift is an open source project licensed under GNU General Public License v3.0 only which is an OSI approved license.
The primary programming language of data-drift is HTML.
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