deequ
Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets. (by awslabs)
SynapseML
Simple and Distributed Machine Learning (by microsoft)
Our great sponsors
| deequ | SynapseML | |
|---|---|---|
| 17 | 18 | |
| 3,126 | 4,967 | |
| 1.7% | 0.6% | |
| 7.4 | 8.9 | |
| 10 days ago | 7 days ago | |
| Scala | Scala | |
| Apache License 2.0 | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
deequ
Posts with mentions or reviews of deequ.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-01.
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[Data Quality] Deequ Feedback request
There's no straightforward way to drop and rerun a metric collection. For example, say you detect a problem in your data. You fix it, rerun the pipeline, and replace the bad data with the good. You'd want your metrics history to reflect the true state of your data. But the "bad run" cannot be dropped. Issue
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Thoughts on a business rules engine
I had similar requirements for QA reporting on large and diverse data sets. I implemented data check pipelines, with rules in AWS Deequ (https://github.com/awslabs/deequ) running on an Apache Spark cluster. The Deequ worked well for me, but there were a few cases where I opted to write the rule checks in the data store to improve throughput (i.e. SQL checks on critical data elements on the database).
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Building a data quality solution for devs and business people
Hey all! At the companies where I've worked as a developer, I've found that business stakeholders typically want a concrete way to check and assure the quality of data that pipelines are producing, before other downstream systems and users get impacted. I've tested solutions like Deequ, but I found that it made building compliance and data rules a bit more complicated and put a greater emphasis on developers to get the rules right that business was expecting. I also experienced issues with running checks in parallel and getting row level details about the failures.
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deequ VS cuallee - a user suggested alternative
2 projects | 30 Nov 2022
- November 15-19, 2022 FLiP Stack Weekly
- What are your favourite GitHub repos that shows how data engineering should be done?
- Well designed scala/spark project
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Soda Core (OSS) is now GA! So, why should you add checks to your data pipelines?
GE is arguably the most well known OSS alternative to Soda Core. The third option is deequ, originally developed and released in OSS by AWS. Our community has told us that Soda Core is different because it’s easy to get going and embed into data pipelines. And it also allows some of the check authoring work to be moved to other members of the data team. I'm sure there are also scenarios where Soda Core is not the best option. For example, when you only use Pandas dataframes or develop in Scala.
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Congrats on hitting the v1 milestone, whylabs! You're r/MLOps OSS tool of the month!
I wonder how this compares with tools like DeeQu (https://github.com/awslabs/python-deequ - requires Spark) or Pandas Profiling? One plus side I can see is that it doesn't require Apache Spark to run profiling (though a quick look at the code indicates that they are working on Spark support) and can work with real time systems.
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What companies/startups are using Scala (open source projects on github)?
There are so many of them in big data, e.g. Kafka, Spark, Flink, Delta, Snowplow, Finagle, Deequ, CMAK, OpenWhisk, Snowflake, TheHive, TVM-VTA, etc.
SynapseML
Posts with mentions or reviews of SynapseML.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-09-12.
- FLaNK Stack Weekly for 12 September 2023
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Microsoft announces new tool for applying ChatGPT and GPT-4 at massive scales
Release Notes: https://github.com/microsoft/SynapseML/releases/tag/v0.11.0
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Data science in Scala
b) There are libraries around e.g. Microsoft SynapseML, LinkedIn Photon ML
- [N] Microsoft Announces New Integrations with OpenAI and MLFlow
- [N] Microsoft Releases new Integrations with OpenAI and MLflow as part of SynapseML
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[P] Microsoft releases SynapseML v0.9.5 with support for speech synthesis, anomaly detection, and geospatial analytics on large-scale data
Link to Release Notes: https://github.com/microsoft/SynapseML/releases/tag/v0.9.5
- Microsoft releases SynapseML v0.9.5 for distributed geospatial analytics, speech synthesis, and anomaly detection in PySpark.
- [P] SynapseML v0.9.5 announces support for geospatial analytics, speech synthesis, and anomaly detection on large-scale datasets
- Microsoft releases SynapseML v0.9.5 with support for speech synthesis, anomaly detection, and geospatial analytics on Apache Spark
What are some alternatives?
When comparing deequ and SynapseML you can also consider the following projects:
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
mmlspark - Simple and Distributed Machine Learning [Moved to: https://github.com/microsoft/SynapseML]
azure-kusto-spark - Apache Spark Connector for Azure Kusto
isolation-forest - A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.
dbt-data-reliability - dbt package that is part of Elementary, the dbt-native data observability solution for data & analytics engineers. Monitor your data pipelines in minutes. Available as self-hosted or cloud service with premium features.
Tensorflow_scala - TensorFlow API for the Scala Programming Language
Quill - Compile-time Language Integrated Queries for Scala
Breeze - Breeze is a numerical processing library for Scala.
BigDL - Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, Baichuan, Mixtral, Gemma, etc.) on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max). A PyTorch LLM library that seamlessly integrates with llama.cpp, Ollama, HuggingFace, LangChain, LlamaIndex, DeepSpeed, vLLM, FastChat, etc.
re_data - re_data - fix data issues before your users & CEO would discover them 😊
cobrix - A COBOL parser and Mainframe/EBCDIC data source for Apache Spark