dbt-data-reliability
deequ
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dbt-data-reliability | deequ | |
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2 | 17 | |
328 | 3,103 | |
7.3% | 1.6% | |
9.7 | 7.5 | |
8 days ago | 18 days ago | |
Python | Scala | |
Apache License 2.0 | Apache License 2.0 |
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dbt-data-reliability
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Launch HN: Elementary (YC W22) β Open-source data observability
For any dbt users, their reliability package has the best and most comprehensive way to upload artifacts directly to the warehouse after a dbt invocation.
deequ
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[Data Quality] Deequ Feedback request
Deequ is library for data quality
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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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 itβs 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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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.
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Launch HN: Elementary (YC W22) β Open-source data observability
Does this in essence similar to the aws deeque project but fancier and more inclusive of edge cases, common scenarios? (https://github.com/awslabs/deequ)
What are some alternatives?
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
azure-kusto-spark - Apache Spark Connector for Azure Kusto
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.
re_data - re_data - fix data issues before your users & CEO would discover them π
Quill - Compile-time Language Integrated Queries 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 HuggingFace, LangChain, LlamaIndex, DeepSpeed, vLLM, FastChat, ModelScope, etc.
SynapseML - Simple and Distributed Machine Learning
soda-core - :zap: Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
sqllineage - SQL Lineage Analysis Tool powered by Python