deequ
analytics
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deequ
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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.
analytics
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I'm not getting it...what's the point of DBT?
Take a look at gitlab's dbt project: https://gitlab.com/gitlab-data/analytics/-/blob/master/transform/snowflake-dbt/models/common/schema.yml
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How would you structure a repo with 10+ ETL pipelines and shared code?
A good reference is the Gitlab data team repo. https://gitlab.com/gitlab-data/analytics
- What are your favourite GitHub repos that shows how data engineering should be done?
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Are there any open corporate Data Team repositories / projects besides GitLab?
For example, their Data Team have a public repository, with a bunch of information on how they organize DAGs, machine learning projects, system configuration, etc.
- Kimball Dim Modelling Code Examples
- Can someone help me, an absolute newbie, understand the usage and benefit of dbt with practical example ?
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Is jinja templating right for DBT?
So I've run through the DBT tutorial stuff and looked over some fairly complex uses of it i.e. GitLab Data and I was wondering if anyone has any opinions or insights into the use of jinja templating in the sql?
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Where can I find free data engineering ( big data) projects online?
Gitlab has their DBT repo open source and is very useful for seeing how to structure a project at scale. https://gitlab.com/gitlab-data/analytics/-/tree/master/transform/snowflake-dbt
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Gitlab's Data Team Platform (in depth look at their stack)
Currently the team is working hard on this: https://gitlab.com/gitlab-data/analytics/-/issues/9508
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Can someone explain the big deal with dbt?
GitLab's dbt project is an excellent example of a mature project at scale. They also have a comprehensive guide to their methodology.
What are some alternatives?
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
dbt-synapse - dbt adapter for Azure Synapse Dedicated SQL Pools
azure-kusto-spark - Apache Spark Connector for Azure Kusto
dagster - An orchestration platform for the development, production, and observation of data assets.
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.
castled - Castled is an open source reverse ETL solution that helps you to periodically sync the data in your db/warehouse into sales, marketing, support or custom apps without any help from engineering teams
Quill - Compile-time Language Integrated Queries for Scala
datahub - The Metadata Platform for your Data Stack
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.
AdvancedSQLPuzzles - Welcome to my GitHub repository. I hope you enjoy solving these puzzles as much as I have enjoyed creating them.
re_data - re_data - fix data issues before your users & CEO would discover them 😊
lightdash - Self-serve BI to 10x your data team ⚡️