analytics
great_expectations
| analytics | great_expectations | |
|---|---|---|
| 15 | 15 | |
| - | 9,479 | |
| - | 1.0% | |
| - | 9.9 | |
| - | 1 day ago | |
| Python | ||
| - | Apache License 2.0 |
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.
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.
great_expectations
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Data Quality at Scale with Great Expectations, Spark, and Airflow on EMR
Great Expectations (GE) is an open-source data validation tool that helps ensure data quality.
- Looking for Unit Testing framework in Database Migration Process
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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.
- Greatexpectations - Always know what to expect from your data.
- Greatexpectations – Always know what to expect from your data
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Package for drift detection
great_expectations: https://github.com/great-expectations/great_expectations
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[D] Do you use data engineering pipelines for real life projects?
For example I just found "Great Expectations" and "Kedro", "Flyte" and I was wondering at which point in time and project complexity should we choose one of these tools instead of the ancient cave man way?
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Data pipeline suggestions
Testing: GreatExpectations
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Where can I find free data engineering ( big data) projects online?
Ingestion / ETL: Airbyte, Singer, Jitsu Transformation: dbt Orchestration: Airflow, Dagster Testing: GreatExpectations Observability: Monosi Reverse ETL: Grouparoo, Castled Visualization: Lightdash, Superset
- [P] Deepchecks: an open-source tool for high standards validations for ML models and data.
What are some alternatives?
dbt-synapse - dbt adapter for Azure Synapse Dedicated SQL Pools
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
dagster - An orchestration platform for the development, production, and observation of data assets.
kedro-great - The easiest way to integrate Kedro and Great Expectations
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
deepchecks - Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
datahub - The Metadata Platform for your Data Stack
re_data - re_data - fix data issues before your users & CEO would discover them 😊
AdvancedSQLPuzzles - Welcome to my GitHub repository. I hope you enjoy solving these puzzles as much as I have enjoyed creating them.
streamlit - Streamlit — A faster way to build and share data apps.
lightdash - Self-serve BI to 10x your data team ⚡️
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models