great_expectations
re_data
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great_expectations | re_data | |
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15 | 15 | |
9,466 | 1,521 | |
1.7% | 0.7% | |
9.9 | 7.1 | |
about 10 hours ago | 3 months ago | |
Python | HTML | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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.
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 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.
- Greatexpectations - Always know what to expect from your data.
- Greatexpectations β 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.
re_data
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How to design a software for extracting and validating data in existing DB(s)
Thereβs also this open source tool I think is doing kind of what the OP is looking for, re_data. The source code lives here: https://github.com/re-data/re-data
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What are the 5 hottest dbt Repositories one should star on GitHub 2022?
What are the 5 hottest dbt Repositories one should star on Github 2022?
dbt is a software framework that sits in the middle of the ELT process. It represents the transformative layer after loading data from an original source. Dbt combines SQL with software engineering principles.
Here are my top5!
- Lightdash (https://github.com/lightdash/lightdash): Lightdash converts dbt models and makes it possible to define and easily visualize additional metrics via a visual interface.
- β re_data (https://github.com/re-data/re-data): Re-Data is an abstraction layer that helps users monitor dbt projects and their underlying data. For example, you get alerts when a test failed or a data anomaly occurs in a dbt project.
- evidence (https://github.com/evidence-dev/evidence): Evidence is another tool for lightweight BI reporting. With Evidence, you can build simple reports in "medium style" using SQL queries and Markdown.
- Kuwala (https://github.com/kuwala-io/kuwala): With Kuwala, a BI analyst can intuitively build advanced data workflows using a drag-drop interface on top of the modern data stack without coding. Behind the Scenes, the dbt models are generated so that a more experienced engineer can customize the pipelines at any time.
- fal ai (https://github.com/fal-ai/fal): Fal helps to run Python scripts directly from the dbt project. For example, you can load dbt models directly into the Python context which helps to apply Data Science libraries like SKlearn and Prophet in the dbt models.
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What are the hottest dbt Repositories you should star on Github 2022? - Here are mine.
re_data ( https://github.com/re-data/re-data ) Re_data is an abstraction layer that helps users monitor dbt projects and their underlying data. For example, you get alerts when a test failed or a data anomaly occurs in a dbt project and which underlying metric is affected. In addition, the lineage graph is also intuitively displayed. Re-data is one of two others frameworks focusing on the observability aspect of lengthy pipelines in dbt (check also out: open-metadata and Elementary).
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What are your hottest dbt repositories in 2022 so far? Here are mine!
- β re_data: Re-Data is an abstraction layer that helps users monitor dbt projects and their underlying data. For example, you get alerts when a test failed or a data anomaly occurs in a dbt project.
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Snowflake SQL AST parser?
Some things you might be interested in are re_data and Elementary Data.
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Sentry for Data Teams
Around a year ago I launched re_data (an open-source data reliability tool) here. After some pivots, we seem to be getting traction and this is how it looks now: https://www.getre.io/. Super interested in getting your feedback and suggestions on the direction :)
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Launch HN: Elementary (YC W22) β Open-source data observability
Nice project, at re_data we just got over a lot of your new updates and it seems a quite large part of your project is "inspired" by code from our library https://github.com/re-data/re-data. Even with parts, we are not especially proud of ;)
If you decide to copy not only ideas but a big part of internal implementation, I think you should include that information in your LICENSE.
Cheers
- How are you guys testing your data?
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great_expectations VS redata - a user suggested alternative
2 projects | 24 Sep 2021
It's more convenient when you are already using dbt and don't want to set up a separate workflow for testing data when it can be done with dbt inside the data warehouse. Also the thing re_data does well is letting you create time-based metrics about your data quality instead of just tests (a lot of the tests can be rewritten to that) That allows you to do a couple of things more than GE, you can for example easily visualize or look for anomalies in those. You can also compute tests much more efficiently. Research about computing metrics as a good way of doing data quality was actually done by the team behind deequ: http://www.vldb.org/pvldb/vol11/p1781-schelter.pdf I'm the author, so obviously I'm a bit biased :)
- re_data - open-source data quality library build on top of dbt.
What are some alternatives?
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
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.
kedro-great - The easiest way to integrate Kedro and Great Expectations
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.
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.
sqllineage - SQL Lineage Analysis Tool powered by Python
streamlit - Streamlit β A faster way to build and share data apps.
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
gradio - Build and share delightful machine learning apps, all in Python. π Star to support our work!
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
deequ - Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.