re_data
dbt-fal
re_data | dbt-fal | |
---|---|---|
15 | 12 | |
1,525 | 851 | |
0.4% | - | |
6.6 | 7.7 | |
5 days ago | 30 days ago | |
HTML | Python | |
GNU General Public License v3.0 or later | 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.
re_data
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How to design a software for extracting and validating data in existing DB(s)
Thereโs 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.
- โ 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!
- โ 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) โ 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.
dbt-fal
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machine learning in snowflake, unhappy data scientists
Happy data scientists use fal and dbt
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dbt for ML Engineering
fal (https://github.com/fal-ai/fal) helps with this! In fact we wrote a blog post about feature engineering with fal and dbt recently
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Dbt-fal: a dbt Python adapter with local code execution
We built a dbt adapter that helps you run local Python code with your dbt project with any other data warehouse. You can see it here: https://github.com/fal-ai/fal/tree/main/adapter
This new adapter helps you run your dbt Python models with isolated Python environments using our open source library: https://github.com/fal-ai/isolate
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Data Stack for Python Scripts (and other transformations)
Have you considered fal? https://github.com/fal-ai/fal
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Comparing dbt with Delta Live Tables for doing transformations
Something to maybe comment on the post is that dbt is introducing Python transformations on the data warehouse offering (e.g. Snowspark) soon and that there are tools like fal that enable these Python transformations to run in a different environment which you have control over.
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What are the hottest dbt Repositories you should star on Github 2022? - Here are mine.
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. This especially improves the data science capabilities within a data pipeline. What I extremely like about fal is that it extends dbt from a interesting angle.
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What are your hottest dbt repositories in 2022 so far? Here are mine!
- ๐ fal ai: 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 libaries like SKlearn and Prophet in the dbt models.
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Wanting to move away from SQL
I havenโt tried it yet but I know https://fal.ai/ helps you run python alongside dbt.
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Do I need orchestration for a Fivetran-dbt stack?
Yes I agree with you that having fivetran/airbyte and dbt covers a lot of the airflow use cases.. That being said you might still want to run some scripts after the DBT transformation is over, we ran into this exact problem and built a useful CLI tool for running python scripts alongside the dbt run.
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Why is Data Build Tool (DBT) is so popular? What are some other alternatives?
Great write-up! For your logging integration, you might have a look at fal. There's an example of sending events to Datadog
What are some alternatives?
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.
dbt-metabase - dbt + Metabase integration
great_expectations - Always know what to expect from your data.
dbt-expectations - Port(ish) of Great Expectations to dbt test macros
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.
kuwala - Kuwala is the no-code data platform for BI analysts and engineers enabling you to build powerful analytics workflows. We are set out to bring state-of-the-art data engineering tools you love, such as Airbyte, dbt, or Great Expectations together in one intuitive interface built with React Flow. In addition we provide third-party data into data science models and products with a focus on geospatial data. Currently, the following data connectors are available worldwide: a) High-resolution demographics data b) Point of Interests from Open Street Map c) Google Popular Times
sqllineage - SQL Lineage Analysis Tool powered by Python
evidence - Business intelligence as code: build fast, interactive data visualizations in pure SQL and markdown
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
gradio - Build and share delightful machine learning apps, all in Python. ๐ Star to support our work!
airflow-dbt - Apache Airflow integration for dbt