monosi
lightdash
Our great sponsors
monosi | lightdash | |
---|---|---|
20 | 13 | |
320 | 3,399 | |
1.3% | 5.3% | |
0.0 | 10.0 | |
over 1 year ago | 3 days ago | |
Python | TypeScript | |
Apache License 2.0 | MIT License |
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.
monosi
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Open source data observability tools with UI?
I also found https://github.com/monosidev/monosi but it seems there are no activities in the repository from last year.
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Databricks monitoring/observability
I'm building an open source data observability platform - https://github.com/monosidev/monosi that visualizes metadata collected from data warehouses. Databricks is currently not supported (contributions welcome!), but it may help to take a look at how we approach the anomaly detection & visualization aspects.
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Monitor PostgreSQL for anomalies in ingested data
Building an open source tool that lets you monitor PostgreSQL instances form anomalies in data coming in - https://github.com/monosidev/monosi
- Open Source Data Observability for BigQuery
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Metadata extraction and management
It’s open source, check out the repository here - https://github.com/monosidev/monosi
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How to Monitor Supabase with Monosi
🎉 Congratulations, you've just set up and scheduled a data monitor on your Supabase instance. You can now add more monitors to other tables in your database. Find more information on how to use Monosi here.
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Setting up data monitoring for PostgreSQL
Now that you’ve worked through an example using a public PostgreSQL instance, you can further extend this to your own data store. For more information, get started here.
- Monosi v0.0.3 Released! Open source Data Observability now with a Web UI, Postgres Support, & more.
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Sunday Daily Thread: What's everyone working on this week?
Continuing to build out & stabilize Monosi (open source data observability) - https://github.com/monosidev/monosi
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Data pipeline suggestions
Observability: Monosi
lightdash
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Apache Superset
> YAML, pivoting being done in the frontend, no symmetric aggregates
(one of the maintainers of Lightdash) You touched on some of our most interesting problems here! Would be especially interested to hear about what you liked / didn't like about symmetric aggregates in Looker and how you find dev with YAML. If you have an idea of how you'd like these to look in Lightdash, the team would be really open to making that a reality.
For pivoting in the backend, this is coming! Issue here: https://github.com/lightdash/lightdash/issues/2907
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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.
- ⏎ 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.
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. The front end helps to understand and extend the underlying SQL queries. Lightdash also visualizes business metrics and makes them shareable with the data team. It is also possible to integrate all data into another visualization tool.
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What are your hottest dbt repositories in 2022 so far? Here are mine!
- ⚡️ Lightdash: Lightdash converts dbt models and makes it possible to define and easily visualize additional metrics via a visual interface.
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Data pipeline suggestions
Visualization / Analysis: Lightdash, Superset
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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
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Launch HN: Metaplane (YC W20) – Datadog for Data
1) An integration with Metabase Cloud is on our roadmap for Q1! We'd love to integrate with Lightdash, but they don't have a public API just yet[1].
2) Several of our customers use us to alert on schema changes in Postgres, specifically so they can get ahead of application database changes that will end up in the warehouse, so you're definitely not alone! Here's a link on how to connect postgres: https://docs.metaplane.dev/docs/postgres
That's an excellent stack and one we kept front and center when building out Metaplane, so definitely let us know if you have any feedback or suggestions here!
[1]: https://github.com/lightdash/lightdash/issues/632
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what's your experience with Looker ?
I would recommend lightdash which is essentially an open source Looker clone https://github.com/lightdash/lightdash
- a full semantic model based on dbt, dimensions, joins and metrics
- An open source alternative to Looker built using dbt. Made for analysts
What are some alternatives?
datahub - The Metadata Platform for your Data Stack
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:
jitsu - Jitsu is an open-source Segment alternative. Fully-scriptable data ingestion engine for modern data teams. Set-up a real-time data pipeline in minutes, not days
superset - Apache Superset is a Data Visualization and Data Exploration Platform
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
Rakam - 📈 Collect customer event data from your apps. (Note that this project only includes the API collector, not the visualization platform)
soda-spark - Soda Spark is a PySpark library that helps you with testing your data in Spark Dataframes
trino_data_mesh - Proof of concept on how to gain insights with Trino across different databases from a distributed data mesh
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
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.
great_expectations - Always know what to expect from your data.
streamlit - Streamlit — A faster way to build and share data apps.