kuwala
mara-pipelines
Our great sponsors
kuwala | mara-pipelines | |
---|---|---|
33 | 3 | |
755 | 2,054 | |
0.0% | 0.4% | |
0.0 | 6.0 | |
over 1 year ago | 4 months ago | |
JavaScript | Python | |
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.
kuwala
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Show HN: GeoSage – A ETL Webtool for Geo and Demographics Data from the Open Web
--> Google Trends Data for Regions (Coming Soon)
The tool goes beyond our previously published CLI tool (https://github.com/kuwala-io/kuwala/tree/master/kuwala) by providing a hostable solution with a user-friendly interface. We have not open-sourced it yet but a demo is available here: https://geosage.kuwala.io/.
Urban planners can utilize movement data to analyze foot traffic in different city zones. Marketers can leverage demographic data to tailor campaigns more effectively. Developers can build their apps on top of it.
To round it up .... GeoSage brings...
Unified Data Management: Access data from OSM, Facebook, and soon Google, all in one place.
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Show HN: Free Datasets for Spatial Engineers and Location Analysts
--> https://github.com/kuwala-io/kuwala/blob/master/kuwala/pipelines/osm-poi/README.md
Googe Popular Times: Movement data can be also found on Google. When you search a location it is often shown how frequently a place was visited on an hourly-daily basis (on an index of 0-100). With this libary you can access all the Popular Times data for location and entire cities
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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.
- Show HN: Open-Source Data Workspace Powered by Dbt and Airbyte
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What are the hottest dbt Repositories you should star on Github 2022? - Here are mine.
Kuwala ( https://github.com/kuwala-io/kuwala ) Kuwala is a data workspace that consolidates the Modern Data Stack and makes it usable for BI analysts and Engineers. Even though dbt is originally targeted at BI Analysts, dbt is mainly used by Engineers. This shifts a large amount of pipeline engineering effort to the IT department. 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. Consequently, the BI Analyst can work more iteratively and maintain the complete workflow from source to metrics in a dashboard. Under the hood and Behind the Scenes, the dbt models are generated so that a more experienced engineer can customize the pipelines at any time. In addition, engineers can easily convert dbt models into reusable “drag and drop” components.
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What are your hottest dbt repositories in 2022 so far? Here are mine!
- 🧱 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.
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Is Geoboundaries still a thing for GIS experts?
I have then built with a friend this here: https://github.com/kuwala-io/kuwala/tree/master/kuwala/pipelines/admin-boundaries . So the script is extracting the boundaries from OSM and cleans it (forms the hierachy and connect shapes). However, it did not lift up and I had not the feeling this was in the end an interesting feature for the community. It would be wonderful to hear your feedback and maybe find someone to pick it up :-)
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My open-source project: there shall be no difference between BI, Data Analysts and Data Engineer
Hi, we have a nice slack channel, here: https://kuwala-community.slack.com/ssb/redirect and my repo on Github is here available: https://github.com/kuwala-io/kuwala
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I don't get the many shady location data providers if there is Google Popular Times and Open Street Map that you can access with ease and drive similar conclusions.
**Global Admin Boundaries:** A huge problem that often people feel when working with location data is aggregating the data into different geo-based slices (country level, admin level, or even smaller into sub-districts). Here is a repo that cleaned the data out of Open Street Map for geo boundaries worldwide from very broad to a very small granularity --> https://github.com/kuwala-io/kuwala/blob/master/kuwala/pipelines/admin-boundaries/README.md
If it is about location data you should know OpenStreetMap. It's the biggest Database with meta info on location. It's not perfect but big companies like Mapbox, Apple, and Microsoft rely on it. Since the API is kind of messy, you can load with this repository whole cities information smoothly into a PostGres --> https://github.com/kuwala-io/kuwala/blob/master/kuwala/pipelines/osm-poi/README.md
mara-pipelines
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How to keep track of the different Transformations done in an ETL pipeline?
The closest I've found is Mara but not what I'm after.
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Using PostgreSQL as a Data Warehouse
The tooling behind the approach has been built as a set of python package named Mara. It is available at GitHub:
https://github.com/mara/mara-pipelines
And additional packages can be found at the Mara org:
https://github.com/mara
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Build your own “data lake” for reporting purposes
Minio and nifi, require machines by themselves. Better off pure python and if obe wants sonething lighweight and visually pleasing Mara [0] or Dagster with Dagit [1] will do the job
[0] https://github.com/mara/mara-pipelines
[1] https://docs.dagster.io/tutorial/execute
What are some alternatives?
uawardata - The data behind uawardata.com
abcd-hcp-pipeline - bids application for processing functional MRI data, robust to scanner, acquisition and age variability.
CKAN - CKAN is an open-source DMS (data management system) for powering data hubs and data portals. CKAN makes it easy to publish, share and use data. It powers catalog.data.gov, open.canada.ca/data, data.humdata.org among many other sites.
pybaseball - Pull current and historical baseball statistics using Python (Statcast, Baseball Reference, FanGraphs)
lightdash - Self-serve BI to 10x your data team ⚡️
dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
re_data - re_data - fix data issues before your users & CEO would discover them 😊
etl-markup-toolkit - ETL Markup Toolkit is a spark-native tool for expressing ETL transformations as configuration
dbt-fal - do more with dbt. dbt-fal helps you run Python alongside dbt, so you can send Slack alerts, detect anomalies and build machine learning models.
dremio-oss - Dremio - the missing link in modern data
evidence - Business intelligence as code: build fast, interactive data visualizations in pure SQL and markdown
airbyte - The leading data integration platform for ETL / ELT data pipelines from APIs, databases & files to data warehouses, data lakes & data lakehouses. Both self-hosted and Cloud-hosted.