kuwala VS re_data

--> 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.

--> 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

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.

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.

- 🧱 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.

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 :-)

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

**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

There’s 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

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.

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).

- ⏎ 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.

Some things you might be interested in are re_data and Elementary Data.

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 :)

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

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 :)