[gervwyk]

This is a really cool use case! Especially with for environmental and ethical conscious brands.

We sort of do the same thing with QR codes in a factory at the moment where we trace production batches, stock management and R&D test data to link the label factory items back to the MRP data and dashboards. This was implemented using Lowdefy [0] - interestingly we also started Lowdefy out of the need for customer facing dashboards and have since widened the scope into a range of other things aswell.

This is a really cool use case! Especially with for environmental and ethical conscious brands.

We sort of do the same thing with QR codes in a factory at the moment where we trace production batches, stock management and R&D test data to link the label factory items back to the MRP data and dashboards. This was implemented using Lowdefy [0] - interestingly we also started Lowdefy out of the need for customer facing dashboards and have since widened the scope into a range of other things aswell.

Explo looks really cool! Congrats on the launch. I would love to see some videos on creating dashboards especially filters etc.

[0] - https://github.com/lowdefy/lowdefy

[anchen976]

Wow the QR code resurgence is real! I'm curious why you chose to support data sources such as Mongo and google sheets first as oppose to others I saw that were coming soon (Postgres, MySQL, etc) and challenges you experienced building out the connections. Looks like we support a completely different set of databases.

And is your data pulled directly from your MRP system or loaded into another database first?

And we'll definitely be adding more examples and videos creating and embedding dashboards in Explo.

[gervwyk]

Although we currently support MongoDB, PostgreSQL, MS SQL Server, MySQL, MariaDB, SQLite, and Amazon Redshift, SendGrid, Http requests, Google sheets and S3. We started by building apps with MongoDB and the aggregation framework really allowed us to do more and more complex things ito data analytics which I doubt one could pull off in SQL. (I'm no SQL expert, so forgive me if I'm wrong). Our Lowdefy operators and application schema also took a lot of inspiration from Mongo's query language.

We usually deal with less than 100k records, scaling has mostly not been an issue for us, and in such cases we can run the analytic aggregations directly on the MRP read replica [0]

We built Lowdefy so that we can build better, more flexible, quality apps faster for customers. Then decided to OS it.

Lowdefy is designed to work any number of connections, so we'll be adding as we grow. Also, we prefer to not add a "thin" connection, but rather build out a well scoped and tested connection - this can be tricky, not sure of this is the best approach ito marketing as we have an extensive list of connection requests we would like to get to [1]. We'll start to prioritise these more in the near future. We'll also finalise module federation for connections, which will enable custom connections.

[0] - https://docs.mongodb.com/manual/core/read-preference/#std-la...

[1] - https://github.com/lowdefy/lowdefy/discussions/309