meltano VS cloudquery

- https://github.com/meltano/meltano

We'd love to hear what you think of Meltano (Cloud). If you join the Beta, you get 100 free credits (200 hourly or 100 daily runs) and a 20% discount on the pricing at GA (June 27). The first 100 to sign up get 1,000 credits -- that's 83 days of hourly runs or 3 years of dailies!

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Meltano[0] might be of interest to you. Easy way to move data that should be very familiar for software engineers. If a connector doesn't exist our SDK makes it easy to build it.

[0] https://github.com/meltano/meltano

(disclaimer - I work at Meltano)

We currently don't do any process optimization on a per-stream basis when doing an extract. We have seen folks in the community running each tap separately for each stream which can speed it up. We've got an issue around this (Melturbo).

Meltano

I built my data pipeline to Extract some data from websites and CSV files, Load it into my database, and Transform it into a reporting-ready schema. I used Python and Pandas to extract and load some of the data and Meltano to load some additional supporting data. All of that data went into a PostgreSQL database hosted in the cloud on Azure where I then used dbt to create data models in the database optimized for reporting. Finally, I use Metabase to visualize the data. (whew! that's a lot of moving parts!)

I'm learning https://meltano.com/ right now, so am building custom Taps, mostly for fun. I'm enjoying it. I'm pulling in a variety of data from https://www.geonames.org/ and Canadian weather/climate data into BigQuery

https://meltano.com/ is ELT but I like it

Check out CloudQuery - https://github.com/cloudquery/cloudquery for an easy cloud asset inventory.

There both do something like what you're looking for.... https://github.com/cloudquery/cloudquery https://github.com/openraven/magpie

Unified API is a holly grail but as many said quite difficult to abstract every use case in a scalable way that won't break. At CloudQuery (https://github.com/cloudquery/cloudquery) we focus solely on the ELT use-case(Founder/Maintainer here).

Congrats!! How does it compare to the ELT space and the modern data stack where you have ingestion/storage/visualization layers decoupled?

Asking as the founder of CloudQuery (https://github.com/cloudquery/cloudquery), Saw Datasette quite a few times around data exploration but curious to hear about the most popular use-cases of Datasette!

Congrats!! We also focus on performance at CloudQuery (https://github.com/cloudquery/cloudquery) by using Golang, gRPC and still trying to be abstract enough to support different databases :)

In any case good luck!

CloudQuery for ETL

Another ELT framework that's an alternative to Airbyte

Another alternate ELT

Check out CloudQuery, we have PostgreSQL source connectors and S3 destination that supports parquet (Disclaimer: Maintainer and founder here)

Hello! Im Yevgeny, Founder & maintainer at CloudQuery . We've built CloudQuery as an open source high performance ELT framework so you should get pretty good results syncing all your cloud assets from high number of accounts (we have users syncing more than 10K Azure subscription and thousands of AWS accounts concurrently).