buildx VS airbyte

This is great! I’ve been waiting a long time for this and it seems like more debug features are in the pipeline too: https://github.com/docker/buildx/issues/1104

Docs: https://docs.docker.com/engine/reference/commandline/buildx_...

Container engine: Docker v23.0.4 | Buildx v0.10.4

ARCH=amd64 # change to 'arm64' for M1 VERSION=v0.10.4 curl -LO https://github.com/docker/buildx/releases/download/${VERSION}/buildx-${VERSION}.darwin-${ARCH} mkdir -p ~/.docker/cli-plugins mv buildx-${VERSION}.darwin-${ARCH} ~/.docker/cli-plugins/docker-buildx chmod +x ~/.docker/cli-plugins/docker-buildx docker buildx version # verify installation

wget -O /tmp/docker/cli-plugins/docker-buildx https://github.com/docker/buildx/releases/tag/v0.10.4/buildx-v0.10.4.linux-amd64

Your particular one looks somewhat like https://github.com/docker/buildx/issues/1595.

Now, we have examples of the actual definitions for a single application’s docker container. Notice that we set the contexts key here which references the dependency targets we just defined. You can basically think of this like the depends_on block if you’ve used Terraform before. The args key let’s us populate the ARG variables in the Dockerfile. This is what is ultimately different between each of the containers. In the future, the buildx team might support using for_each loops like in Terraform but for now each block will have a bit of duplication.

Now as bonus we i will add how to use this techniques to build a multi arch build container. Maybe you have the same application but you need to run it in arm or riscv architecture. For this we can use the buildx plugin from docker https://github.com/docker/buildx.

Third step is docker/setup-buildx-action configures buildx, which is a Docker CLI plugin that provides enhanced build capabilities.

I'l also give a shout-out to Airbyte (https://airbyte.com/), with which I've had some limited success with integrating Salesforce to a local database. The particular pull for Airbyte is that we can self-host the open source version, rather than pay Fivetran a significant sum to do this for us.

It's an immature tool, so I don't yet know that I can claim we've spent _less_ than Fivetran on the additional engineering and ops time, but it feels like it has potential to do so once stabilized.

Link to GitHub -->

When it says "available in open source", is that under the main airbyte repo's licensing [1], hence primarily licensed under the Elastic License v2 and therefore not typically considered open source by many?

Airbyte has previous of advertising their offering as open source while not really being as per the OSD[2]. This has been raised with them previously but without response [3][4]. They've also been extending their use of ELv2, recently moving many of their existing MIT licensed connectors to be ELv2 [5].

[1] https://github.com/airbytehq/airbyte/blob/master/LICENSE

As possible solution, I can suggest Airbyte(https://airbyte.com/). it's more performant than generic python script.

Airbyte, an open-source data integration engine that offers hundreds of connectors with data warehouses and databases, has gained popularity for its seamless integration and data syncing capabilities. Xata's integration with Airbyte offers a streamlined data ingestion process from any Airbyte input source directly into your Xata database.

You can try and contribute some new connectors/operators for workflow managers like Airflow or Airbyte