Airbyte made simple

AirbyteServerless is a simple tool to manage Airbyte connectors, run them locally or deploy them in serverless mode.

Airbyte is a must-have in your data-stack with its catalog of open-source connectors to move your data from any source to your data-warehouse.

To manage these connectors, Airbyte offers Airbyte-Open-Source-Platform which includes a server, workers, database, UI, orchestrator, connectors, secret manager, logs manager, etc.

AirbyteServerless aims at offering a lightweight alternative to Airbyte-Open-Source-Platform to simplify connectors management.

abs is the CLI (command-line-interface) of AirbyteServerless which facilitates connectors management.

pip install airbyte-serverless

abs create my_first_connection --source="airbyte/source-faker:0.1.4" --destination="bigquery" --remote-runner "cloud_run_job"

1. Docker is required. Make sure you have it installed. (IF YOU DON'T HAVE DOCKER AND WANT TO RUN A PYTHON CONNECTOR, READ NEXT SECTION)
2. source param can be any Public Docker Airbyte Source (here is the list). We recomend that you use faker source to get started.
3. destination param must be one of the following:
   • print (default value if not set)
   • bigquery
   • contributions are welcome to offer more destinations 🤗
4. remote-runner param must be cloud_run_job. More integrations will come in the future. This remote-runner is only used if you want to run the connection on a remote runner and schedule it.
5. The command will create a configuration file ./connections/my_first_connection.yaml with initialized configuration.
6. Update this configuration file to suit your needs.

Actually, source argument can be a docker image as above or any command.

Below, we use pipx tool to run airbyte-source-faker python package available on pypi.

abs create my_first_connection --source="pipx run airbyte-source-faker==0.1.4"

The value just after pipx run can be any Airbyte Python Source available on pypi. For security reasons, beware to check that the source you are going to install is really from Airbyte.

The other arguments are the same as before.

abs run my_first_connection

1. This will launch an Extract-Load Job from the source to the destination.
2. The run commmand will only work if you have correctly edited ./connections/my_first_connection.yaml configuration file.
3. If you chose bigquery destination, you must:
   • have gcloud installed on your machine with default credentials initialized with the command gcloud auth application-default login.
   • have correctly edited the destination section of ./connections/my_first_connection.yaml configuration file. You must have dataEditor permission on the chosen BigQuery dataset.
4. Data is always appended at destination (not replaced nor upserted). It will be in raw format.
5. If the connector supports incremental extract (extract only new or recently modified data) then this mode is chosen.

You may not want to copy all the data that the source can get. To see all available streams run:

abs list-available-streams my_first_connection

If you want to configure your connection with only some of these streams, run:

abs set-streams my_first_connection "stream1,stream2"

Next run executions will extract selected streams only.

For security reasons, you do NOT want to store secrets such as api tokens in your yaml files. Instead, add your secrets in Google Secret Manager by following this documentation. Then you can add the secret resource name in the yaml file such as below:

source:
  docker_image: "..."
  config:
    api_token: GCP_SECRET({SECRET_RESOURCE_NAME})

Replace {SECRET_RESOURCE_NAME} by your secret resource name which must have the format: projects/{PROJECT_ID}/secrets/{SECRET_ID}/versions/{SECRET_VERSION}. To get this path:

1. Go to the Secret Manager page in the Google Cloud console.
2. Go to the Secret Manager page
3. On the Secret Manager page, click on the Name of a secret.
4. On the Secret details page, in the Versions table, locate a secret version to access.
5. In the Actions column, click on the three dots.
6. Click on 'Copy Resource Name' from the menu.

WARNING: THIS ONLY WORKS FOR NOW WITH A DOCKER SOURCE in python language.

abs remote-run my_first_connection

2. The remote-run commmand will only work if you have correctly edited ./connections/my_first_connection.yaml configuration file including the remote_runner part.

1. This command will launch an Extract-Load Job like the abs run command. The main difference is that the command will be run on a remote deployed container (we use Cloud Run Job as the only container runner for now).
2. If you chose bigquery destination, the service account you put in service_account field of remote_runner section of the yaml must be bigquery.dataEditor on the target dataset and have permission to create some BigQuery jobs in the project.
3. If your yaml config contains some Google Secrets, the service account you put in service_account field of remote_runner section of the yaml must have read access to the secrets.

When you create a connection using abs create my_connection --source "SOURCE", you can put any docker image you have access to as SOURCE. So SOURCE can be:

• a public docker image from Docker Hub
• a local docker image that you built
• a docker image that you built and pushed on Google Artifact Registry.

To run remotely on a cloud run job, the image must be available to Cloud Run (so cannot be local). It must be either public from Docker Hub or from Google Artifact Registry.

abs schedule-remote-run my_first_connection "0 * * * *"

⚠️ THIS IS NOT IMPLEMENTED YET

$ abs --help
Usage: abs [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  create                  Create CONNECTION
  list                    List created connections
  list-available-streams  List available streams of CONNECTION
  remote-run              Run CONNECTION Extract-Load Job from remote runner
  run                     Run CONNECTION Extract-Load Job
  run-env-vars            Run Extract-Load Job configured by environment...
  set-streams             Set STREAMS to retrieve for CONNECTION (STREAMS...

Join our Slack for any question, to get help for getting started, to speak about a bug, to suggest improvements, or simply if you want to have a chat 🙂.

Any contribution is more than welcome 🤗!

• Add a ⭐ on the repo to show your support
• Join our Slack and talk with us
• Raise an issue to raise a bug or suggest improvements
• Open a PR! Below are some suggestions of work to be done:
  • implements a scheduler
  • create a very light python Airbyte source / add a tutorial to use it in abs
  • implement the get_logs method of BigQueryDestination
  • use the new BigQuery Storage Write API for bigquery destination
  • enable updating cloud run job instead of deleting/creating when it already exists
  • add a new destination connector (Cloud Storage?)
  • add more remote runners such compute instances.
  • implements vpc access
  • implement optional post-processing (replace, upsert data at destination instead of append?)

• Big kudos to Airbyte for all the hard work on connectors!
• The generation of the sample connector configuration in yaml is heavily inspired from the code of octavia CLI developed by Airbyte.