jitsu VS airbyte

you may check: https://github.com/jitsucom/jitsu. "Jitsu is an open-source Segment alternative. Fully-scriptable data ingestion engine for modern data teams. Set-up a real-time data pipeline in minutes, not days"

You can create an API endpoint, and send those JSON to it. In the "destination" part, it can sync to clickhouse (one of many choices, like redshift, snowflake,besides clickhouse) very quickly, and flatten the JSON into columns. If there is new key found in JSON, it will create a new column in clickhouse.

We also have telemetry set up on our Monosi product which is collected through Snowplow,. As with Airbyte, we chose Snowplow because of its open source offering and because of their scalable event ingestion framework. There are other open source options to consider including Jitsu and RudderStack or closed source options like Segment. Since we started building our product with just a CLI offering, we didn’t need a full CDP solution so we chose Snowplow.

Ingestion / Extraction: Airbyte, Singer, Jitsu

Ingestion / ETL: Airbyte, Singer, Jitsu Transformation: dbt Orchestration: Airflow, Dagster Testing: GreatExpectations Observability: Monosi Reverse ETL: Grouparoo, Castled Visualization: Lightdash, Superset

I’m just saying this is better:

We are building Jitsu, (https://github.com/jitsucom/jitsu, https://jitsu.com/) We help companies collect events from their apps, websites, and APIs and send them to databases.

Think of us as an open-source Segment alternative.

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