temporal_tables VS airbyte

There is also the temporal_tables extension.

[0] https://github.com/arkhipov/temporal_tables

- https://github.com/arkhipov/temporal_tables

I haven't used any of these but I work on https://xtdb.com which is also implementing SQL:2011's temporal features :)

It seems like PG has this extension here anyone ever use it?

You could look at that or ask me questions about it (disclaimer, I am the author). Also there is https://github.com/arkhipov/temporal_tables/

All good. Note there is also https://github.com/arkhipov/temporal_tables/ (which is also type 4 as a postgres extension - pretty similar to what ebean orm is doing)

The Temporal Tables Postgres extension works well. https://github.com/arkhipov/temporal_tables

If you're doing this across regions, you really really should reconsider. If you're doing it in the same data center you might be able to get away with it (but then I'm not sure why you're doing it in the first place, if the system fits in one DC then you probably can just scale up). It might be worth considering a sharded & passively combined approach -- i.e. every country has it's own data, and there's some huge public schema which consists of all the data that is drip fed in to materialized views or tables at regular intervals. You could also combine this with temporal_tables to get a very delayed but theoretically time-consistent (well, aside from clock skew across regions of course...) view of your DB to query... Really depends on the use case.

One of postgres's most underrated features. RLS is amazing, can be unseen/basically work silently if your programming language-side tools are good enough, and is documented well (like everything else):

https://www.postgresql.org/docs/current/ddl-rowsecurity.html

But the power of PG is that it doesn't stop there, if you combine this with a plugin like temporal_tables and you can segment by user and time:

https://github.com/arkhipov/temporal_tables

All of this mostly unknown to the thing that's accessing the DB. If that's not enough for you, why not add some auditing with pgaudit:

https://www.pgaudit.org/#section_three

I think it might not actually be hyperbole to say that Postgres is the greatest RDBMS database that has ever existed.

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