liftbridge VS debezium

Liftbridge (https://liftbridge.io/) is more or less the NATS Kafka versioning.

[Gufo Liftbridge](https://pypi.org/project/gufo-liftbridge/) is the Python asyncio Liftbridge client.

https://liftbridge.io/

Apache Pulsar might be worth a look, but it's actually more complex under the hood than Kafka, but has a lot of features built-in that either aren't in FOSS Kafka yet, like tiered storage, or won't be until Confluent doesn't dominate the PMC (like an integrated schema registry), or just can't be done very nicely, if at all, like decent multi-tenancy.

That said, it's a fast moving target, the code quality last I looked was patchy in places, ditto the documentation for both it and Bookkeeper, and the admin overhead is higher (managing bookies and brokers and Zookeepers vs. just brokers and ZK with Kafka, or when KRaft is production ready, just brokers).

This is basically an ad for GCP right?

That said, it looks like Kafka is by far and away the way to handle persistent logs/events at scale. AFAIK a company here in Japan called LINE has all their messaging flowing through a large kafka cluster themselves.

Wonder if anyone is running large NATS Jetstream[0]/Liftbridge[1] or Pulsar[2] (yahoo runs those) clusters. I guess Pulsar might be #2 in terms of adoption at large scale?

[0]: https://docs.nats.io/jetstream/jetstream

[1]: https://liftbridge.io/

[2]: https://pulsar.apache.org/

Do you need the messages to be durable, if so you can have a look at Liftbridge: - https://liftbridge.io/ - https://github.com/liftbridge-io/liftbridge

And if you want closer kafka semantics built on top of nats, check out liftbridge:

https://liftbridge.io/

They manage data in the application layer and your original data stays where it is. This way data consistency is no longer an issue as it was with streaming databases. You can use Change Data Capture (CDC) services like Debezium by directly connecting to your primary database, doing computational work, and saving the result back or sending real-time data to output streams.

Both of these articles mention a key player, Debezium. In fact, Debezium has had a place in the modern infrastructure. Let's use a diagram to understand why.

This might be overkill, but you could use an extra tool like https://debezium.io to capture logs about all creates, updates, and deletes in your table

If you're considering a relational database for an event-driven architecture, check out Debezium. It lets you stream changes to relational databases, and subscribe to change events.

Debezium

https://debezium.io/ comes to mind as an open source product, but there are a gazillion of these tools out there.

Contrary to what it sounds, it’s quite straightforward. The database changes are captured via Debezium and published as events on Apache Kafka. ClickHouse consumes those changes in partial order by Kafka Engine. Real-time and eventually consistent.