kestra VS kogito-runtimes

Kestra's communication is asynchronous and based on a queuing mechanism. It leverages the Micronaut framework and offers two runners: one that uses a database (JDBC) for both the message queue and resource storage, and another that uses Kafka as the message queue and Elasticsearch as the resource storage. The platform is fully extensible and plugin-based, providing a rich set of plugins for various workflow tasks, triggers, and data storage options. For those interested, the GitHub repository is available here: https://github.com/kestra-io/kestra

For everyone wondering https://github.com/kestra-io/kestra/discussions/468

If you need any guidance with your Snowflake deployment, our experts at Kestra would love to hear from you. Let us know if you would like us to add more plugins to the list. Or start building your custom Kestra plugin today and send it our way. We always welcome contributions!

But I totally agree that a large static dag is not appropriate in the actual data world with data mesh and domain responsibility.

[0] https://github.com/kestra-io/kestra

- We have plans to add another option that will replace both dependencies with jdbc (https://github.com/kestra-io/kestra/pull/368), is theses dependencies more comfortable for you?

With Kestra's innate flexibility, and many integrations, you are not locked into the choice of one ingestion method or the other. Complex workflows can be developed, whether in parallel or sequentially, to deliver both ELT and ETL processes. Simple descriptive yaml is used to connect plugins, and to create flows. Because workflows created in Kestra are represented visually, and issues can be seen in relation to individual tasks, there is no need to fear complexity. Trouble can be traced to its source in an instant, allowing you to try new things and come up with a new solution without fear. Give it a try, and let us know what you come up with!

Kestra is an orchestration and scheduling platform that is designed to simplify the building, running, scheduling, and monitoring of complex data pipelines. Data pipelines can be built in real-time, no matter how complex the workflow, and can connect to multiple resources as needed (including Debezium).

First, I transformed this Trie Tree implementation into an open-source project, which I called Praecepta (Rules in Latin), so anyone can try it out. Then, I added three modules to it, with a decision table based on a real scenario (blurred input/output values). Each module uses a different framework: Praecepta, Camunda 7, and Kogito (the newest Kie solution for the Cloud).

Please notice this new Drools Quarkus extension is not a replacement for Kogito.

https://jbpm.org, https://kogito.kie.org, and the other projects under the https://kie.org umbrella are also worth looking into. jBPM is the grandfather of several of the Java based BPMN solutions if I'm not mistaken.

Query examples: https://github.com/kiegroup/kogito-runtimes/wiki/Data-Index-Service