Drools VS Apache Flink

See https://cacm.acm.org/magazines/2023/6/273222-the-silent-revolution-of-sat/fulltext and also modern production rules engines like https://drools.org/ Oddly, back when “expert system shells” were cool people thought 10,000 rules were difficult to handle, now 1,000,000 might not be a problem at all. Back then the RETE algorithm was still under development and people were using linear search and not hash tables... - Source: Hacker News / 5 months ago

Drools – an open-source business rule management system that allows developers to create and manage complex decision logic. Source: about 1 year ago

- Drools - Available in JVM environments (Java, Scala and similar) - uses FEEL for expression language. Source: about 1 year ago

GoRules is a modern, open-source rules engine designed for high performance and scalability. Our mission is to democratise rules engines and drive early adoption. Rules engines are very useful as they allow business users to easily understand and modify core business logic with little help from developers. You can think of us as a modern, less memory-hungry version of Drools that will be available in many... Source: about 1 year ago

Is this something like Drools? It's quite uncommon but it is used in situations where certain sets of business rules change a lot and you want business analysts to be able to quickly change them in a simple graphical UI. Source: over 2 years ago

You should let the Apache Flink team know, they mention exactly-once processing on their home page (under "correctness guarantees") and in their list of features. [0] https://flink.apache.org/ [1] https://flink.apache.org/what-is-flink/flink-applications/#building-blocks-for-streaming-applications. - Source: Hacker News / 7 days ago

Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / about 1 month ago

Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 3 months ago

Also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc. - Source: dev.to / 5 months ago

Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features. - Source: dev.to / 5 months ago