pippin VS flink-kubernetes-operator

take a look at the concurrent code written by other devs out there: for example, feel free to check the internals of my library Pippin, but I bet there are many better projects out there to learn from - Google/Bing/DuckDuckGo/Kagi and ChatGPT can help to find the right one

Christmas season is around the corner, that's why another evening I was standing in a long queue at the post office with some Xmas presents packed inside the box. The line moved pretty slowly, as there was only one postman for the whole crowd of customers. The guy was running back and forth, and I felt really sorry for him. Not sure why, either out of boredom or because of several long evenings I spent working on my open-source library for managing asynchronous pipelines, but my brain turned engineering mode on and tried to optimize the process of handling parcels.

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, implementing a real-time anomaly detection model in Kafka Streams would require translating Python code into Java, slowing down pipeline performance, and requiring a complex initial setup.

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.

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.

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.

Due to the technology transformation we want to do recently, we started to investigate Apache Iceberg. In addition, the data processing engine we use in house is Apache Flink, so it's only fair to look for an experimental environment that integrates Flink and Iceberg.