n0rdy-blog-code-samples VS flink-kubernetes-operator

All the code examples are available here. If you run this code, you'll see the same JSON printed to the terminal as above.

You can find it in this GitHub repo.

The next step should be to replace the slice with a channel to define the customers' queue. But how should we get the customers to put in that channel? Another good question, you are on fire today! We need a function to generate random customers and, ideally, with random waiting to make it more realistic, as customers come to the post office at a different frequency. For that, I asked ChatGPT to generate a list of 50 random names and 50 random Xmas presents that fit into the postal package. I won't provide these lists here to save space, but feel free to check them out on a GitHub repo. Once we have it, the rest of the generator code is trivial:

This and other code examples are available in this GitHub repo.

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.