n0rdy-blog-code-samples VS Apache Spark

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.

Recently I had to revisit the "JVM languages universe" again. Yes, language(s), plural! Java isn't the only language that uses the JVM. I previously used Scala, which is a JVM language, to use Apache Spark for Data Engineering workloads, but this is for another post 😉.

Consume data into third party software (then let Open Search or Apache Spark or Apache Pinot) for analysis/datascience, GIS systems (so you can put reports on a map) or any ticket management system

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.

Apache Spark - https://spark.apache.org/

A JVM based framework named "Spark", when https://spark.apache.org exists?

I recently saw a pull request that was merged to the Apache/Spark repository that apparently adds initial Python bindings for PySpark on K8s. I posted a comment to the PR asking a question about how to use spark-on-k8s in a Python Jupyter notebook, and was told to ask my question here.