shadow VS Apache Spark

So: I am making a JavaFX application to act as a remote console for my server. I'm the only person that will have a copy of it, so it doesn't need to be snazzy or 100% efficient. I have turned the project into a jar via ShadowJar, turned that into an EXE via Launch4j, and yesterday turned that into an installer via Inno.

I’ve never tried this and I I’m not libgdx expert, but you can try running a Uber jar with shadow. it’s the Gradle equivalent of the shade plug-in for maven builds.

When you get into a situation where you need to sandbox a dependency within another, including this example, where you want each of your dependencies to get its own unique instance of their dependency, you can "shadow" the dependency that is to be sandboxed this way. Look into https://github.com/johnrengelman/shadow.

Try using the shadow plugin for gradle https://github.com/johnrengelman/shadow

and also apparently, it's a problem with shadowJar itself https://github.com/johnrengelman/shadow/issues/713

Behind the scenes, Shadow creates a FatJar with all the things we may need in the future.

Note: **According to Serverless documentation you can use the command **serverless deploy -v. However, with Java-based-projects that’s not true because it needs to create a Fatjar with all the needed requirements (so the command will fail). By default, Serverless will set **shadow** as a dependency in the gradle file for those purposes. The deploy command will generate a build file called -all.jar that is the one that’s going to be uploaded to the AWS Lambda function.

The result .jar is going to submitted to Apache Spark cluster (e.g. spark-submit command). So, it should contain all runtime artefacts. Unfortunately, the standard Spring Boot packaging does not put the dependencies in the way Apache Spark expects it. So, we'll use shadow-jar Gradle plugin. Take a look at the example below.

I've writen many Kotlin+Gradle projects that produce jars without the shadow plugin. They're not fat, shaded, standalone executable jars though - is that what you need?

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.