There are many public clouds provide managed Apache Spark as service, such as databricks, AWS EMR, Oracle OCI DataFlow, see the table below for a detailed list.
However, the way to deploy Spark application and launch Spark application are incompatible among different cloud Spark platforms.
spark-etl is a python package, provides a standard way for building, deploying and running your Spark application that supports various cloud spark platforms.
Your application using spark-etl can be deployed and launched from different cloud spark platforms without changing the source code.
An application is a python program. It contains:
main.py file which contains the application entrymanifest.json file, which specify the metadata of the application.requirements.txt file, which specify the application dependency.In your application's main.py, you shuold have a main function with the following signature:
spark is the spark session objectinput_args a dict, is the argument user specified when running this application.sysops is the system options passed, it is platform specific. Job submitter may inject platform specific object in sysops object.main function's return value should be a JSON object, it will be returned from the job submitter to the caller.def main(spark, input_args, sysops={}):
# your code here
Here is an application example.
etl -a build -c <config-filename> -p <application-name>
etl -a deploy -c <config-filename> -p <application-name> -f <profile-name>
etl -a run -c <config-filename> -p <application-name> -f <profile-name> --run-args <input-filename>
| You setup your own Apache Spark Cluster. |
| Use PySpark package, fully compatible to other spark platform, allows you to test your pipeline in a single computer. |
| You host your spark cluster in databricks |
| You host your spark cluster in Amazon AWS EMR |
| You host your spark cluster in Google Cloud |
| You host your spark cluster in Microsoft Azure HDInsight |
|
| You host your spark cluster in Oracle Cloud Infrastructure, Data Flow Service |
| You host your spark cluster in IBM Cloud |
For job deployers, please check the wiki .
For job submitters, please check the wiki
FAQs
Generic ETL Pipeline Framework for Apache Spark
We found that spark-etl demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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