Recently, I worked on a project to consume Kafka message and ingest into Hive using Spark Structure Streaming. I mainly used python for most of the work with data pipeline construction, and this project is not exception.
Everything moved smoothly at the beginning when launching first Spark Structure Streaming to read simple message in raw text format from Kafka cluster. The problem was rising when I tried to parse the real Kafka message serialized in Avro format.
To quickly launch spark and kafka for local development, docker would be the top choice as of its flexibily and isolated environment. This save lot of time for manually installing bunch of packages as well as conflicting issues.
This post provides a general setup to start with Spark development on local computer. This is helpful for getting started, experimenting the Spark functionalities or even run a small project.
Understand Spark hierarchy in term of hardware and software design will help you better in develop an optimized Spark application.
Spark is an unified engine designed for large scale distributed data processing and machine learning on compute clusters, whether running on-premise or cloud. It replaces Hadoop MapReduce with its in-memory storage for intermediate computations, making it much faster (100x) than Hadoop MapReduce.
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