Following up on our previous post on saving data to Scylla, this time, we’ll discuss using Spark Structured Streaming with Scylla and see how streaming workloads can be written in to ScyllaDB. This is the fourth part of our four part series.
Last time, we discussed how Spark executes our queries and how Spark’s DataFrame and SQL APIs can be used to read data from Scylla. That concluded the querying data segment of the series; in this post, we will see how data from DataFrames can be written back to Scylla.
In part 2 of our Scylla and Spark series, we will delve more deeply into the way data transformations are executed by Spark, and then move on to the higher-level SQL and DataFrame interfaces.
Welcome to part 1 of an in-depth series of posts revolving around the integration of Spark and Scylla. In this series, we will delve into many aspects of a Spark and Scylla solution: from the architectures and data models of the two products, through strategies to transfer data between them and up to optimization techniques and operational best practices.