Spark Structured Streaming with Scylla Hello again! 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. Make sure you check out all the prior blogs! Our code samples repository for this post contains an example project along with a docker-compose.yaml file with the necessary infrastructure for running the it. We’re going to use the infrastructure to run the code samples throughout the post and run the project itself, […]
Spark and Scylla: Spark DataFrames in Scylla Welcome back! 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. As always, we have a code sample repository with a docker-compose.yaml file with all the necessary services we’ll need. After you’ve cloned it, start up the services with docker-compose: After that is done, launch the Spark shell as in the previous posts […]
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.
Spark and Scylla 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. The series will include many code samples which you are encouraged to run locally, modify and tinker with. The Github repo contains the docker-compose.yaml file which you can use to easily run everything locally. In this post, we […]