In the Day 6 post, we expanded the initial Mutant Monitoring Scylla Cluster from one to two datacenters. Now we must begin to learn more about how to work with our data in such a setup. In this post, you will learn more about the consistency level options available when using Scylla with multiple datacenters.
Now, at Division 3 we must prepare for disaster readiness by expanding our Scylla cluster across geographic regions in a multi-datacenter configuration. In this post, we will set up a new Scylla cluster in another datacenter and learn how to convert our existing keyspaces to be stored in both datacenters.
Apache Zeppelin is a Java Web-based solution that allows users to interact with a variety of data sources like MySQL, Spark, Hadoop, and Scylla. Once in Zeppelin, you can run CQL queries and view the output in a table format with the ability to save the results. In this post, you will learn how to visualize the data in MMS with Apache Zeppelin.
If you got this far, you have your Mutant Monitoring set up and running with the Mutant Catalog and Tracking keyspaces populated with data. We must now prepare to plan for disaster scenarios so that we know for sure that we can survive an attack. In this post, we will go through a node failure scenario and learn about consistency levels.
Presto is a distributed SQL query engine for Big Data technologies like Scylla. With it, you can run complex queries such as full-text search, compare values, joins, and query data from multiple data sources. In this post, you will learn how to use Presto to run queries from the Mutant Monitoring System and view statistics from the web console.
Learn how to build the monitoring part of the system by creating the keyspace in Scylla. This post teaches you about different compaction strategies available in Scylla and how to run basic CQL queries.
This is the first of the Mutant Monitoring System series. In this post, we set up a Scylla cluster in Docker and create the initial keyspace for the Mutant Catalog.