Anant Corporation Blog: Our research, knowledge, thoughts, and recommendations about building and managing online business platforms.
In one of my earlier articles, I talked about why your company or organization should adopt Sitecore as your Experience Platform. It’s a good platform for users, content authors, and developers to create compelling and engaging digital experiences as well as collect information on website traffic. Machine learning and analytics in personalized content are two of the most compelling features of Sitecore. In today’s world, companies, particularly the Fortune 500, require real-time analytics to help drive stakeholder goals.
This resource for monitoring Datastax, Cassandra, Spark, & Solr performance is just the first iteration of a longer initiative to create the best knowledge base on these real-time data platform technologies such as DataStax Enterprise (Cassandra, Spark, and Solr) as well as for Kafka, Docker, and Kubernetes. Our firm, Anant, has been working with Solr/Lucene for several years, and then over the years picked up Spark and Cassandra, and then made the logical move to become experts at and partners with Datastax.
Datastax OpsCenter is good but we’re wise enough to say, however, that it is just the beginning of the toolset needed to really understand what is happening under the hood in the component technologies that comprise of the Datastax Enterprise Platform. When monitoring to scale complex systems such as business platforms you need to review all signals, not just those that come from the database.
In our opinion Cassandra is one of best nosql database technologies we’ve used for high availability, large scale, and high speed business platforms, More specifically, we work with Datastax Enterprise version for Cassandra where the clients are above a certain size and need to have enterprise grade support 24/7 365 days a year with expertise around the world. There are many topics in which I could have written about as my first “Cassandra” post on our blog, but decided to write about what I call the three stooges of Cassandra data modeling: Larry (Tombstones), Curly (Data Skew), and Moe (Wide Partitions).