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In Apache Cassandra Lunch #25: Cassandra Use Cases – Reference Architectures, we cover how Cassandra is used for real-time data platforms; as well as, cover different reference architectures in which Cassandra is and can be used. The live recording of Cassandra Lunch, which includes a more in-depth discussion, is also embedded below in case you were not able to attend live. If you would like to attend Apache Cassandra Lunch live, it is hosted every Wednesday at 12 PM EST. Register here now!Read more
Welcome to the “Diving deep into Gartner’s Top 10 Data and Analytics Technology Trends for 2019” blog series. In this series, we’ll explore the following top 10 Data & Analytics 2019 trends identified by Gartner.Continue reading
Once a new distributed platform is implemented, any growing organization needs to be able to properly scale the system. Scaling a system for the sake of scaling it doesn’t make sense because cloud resources cost money. The best way to see when and how to scale is to properly monitor the software.Continue reading
Thus far we’ve discussed how Cassandra, Spark, Kafka, Docker, and Kubernetes can be useful to build a global data platform. These components are powerful in their own right and managing them is a little simpler if we decide to use commercial components from DataStax and Confluent.
There are other tools and services we can use to further accelerate our timeline to deliver a world-class global data and analytics platform. Although bringing up a distributed data (Cassandra), distributed computing (Spark), and distributed communication (Kafka) is a great start for a framework, it still needs a few more components to make it a “Platform” which allows quick creation and delivery of services that an enterprise can use.Continue reading
It’s much easier to iterate your platform on containers before deciding to use more “traditional” computing systems in Staging or Production. It’s not a requirement to use Docker or Kubernetes, but even if the system is using containers all the way up to Production, many of the DevOps cycles can be done more quickly because of how quickly environments can be refreshed.Continue reading