This series explores essential concurrency patterns in Go, essential for building scalable systems in today’s multi-core environments. Covering 12 patterns with practical examples and use cases, it aims to deepen Go developers’ understanding of concurrency, enhancing their ability to design robust applications. The first focus is on the Pipeline Pattern. Continue reading →

The article examines the CAP Theorem and its impact on distributed systems, focusing on Apache Kafka, Flink, and Cassandra. It discusses trade-offs in consistency, availability, and partition tolerance, especially during network partitions. Moreover, it provides deployment strategies and mitigation techniques for challenges within these systems, including insights on Confluent Cloud’s management of CAP issues. Continue reading →

If you’re interested in running data-intensive systems (think Apache Cassandra, DataStax Enterprise, Kafka, Spark, Tensorflow, Elasticsearch, Redis, etc) in Kubernetes this is a great talk. @Lenadroid covers what options are available in Kubernetes, how architectural features around pods, jobs, stateful sets, and replica sets work together to provide distributed systems capabilities. Other features she continues... Continue reading →

The last post in this series is Distributed Database Things to Know: Consistent Hashing. Let’s talk about the analogy of Apache Cassandra Datacenter & Racks to actual datacenter and racks. I kind of enjoy the use of the terms datacenter and racks to describe architectural elements of Cassandra. However, as time moves on the relationship... Continue reading →

Over the last week I had the privilege and adventure of coming out to Chicago and Dallas to teach about operations and security capabilities of DataStax Enterprise. More about that later in this post, first I’ll elaborate on and answer the following: What is DataStax Developer Day? Why would you want to attend? Where are... Continue reading →