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Why Kubernetes Is the Best Technology for Running a Cloud-Native Database

We’ve been talking about migrating workloads to the cloud for a long time, but a look at the application portfolios of many IT organizations demonstrates that there’s still a lot of work to be done. In many cases, challenges with persisting and moving data in clouds continue to be the key limiting factor slowing cloud adoption, despite the fact that databases in the cloud have been available for years. 

For this reason, there has been a surge of recent interest in data infrastructure that is designed to take maximum advantage of the benefits that cloud computing provides. A cloud-native database achieves the goals of scalability, elasticity, resiliency, observability, and automation; the K8ssandra project is a great example. It packages Apache Cassandra and supporting tools into a production-ready Kubernetes deployment.

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Simplify Your Microservices Architecture With a Data API

This is an article from DZone's 2022 Microservices and Containerization Trend Report.

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Have you encountered challenges in how to manage data in a microservices architecture? In this article, we examine traditional approaches and introduce the data API gateway (also sometimes known as a "data gateway"), a new type of data infrastructure. We explore the features of a data API gateway, why you should implement it, and how to apply it to your architecture. 

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Why a Cloud-Native Database Must Run on K8s

We’ve been talking about migrating workloads to the cloud for a long time, but a look at the application portfolios of many IT organizations demonstrates that there’s still a lot of work to be done. In many cases, challenges with persisting and moving data in clouds continue to be the key limiting factor slowing cloud adoption, despite the fact that databases in the cloud have been available for years.

For this reason, there has been a surge of recent interest in a data infrastructure that is designed to take maximum advantage of the benefits that cloud computing provides. A cloud-native database is one that achieves the goals of scalability, elasticity, resiliency, observability, and automation; the K8ssandra project is a great example. It packages Apache Cassandra and supporting tools into a production-ready Kubernetes deployment.

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Multi-Cluster Cassandra Deployment With Google Kubernetes Engine (Pt. 2)

This is the second in a series of posts examining patterns for using K8ssandra to create Cassandra clusters with different deployment topologies.

In the first article in this series, we looked at how you could create a Cassandra cluster with two datacenters in a single cloud region, using separate Kubernetes namespaces in order to isolate workloads. For example, you might want to create a secondary Cassandra datacenter to isolate a read-heavy analytics workload from the datacenter supporting your main application.

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How to Put a Database in Kubernetes

The idea of running a stateful workload in Kubernetes (K8s) can be intimidating, especially if you haven’t done it before. How do you deploy a database? Where is the actual storage? How is the storage mapped to the database or the application using it?

At KubeCon North America 2021, I gave a talk on “How to put a database in Kubernetes” where I demystified the deployment of databases and stateful workloads in K8s. Basically, it boils down to a few key steps:

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Deploy a Multi-Datacenter Apache Cassandra Cluster in Kubernetes (Pt. 1)

The Get Started examples on the K8ssandra site are primarily concerned with spinning up a single Apache Cassandra™ datacenter in a single Kubernetes cluster. However, there are many situations that can benefit from other deployment options. In this series of posts, we’ll examine different deployment patterns and show how to implement them using K8ssandra.

Flexible Topologies With Cassandra

From its earliest days, Cassandra has included the ability to assign nodes to datacenters and racks. A rack was originally conceived as mapping to a single rack of servers connected to shared resources, like power, network, and cooling. A datacenter could consist of multiple racks with physical separation. These constructs allowed developers to create high-availability deployments by replicating data across different fault domains. This ensured that Cassandra clusters remain operational amid failures ranging from a single physical server, rack, to an entire datacenter facility. 

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Why We Decided to Build a K8ssandra Operator – Part 4

In the first, second, and third posts in this series, we’ve shared conversations with K8ssandra core team members on our journey to build a Kubernetes operator for K8ssandra. We’ve discussed the virtues of the Helm package manager versus Kubernetes operators for deploying and managing infrastructure in Kubernetes and some of our implementation choices for the operator.

In this final post of the series, we pick up from the previous post with a discussion of how we decided to structure our projects in GitHub, how we test the K8ssandra operator, and our hopes for how the operator will expand the K8ssandra developer community.