DataStax Developer Blog

I'm happy to announce built-in TypeScript support in our DataStax Node.js drivers for Apache Cassandra and DataStax Enterprise Node.js Driver.

Today we released into DataStax Labs the DataStax Java Driver Spring Boot Starter. This starter streamlines the process of building standalone Spring-based applications that use Apache Cassandra™ or DataStax databases. This is preview software so we have not yet pushed this to Maven Central and instead provide a tarball artifact that contains the starter jar, the starter test jar, README files, the demo described later in this blog post, and an install_maven.sh bash script.

DataStax's Java driver for Cassandra/DSE has a very useful feature called Object Mapper that greatly simplifies the development of the code that accesses the database. 

The first three blog posts in this series dealt with data loading (here, here, and here), and the fourth blog post (here) dealt with data unloading.  This blog will deal with the count mode for dsbulk.

Something new in dsbulk version 1.1.0 is the ability to count the data in a table.  This is a common task that folks do once the load data to ensure that the data was loaded correctly.  It could be that a load failed midway and they would like to see how far it got. It could be that the primary key was not what was desired, and instead of unique inserts it became overwrites.

This is the latest installment of a series about building a Python application with Apache Cassandra — specifically a Python implementation of the KillrVideo microservice tier. In previous posts I shared what motivated this project, how I started with infrastructure including GRPC and Etcd, the testing approach, and most recently, how I began implementing data access using Cassandra.

In this post we’ll look at some additional examples of data access using the DataStax Python Driver, ranging from the simple to the complex. (I’ll be making reference some driver / mapper concepts from the previous post so you may want to review that if you haven’t already.)

In the previous 3 blog posts (here, here, and here), we covered some loading examples, and covered some of the common options, such as logging and connection details.  In this blog post, we will turn our attention to unloading.

Some notes along with this talk. Which is about ways to mitigate super nodes, partitioning strategies, and related efforts. Jonathan’s talk is vendor neutral, even though he works at DataStax. Albeit that’s not odd to me, since that’s how we roll at DataStax anyway. We take pride in working with DSE but also with knowing the various products out there, as things are, we’re all database nerds after all. (more below video)

We recently announced availability of DataStax Distribution of Apache Cassandra on Microsoft Azure aimed at helping developers take advantage of a simpler, more cost-effective way to build and scale applications. We wanted to take our experience with large scale deployments on Azure for over 5 years and deliver an environment with best practices built-in so you can focus on your business. With that in mind, let’s jump into what you need to know to build an app.

Apache Spark! Darling of the Big Data world and the easiest entry point into Machine Learning. It's fast, it's cool, and it's hip. So many great things, but should you be using it in your stack?

Over the first few posts of this series, I’ve been sharing about my experience building a Python implementation of the KillrVideo microservice tier. In the previous posts I shared why I started this project, about building GRPC service stubs, advertising the endpoints in etcd, and setting up integration tests to exercise the service APIs.