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 and delves into include custom resource definitions (CRDs), operators, and HELM Charts, which include future and peripheral feature capabilities that can help you host various complex distributed systems. I’ve included references below the video here, enjoy. 

In the last blog post, we introduced the dsbulk command, some basic loading examples, and dove into some mappings.  In this blog post, we are going to look into some additional elements for data loading.

As a software industry veteran I’ve s̶e̶e̶n̶ / e̶x̶p̶e̶r̶i̶e̶n̶c̶e̶d̶ / i̶n̶f̶l̶i̶c̶t̶e̶d̶ / been victimized by any number of inventive approaches to integrating and testing distributed systems, so the title of this post is a bit tongue-in-cheek. I’ve been sharing about my experience building a Python implementation of the KillrVideo microservice tier. In the previous posts, I shared how I got started on this project, about building GRPC service stubs and advertising the endpoints in etcd. This time, I’d like to elaborate about why I built this service scaffolding first before implementing any business logic.

Lightweight transactions are extremely powerful when used correctly.  They not only enable you to use a highly durable distributed system in an ACID-like way, but they also allow you to do it with ease. In this blog, we'll explore lightweight transactions, show how DSE implements them, and call out a few pitfalls to keep in mind.

The DataStax Bulk Loader, dsbulk, is a new bulk loading utility introduced in DSE 6.  It solves the task of efficiently loading data into DataStax Enterprise, as well as efficiently unloading data from DSE and counting the data in DSE, all without having to write any custom code or using other components, such as Apache Spark.  In addition to the bulk load and bulk unload use cases, dsbulk aids in migrating data to a new schema and migrating data from other DSE systems or from other data systems. There is a good high-level blog post that discusses the benefits of dsbulk:

• Easy to use.
• Able to support common incoming data formats.
• Able to export data to common outgoing data formats.
• Able to support multiple field formats, such as dates and times.
• Able to support all the DSE data types, including user-defined types.
• Able to support advanced security configurations.
• Able to gracefully handle badly parsed data and database insertion errors.
• Able to report on the status and completion of loading tasks, including summary statistics (such as the load rate).
• Efficient and fast.

Now, I’m a person who learns by example, so what I’m going to do in this series of blog posts is show some of the ways to use dsbulk to do some common tasks.  For the documentation on dsbulk, including all of the parameters and options, see the documentation pages for dsbulk.

The Java driver team is pleased to announce the general availability of two new major versions: OSS driver 4.0.0 and DSE driver 2.0.0.

These are long-awaited versions that address longstanding issues with the 3.x line:

• drop the dependency to Guava, and update the API to use Java 8 futures;
• make the driver more pluggable and better expose the internals;
• clean up the codebase and make it more modular.

Many architects and developers today are targeting Apache Cassandra™and DataStax Enterprise (DSE) as their database of choice because of its ability to easily scale and perform under extreme workloads. Developers like the fact that no modifications are required on their part if the application is initially written to just use one DSE node, but scales into hundreds in the future.

Here’s a talk Cedrick Lunven (who I have the fortune of working with!) about creating API’s for your database, your distributed database. He starts out with a few objectives for the talk:

1. Provide you a working API implementing Rest, gRPC, and GraphQL.
2. Give implementation details through Demo.
3. Reveal hints to choose and WHY, (specifically to work with Databases)

DataStax powers the Right-Now Enterprise with the always-on, distributed cloud database built on Apache Cassandra™ and designed for hybrid cloud. So let's get started.

There are several options to install DSE which are documented here. In this tutorial, I will use the Lifecycle Manager (LCM) which allows you to easily provision, configure, and install DataStax Enterprise clusters.

Snitches. What a great name for a feature right? I’d bring up the Harry Potter thing, but I’m gonna let that one fly. (get it, it flies!)