This series is an introduction to some specific aspects of Gremlin to help you get more familiar with the traversal language.

Here are a set of simple examples demonstrating the capabilities of DataStax Studio Graph. We'll go over the visual aspects of displaying edges and vertices, interacting with objects in the canvas, and how Studio can help you create your graph traversals.

This series takes a look at some of the challenges that users of Apache Cassandra and DataStax Enterprise encounter when creating data models for real world applications. There are a lot of great resources available to learn the basics of Cassandra data modeling, but this series focuses on that next level of questions you’ll run into when you’re developing your application.

We have lots of helpful videos on DataStax Academy. Some cover big topics, like Apache Cassandra data modeling and some answer single questions like how do I install DataStax Enterprise. If our big course-long topics are "full course meals," these single question, DataStax Enterprise Recipes are "quick snacks."

Go through the first steps to downloading and installing DataStax Enterprise, the database for cloud applications. This is the first tutorial in the recipe series, getting started with DataStax Enterprise.

DataStax Enterprise 5.0, the database platform for cloud applications, includes Apache Cassandra 3.x with materialized views, tiered storage and advanced replication. Introduced in 5.0 is DataStax Enterprise Graph, the first graph database fast enough to power customer-facing applications, scale to massive datasets and integrate advanced tools to power deep analytical queries.

This multi-part overview empowers you to get up and running with the latest features in DataStax Enterprise 5.0.

What's New

Apache Cassandra 3.x brings even more efficiency, flexibility, and cost savings to DataStax Enterprise 5.0, starting with a revamped storage engine that helps model data in a more practical and less duplicated way. Materialized views give developers greater flexibility to quickly query and leverage existing tables, while improved hint storage provides more efficiencies when handling replica failures.

Tiered Storage helps you save money by giving you the power to choose where data is stored, giving developers the ability to use more expensive SSDs for hot data and cheaper storage for cold data.  

Advanced Replication brings another level of control to distributed databases. Users now have the ability to create separate, autonomous database spokes that can talk to a central hub at the table level. Sometimes remote spokes don't have continuous connectivity, letting them report back to the hub when available or as required by the application.

Multi-instance gives you the flexibility to deploy multiple nodes on a single box, using hardware resources as efficiently as possible.

DataStax Enterprise Graph provides the first real-time, scalable graph database. Built on open source technologies like Apache Tinkerpop and the graph query language Gremlin, the graph data model lets users create rich data connections. Additionally, DataStax Enterprise Graph comes fully integrated with the rest of the data models and tools found in DataStax Enterprise.

OpsCenter 6.0 adds centralized lifecycle management to the configuration of your cluster. Giving you the ability to upgrade or add configurations like the new tiered storage to your cluster at the click of a button. 

View the full release notes.

In this tutorial, Jon Haddad, Apache Cassandra Evangelist at DataStax, will discuss a variety of tools built for monitoring and maintaining DataStax Enterprise or Apache Cassandra. In addition, Jon will cover specific tips & tricks to be aware of during setup and while diagnosing problems found in your production deployment.

This document is a guide to getting started with DataStax Enterprise (DSE) in the Google Compute Engine (GCE) cloud. First, this guide will walk through the steps needed to deploy DSE-ready nodes in GCE, and then illustrates how to deploy DSE on those nodes using DataStax OpsCenter. Finally, the guide also discusses deployment considerations to take into account when mapping DSE high-availability features to GCE high-availability mechanisms. All of the scripts in this document, as well as the source-code for the document itself, can be found at the DataStax Partner Network GitHub site. Pull requests for any of the scripts used here, or for any part of this document are welcome.

DataStax Cloud Deployment Guides deliberately avoid the use of popular devops tools like puppet, chef or docker in order to show the step by step instructions required to get up and running from the most basic tools. That way administrators can adapt their own automation suites from the detailed instructions provided here.