Distributed Data Show

Developers need new development patterns in order to embrace Cloud. As all resources are shared resiliency and security are keys.

How has the industry changed with the rise of cloud and software as a service? What does this mean for the future? All this and more will be covered in today's Distributed Data Show.

David chats with Cristina about her non-traditional background breaking into the tech sector, and the journey that lies ahead as she takes on her new role of Developer Advocate at DataStax.

We talk with Chris Splinter regarding Spring Data Cassandra, a slight change in our approach in how we talk about it, and a glimpse into the possible future improvements in the Java Driver to make it easier to use with the Spring Framework.

Spring Boot is a powerful framework helping developers building applications fast. In this episode Franck explains us how he came to Spring in the first place and what are the good and bad sides about it.

One of the most common use cases when dealing with Apache Cassandra™ are timeSeries. After introducing the concept of Time series in a few words Alice and Cedrick will analyze why Cassandra got so much traction and detail what we see at customers, what are the pitfalls and what are today’s challenges.

The new 2.0 version of the DataStax Java Driver introduced some new patterns for accessing your data : reactive programming. In this episode, Alexandre explains the reactive APIs available in the new driver and the "sweetspots" for when to use them.

DataStax Engineer Alexandre Dutra gives us a tour of the new DataStax Java Driver 4.0 and offers some tips for a smooth migration for your Java app.

Cassandra is a highly available, partition-tolerant database. In the modern world, what happens when you need strong consistency but still require the unique partition tolerance that Cassandra provides. Enter the Lightweight transaction to provide ACID like transactions in distributed Cassandra workloads.

Go and Apache Cassandra Joining Forces with Go-Cql | Ep. 90 Distributed Data Show by DataStax Developers

Amanda and Jeff talk about their latest project: implementing the microservice layer of KillRVideo in Python. They talk about what was easy, what was not too easy, and what was just plain fun. They spend some time on discussing recommendations engines and ask the audience should they implement this in DSE graph or using PySpark or other python package? Comment below!

Denise Gosnell interviews Dave Bechberger live at Data Day Texas regarding challenges when developing Graph based applications, recommendations on approaches to take, and what resources are available for developers new to Graph.

Description - Jeff and Tim talk about the most common questions developers have about Kafka and three great ways to combine Kafka with Cassandra in your applications.

Patrick talks with Holden Karau, Developer Advocate at Google, about ways to incorporate Spark in your application without impacting performance or having to learn Scala.

Jeff Carpenter and Amanda Moran discuss the big questions the DataStax Developer Advocate team hears most frequently from our developer community, and introduce a series of episodes to answer questions about application development.