In the past year, Kubernetes proved itself as a powerful tool for creating and managing service based architectures. However, as with anything new, it is easy to find yourself lost when the complexity …

Big data architectures and enterprise/microservice architectures are slowly converging. Big data is transitioning to "fast data," emphasizing streaming over batch processing, while data processing is growing ubiquitous. Dean Wampler explores the SMACK stackSpark, Mesos, Akka, Cassandra, and Kafkaand explains how it addresses the needs of both fast data and the enterprise.

In the past few years, there has been a proliferation of production-ready open source databases, giving developers and operators more choices than ever. Joseph Lynch explores how Yelp has combined complimentary data stores to provide a powerful data tier for our developers. Along the way, Joseph shares lessons learned about deployment, configuration, and monitoring from a production environment.

Jeff Carpenter describes how data modeling can be a key enabler of microservice architectures for transactional and analytics systems, including service identification, schema design, and event streaming.

Adam Bordelon and Mohit Soni demonstrate how projects like Apache Myriad (incubating) can install Hadoop on Mesosphere DC/OS alongside other data center-scale applications, enabling efficient resource sharing and isolation across a variety of distributed applications while sharing the same cluster resources and hence breaking silos.

Finding the right balance between writing custom in-house software and using an off-the-shelf solution is difficult. Aish Raj Dahal sheds light on the age old build versus buy problem and "not invented here syndrome" by explaining how PagerDuty built a distributed task scheduler and later moved off it to use an off-the-shelf open source solution.

Which venues have similar visiting patterns? How can we detect when a user is on vacation? Can we predict which venues will be favorited by users by examining their friends' preferences? Natalino Busa explains how these predictive analytics tasks can be accomplished by using Spark SQL, Spark ML, and just a few lines of Scala code.

Jon Haddad and Dani Traphagen explore all the basics youll need to become best buds with the radically scalable, always-on, and increasingly popular Apache Cassandra database. But wait, there's more. Jon and Dani also cover using Apache Spark for large-scale data processing. What else do you need to know to make new friends?

AWS, Azure, and GCP all provision clusters differently. Docker, Kubernetes, and Mesos are seeking to simplify that process but haven't fully delivered on that promise. Ben Lackey and Boris Baryshnikov explain the best way to provision with the tools available today as we wait for the perfect orchestration layer to arrive.

Roopa Tangirala details Netflix's migration from Oracle to Cassandra, covering the problems encountered, what worked and what didn't, and lessons learned along the way.

Developers who want both streaming analytics and ad hoc, OLAP-like analysis have often had to develop complex architectures such as Lambda. Helena Edelson and Evan Chan highlight a much simpler approach using the NoLambda stack (Apache Spark/Scala, Mesos, Akka, Cassandra, Kafka) plus FiloDB, a new entrant to the distributed-database world, which combines streaming and ad hoc analytics.