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Looking Behind Microservices to Brewer's Theorem, Externalised Replication,and Event Driven Architecture

YOW! Data 2017

Scaling data is difficult, scaling people even more so.

Today Microservices makes it possible to effectively scale both data and people by taking advantage of bounded contexts and Conway's law.
But there's still a lot more theory that's coming together in our adventures in dealing with ever more data. Some of these ideas and theories are just history repeating, while others are newer concepts.

These ideas can be seen in many Microservices platforms, within the services' code but also in the surrounding infrastructural tools we become ever more reliant upon.

Mick'll take a dive into it using examples and offer recommendations after seven years of coding Microservices around 'big data' platforms. The presentation will be relevant to people wanting to move beyond REST based asynchronous platforms, to eventually consistent asynchronous designs that aim towards the goal of linear scalability and 100% availability.

Mick Semb Wever

Consultant

The Last Pickle

Australia

Mick Semb Wever works at The Last Pickle, helping customers around the world deliver and improve Apache Cassandra based solutions. With 19 years software engineering behind him, from robotic kinematics, reverse modelling in geo sciences, computer graphics languages, federated search engines, to at-scale event-driven designs, Mick has done his time in the trenches. And is now an advocate for change towards Microservices that are built upon modern data-driven practices introducing eventually-consistent highly-available solutions reliant upon Apache technologies like Cassandra, Hadoop, Spark, and Kafka.

He's always had a keen involvement in open source code, contributing to codebases along the way, and now relishes opportunities to speak at conferences and meetups, like JavaZone and the Cassandra Summit, where ever he travels. He is an Apache Member, an Apache Cassandra committer, the PMC Chair for Apache Tiles, as well as being a DataStax MVP for Apache Cassandra since 2014.