Strange Loop

Zipkin: a distributed tracing framework

Big Data has dramatically increased the complexity of building data systems. Big Data forces you to leave the comfortable world of ACID, transactions, and relations, and thrusts you into a challenging world of distributed systems, CAP, and restrictive data models.

You cannot battle complexity with ever more complex systems. This leads to to restrictive systems that are difficult to operate and have poor performance. The only way to reasonably address the complexity of Big Data systems is to fundamentally rethink your approach to avoid that complexity in the first place. A key insight is that the ability to store and process very large amounts of data opens up entirely new ways of building systems that were not possible pre-"Big Data".

NoSQL is not a panacea. Nor is Hadoop, Storm, or any of the other tools out there for Big Data. Yet there is a way to use these tools in conjunction with one another to build complete and robust realtime data systems with a minimum of complexity. These techniques are possible today and can be implemented and operated by small teams.

In this talk you'll learn:

Johan Oskarsson

Johan Oskarsson