Arm your DevOps Team with Big Data

Abstract:

The IT industry is changing fast, and DevOps teams need tools that can keep up with the pace –making their job easier, not more difficult. Machine data analytics combines the agility and flexibility needed by DevOps teams with the deep insight of Big Data-style analytics, but it’s all about the right tools and approach. This session explores strategies that align data analytics with the goals and workflow of your DevOps team.

Some key take-aways will include:

Let the data speak for itself: Make only basic assumptions about the data being consumed (time stamped, text-based, etc.). The important patterns should be determined by the data itself, and not by pre-judging what patterns are relevant, and which are not. Your application rapidly changes, the patterns that are significant – both good and worrisome– will also change accordingly.

Continuous reinterpretation: Never try to force the machine data into tired old buckets that are forever out of date. The data should be stored raw so that it can continually be reinterpreted and re-parsed to reveal new meaning. Fast moving DevOps teams can’t wait for the stodgy software vendor to change their code or send their consultant onsite. They need it now.

Any metric you want, any time you want it: With a DevOps approach to machine data analytics, the people that know the app the best, the developers, produce the metrics needed to keep the app humming. Adding a new metric can be as simple as writing a single line of code.

Set the data free: Free-flow of data is the new norm, and mash-ups provide the most useful metrics. Specifically, pulling business data from outside of the machine data context allows you to put it in the proper perspective.

Speaker:

Christian Beedgen, Co-Founder and CTO at Sumo Logic