The Pathologies of Big “Messy” Data in Telco

A SkillsCast for this session is not available.

In this talk, Jay will share his experiences of dealing with the realities of telecoms data at scale. The tools he uses are broadly applicable outside of the domain.

Modern networks are today still being managed with tools that were built over a decade ago. It sounds crazy, but it’s true.

Nowhere is this more ludicrous than in the world of telecoms operators, where Big Data was all but invented. The technologies coming out of the social- and web-scale space are all well-and-good when the data is well formed, and understood, and where the organisation is relatively flat. But in telco, it’s a different world.

ITIL, eTOM and the Common Information Model whilst widely adopted, have done nothing to improve the realities of dealing with the typical data found in the telecoms domain. It’s messy, voluminous, fragmented, duplicated, and at times seems almost purposefully obfuscated! As our networks get larger, as access technologies proliferate, as the products get more complex and the accumulated cruft of legacy products takes a long time to shake off, the way we manage network telemetry pipelines and the data that makes sense of it simply must evolve.

In this talk, Jay will share his experiences spanning Splunk® and it’s Unix Pipe Philosophy “SPL” language, coupled with Titan, and several other tools and technologies to make sense of that data and unravel the mess, at scale.

YOU MAY ALSO LIKE:

• Ends in Data (SkillsCast recorded in December 2020)
• Ends in Data (SkillsCast recorded in December 2020)