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Machine Data Almanac

Big Data has a BIG STORY

David Andrzejewski

David Andrzejewski is a senior engineering manager at Sumo Logic, where he works on applying statistical modeling and analysis techniques to machine data such as logs and metrics. He also co-organizes the SF Bay Area Machine Learning meetup group. David holds a PhD in Computer Sciences from the University of Wisconsin–Madison.

Recapping the Top 3 Talks on Futuristic Machine Learning at Scale By the Bay 2018

As discussed in our previous post, we recently had the opportunity to present some interesting challenges and proposed directions for data science and machine learning (ML) at the 2018 Scale By the Bay conference. While the excellent talks and panels at the conference were too numerous to cover here, I wanted to briefly summarize three […]

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Stateful traversal and econometric simulation: together at last!

Functional programming concepts such as the State monad and the Traversable type class can be powerful tools for thinking about common software engineering problems. As with any abstractions, these ideas may seem unfamiliar or opaque in the absence of concrete examples. The goal of this post is therefore to motivate and contextualize a “stateful traversal” design pattern by working through the specific […]

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Scala at Sumo: type class law for reliable abstractions

Abstraction is a fundamental concept in software development. Identifying and building abstractions well-suited to the problem at hand can make the difference between clear, maintainable code and a teetering, Jenga-like monolith duct-taped together by a grotesque ballet of tight coupling and special case handling. While a well-designed abstraction can shield us from detail, it can also […]

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Machine Data at Strata: “BigData++”

A few weeks ago I had the pleasure of hosting the machine data track of talks at Strata Santa Clara. Like “big data”, the phrase “machine data” is associated with multiple (sometimes conflicting) definitions, two prominent ones come from Curt Monash and Daniel Abadi. The focus of the machine data track is on data which is generated and/or collected automatically by […]

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Beyond LogReduce: Refinement and personalization

LogReduce is a powerful feature unique to the Sumo Logic offering. At the click of a single button, the user can apply the Summarize function to their previous search results, distilling hundreds of thousands of unstructured log messages into a discernible set of underlying patterns. While this capability represents a significant advance in log analysis, we haven’t stopped there. […]

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Scala at Sumo: type classes with a machine learning example

At Sumo Logic we use the Scala programming language, and we are always on the lookout for ways that we can leverage its features to write high-quality software. The type class pattern (an idea which comes from Haskell) provides a flexible mechanism for associating behaviors with types, and context bounds make this pattern particularly easy to express in Scala code.  Using this technique, we […]

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Scala at Sumo: grab bag of tricks

As mentioned previously on this blog, at Sumo Logic we primarily develop our backend software using the Scala programming language. In this post we present some (very) miscellaneous Scala tidbits and snippets that showcase some interesting features we have taken advantage of in our own code.

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“Sumo Logic brings everything together into one interface where we can quickly scan across 1,000 servers and gigabytes of logs and quickly identify problems. It’s awesome software and awesome support.”

Jon Dokuli,
VP of Engineering

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