Announcing Trifacta

The big news around here today is the public announcement of Trifacta, a company I’ve been quietly cooking over the last few months with colleagues Jeff Heer and Sean Kandel of Stanford. Trifacta is taking on an important and satisfying challenge: to build a new generation of user-centric data management software that is beautiful, powerful, and eminently useful.

Before I talk more about the background let me say this: We Are Hiring. We’re looking for people with passion and talent in Interaction Design, Data Visualization, Databases, Distributed Systems, Languages, and Machine Learning. We’re looking for folks who want to reach across specialties, and work together to build integrated, rich, and deeply satisfying software. We’ve got top-shelf funding and a sun-soaked office in the heart of SOMA in San Francisco, and we’re building a company with clear, tangible value. It’s early days and the fun is ahead. If you ever considered joining a data startup, this is the one. Get in touch.

Metablogging MADlib opening @ SIGMOD

When the folks at ACM SIGMOD asked me to be a guest blogger this month, I figured I should highlight the most community-facing work I’m involved with.  So I wrote up a discussion of MADlib, and that the fact that this open-source in-database analytics library is now open to community contributions. (A bunch of us recently wrote a paper on the design and use of MADlib, which made my writing job a bit easier.) I’m optimistic about MADlib closing a gap between algorithm researchers and working data scientists, using familiar SQL as a vector for adoption on both fronts.

MADlib goes beta! Serious in-database analytics

MADlib is an open-source statistical analytics package for SQL that I kicked off last year with friends at EMC-Greenplum. Last Friday we saw it graduate from alpha, to the first beta release version, 0.20beta. Hats off the MADlib team!

Forget your previous associations with low-tech SQL analytics, including so-called “business intelligence”, “olap”, “data cubes” and the like. This is the real deal: statistical and machine learning methods running at scale within the database, massively parallel, close to the data. Much of the code is written in SQL (a language that doesn’t get enough credit as a basis for parallel statistics), with key extensions in C/C++ for performance, and the occasional Python glue code. The suite of methods in the beta includes:

• standard statistical methods like multi-variate linear and logistic regressions,
• supervised learning methods including support-vector machines, naive Bayes, and decision trees
• unsupervised methods including k-means clustering, association rules and Latent Dirichlet Allocation
• descriptive statistics and data profiling, including one-pass Flajolet-Martin and CountMin sketch methods (my personal contributions to the library) to compute distinct counts, range-counts, quantiles, various types of histograms, and frequent-value identification
• statistical support routines including an efficient sparse vector library and array operations, and conjugate gradiant optimization.

More methods are planned for future releases.  Myself, I’m working with Daisy Wang on merging her SQL-based Conditional Random Fields and Bayesian inference implementations into the library for an upcoming release, to support sophisticated text processing.