Pilosa was initially built to power lightning fast segmentation queries from a large and disparate data set. Along the way we discovered that it is also especially helpful with data science.
Retail transactions produce huge volumes of data, and only fast queries can pull value out this mess of data. We used Pilosa to explore the well-known Star Schema Benchmark, producing impressive performance numbers in the process.
Chemical similarity is essential to pharmaceutical development. Running Tanimoto algorithms over Pilosa clusters allows researchers to conduct exhaustive searches of existing structures to identify target chemicals, accelerating drug development.
Transportation systems are vital to economic networks but produce massive amounts of data that are difficult to analyze. Using New York City taxi data, we harnessed Pilosa's ability to work across datastores while supporting granular attributes.
Umbel is where Pilosa’s journey began. See how the Umbel platform uses Pilosa to create highly-specific customer segments, allowing clients to personalize their messaging and increase revenue with data-driven, targeted campaigns.
Modern network attacks require an increasingly complex infrastructure of intrusion prevention, creating datastores which grow exponentially. Layering Pilosa atop existing security solutions allows us to analyze high-volume network data and even predict network intrusions.