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Continuous Analysis on Really Big Data

Pilosa is an open source, distributed bitmap index that dramatically accelerates continuous analysis across multiple, massive data sets.

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No More Compromises.

Data volume is growing exponentially, but productivity gains are still lagging behind. It's time to stop making compromises that are slowing you down – gone are the days of cumbersome pre-computation, batching, caching, and sampling.

Enterprises need to be able to understand and act on data instantly, no matter how complex, fragmented, or large it is.

Pilosa collapses the speed and batch layer by abstracting the index from data storage, optimizing it for massive scale, and making your data instantly queryable and continuously analyzable.

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Real-Time Accessibility

Pilosa makes your data instantly accessible without the addition of expensive, exotic hardware. No longer is your data locked in a data lake or fragmented across multiple data sources.

Continuous Monitoring

The Pilosa solution remains up-to-date and highly responsive at all times; allowing you to monitor continuous streams of data without placing undue burdens on your existing data store.

Unlimited Federation

Pilosa eliminates the need for joins by combining data from multiple distributed data stores into a single index, regardless of size or location.

Artificial Intelligence

Pilosa allows for algorithmic plugins, thereby paving the way for artificial intelligence and machine learning algorithms.

High Cardinality

Pilosa is built for massive volume. It indexes up to 2^64 objects and is practically unbounded in the number of attributes.

Reduce Costs

Pilosa can easily integrate into existing architectures and is simple to maintain. With its minimal footprint in memory, the cost-per-query and cost-per-index is substantially lower than other solutions.

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Our story

Pilosa was founded in 2017 when the engineering team at Umbel, a data management platform, was challenged with creating a scalable way to run ad hoc queries to group and sort massive volumes of data in real-time. After three years of internal testing, we moved to open source with a commitment to unlock the power of data science for everyone.

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