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Unlike other platforms, FinchDB offers scored and ranked results – analyzing all
possible answers and returning the best ones to a user, along with a confidence score for each. This allows a
user to perform fuzzy searches and to find exactly what they’re looking for -- regardless of whether they’ve
spelled it exactly right, or have the exact right information to build a query.
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FinchDB was built from the ground-up, completely in-memory and for inmemory uses. Other solutions have in-memory components, or are in-memory versions of older products. But
FinchDB was meant to be in-memory; so speed, flexibility, computing power, compression and decompression
were primary considerations in its design. And we developed proprietary new technologies in each area.
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FinchDB also affords users the ability to embed models in the query – so that
they change as the data changes. And, so users can perform analytics that are as dynamic as their data is.
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One of the most distinctive features of FinchDB is that analytical models are
embedded in each query – meaning that once data is prepped and once models are built, a user doesn’t have
to rebuild and reprocess their data. It can be queried and analyzed any number of ways for any number of
users – each time returning results that are based on the most accurate, most up-to-date picture of the data.
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Another proprietary feature of FinchDB is its ability to perform real-time, on-the-fly entity
linking. Its in-memory architecture coupled with its ability to process all relevant, contextual data around an
entity make it capable of instantly finding connections in the data and performing real-time knowledge
discovery and machine learning – meaning it gets smarter and better the more data it ingests.
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Not only does FinchDB combine search, database and analytics
technologies, it allows a user to perform analytics on specific transactions, at the exact moment of the
transaction, and to make immediate, impactful decisions based on the result. FinchDB offers true real-time
analytics. Like never before.
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An important feature of FinchDB is its patented compression approach. It’s what makes inmemory feasible at scale. FinchDB compresses a dataset to as little as 16% of its original size while preserving
the ability to – in fractions of a millisecond – decompress a single record or field. Making huge volumes of data
accessible instantly is a huge enabler of true, accurate, real-time analytics.
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For all of its massive computing power, FinchDB can run on commodity hardware –
meaning small commercial-grade processors. In fact, with the computing power of just a four-node cluster of
these processors, FinchDB can process roughly 1,800 complex queries per second.
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FinchDB can work alongside or in partnership with other databases or search
and analytics tools. Its dynamic modeling approach and small hardware and infrastructure footprint, along with
its ability to handle enterprise-size volumes of data make it massively scalable as well.