Hub API

HUB

Enterprise-grade performance for data matching at scale. Available in API, RESTful Web Service, or Apache Spark integrations.
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Matching Framework

Powered by matchit®
The foundation of our matching software, matchit has been designed specifically to deliver matching results that mirror human-like perception, at scale, without preprocessing.

Artificial Intelligence, a proprietary algorithm, lexicons, and a contextual scoring engine work together to defeat the errors, inconsistencies, and challenges commonly found in contact and business data.

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Product Overview

Unprecedented Speed
and Accuracy.
Hub caters to use cases where matchit needs to be integrated into back-end data flows, applications, and data quality processes. This in-memory API is ideally suited to handle batch, real-time, and big data requirements.
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HUB API

Enterprise-grade performance.
This high-performance API processes data in-memory, making it many times faster than solutions that rely on traditional disk IO. Hub is also database-agnostic, able to process data from virtually any source.
API
HUB Web Service

HUB Web Service

Real-time Capabilities.
Using the RESTful Web Service, Hub Service can hold an already normalized and tokenized master table completely in-memory, allowing it to access, retrieve, and output answers instantly.

Hub delivers accurate results in real-time even when comparisons are made against massive and complex datasets. For a single transactional record, comparisons against 750 million records happen in only a fraction of a second.

HUB Spark

Big Data.
Rising data volumes present a challenge for anyone working with data. Enterprises today commonly manage multiple data warehouses, all housing billions of records. And yet quality must be maintained while time-to-answers can always be faster.

To meet this challenge head-on, we used Hub and Apache Spark to blend in-memory with distributed processing. The result is a matching engine that can process billions of records in minutes, not days.

Matching Technology

Product Overview

Future-proof Match Technology
While classic database systems like SQL, DB2, and Oracle are still wildly popular, the database market is seeing more and more entries, many of which are gaining significant adoption.

Trying to future-proof your matching technology for the database of tomorrow isn’t possible. We built Hub so you don’t have to.

Hub is database-agnostic because it doesn’t require a database at all. Using in-memory data structures to pass data from any source to any destination, Hub eliminates the need for expensive third-party databases and opens the door to a much wider range of use cases and integration possibilities.

Performance

In-memory processing wasn’t just selected for the obvious benefits inherent to a database-agnostic matching solution; it also has huge performance advantages. Able to process large volumes of data many times faster than solutions that rely on traditional disk storage, Hub performs deduplication, matching, and lookup in real-time with ease.
API

DEDUPE

49m 10s

49 million records

MATCH

05m 03s

1 million : 30 million records

Using a 10-Core hyper-threaded Windows PC with 64GB of RAM

Service

LOOKUP

00s 14ms

1 : 98 million records

MATCH

00m 15s

100,000 : 50 million records

Using a 10-Core hyper-threaded Windows PC with 64GB of RAM

Spark

DEDUPE

20m 15s

1 billion records

MATCH

TBD

N/A

Using a 20-machine cluster on AWS, each with 48 Cores & 192GB RAM

Inaccurate Data Can Cost You Millions. We’re Here To Help.

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