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The unrivaled data processing speed of Ultipa Server has allowed the delivery of real-time business intelligence (RTBI) or insights (or even foresights) about business operations as they occur. Traditionally, such intelligence has to wait days or weeks before generated, with Ultipa’s real-time, super-deep traversing and smart graph computing capability, real-time actions and reactions can be taken to address your business needs online. Typical RTBI scenarios including but not limited to: fraud detection, customer relationship management, supply-chain finance, data security monitoring, yield management, etc.
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Pain PointsSolutions
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Either AP or TP
Today’s OLAP systems are NOT designed for OLTP operations, vice versa. Having OLTP+OLAP in one unified system is to bring together the best of both worlds, and it’s what a genuine RTBI desperately needs to thrive, because TP addresses the real-time-ness, and AP addresses the deep analytics side of the needs.
High Concurrency
Ultipa Graph is designed to cater to HTAP scenarios, namely OLTP + OLAP, powered by our highly-parallel computing engine, which maximizes data throughput that are orders of magnitude faster than other BI big-data frameworks.
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Sluggishness
Online fraud detection must be real-time, every extra second taken to respond means degraded user experience and friction for trusted users. Unfortunately, traditional NoSQL or RDBMS are not capable of instant decision making on large amount of varied data.
Low Latency
Ultipa Graph handles graph queries, sometimes complex ones, in microseconds instead of milliseconds, that’s 1,000 times speed-up. If time is equal to $, you will save tons of bucks with Ultipa.
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Shallowness
Most of today’s BI systems were not designed for deep data analytics, fundamentally, they (RDBMS or most NoSQL or big-data frameworks) can handle data that are only 1-hop away – meaning very shallow intelligence – in human terms. Real BI requires ability to handle data that are multi-hop away.
High Scalability
Ultipa Graph is highly scalable and designed to handle large amount of data online and in real-time, we often store 10s of times more data with a smaller cluster than traditional BI solutions.