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Stream processing and event-driven microservices are complicated! They’re complicated because they combine the hardest problems from the database domain with the hardest problems from the distributed systems domain. Learn how 7+ years of deep R&D enables the Quine Streaming Graph to solve these challenges.
Detecting interesting data among a vast sea of otherwise non-interesting data is an important problem with many applications. thatDot is offering a unique new anomaly detection technology to calculate “novelty scores” in streaming data using our patent-pending technology, developed with funding from DARPA.
Quine is the open source streaming graph interpreter at the heart of the thatDot platform. You'll find user docs, Quickstart guides and easy-to-use recipes that address specific use cases at the Quine.io website.
For digital twins to be truly useful, they must be able to drive actions – for example, issue alerts or power down equipment – the instant an issue emerges, perhaps even beforehand.
According to McKinsey, “by 2025, smart workflows and seamless interactions among humans and machines will likely be as standard as the corporate balance sheet, and most employees will use data to optimize nearly every aspect of their work.”
Realistically, that’s only a couple of years away, meaning we have a short time to get this right.
In this special guest feature, Rob Malnati, COO of thatDot, digs into the debate between stream vs. batch processing, when it is best to use each approach, and the inevitable growth of stream processing.
The ability to act on data in real time when the consequences are most significant is driving the move to streaming data processing—for example, stopping an insider trade before it happens or recommending a product that pairs well with something in a shopping cart before someone checks out.
In practical terms, companies can’t afford to embrace real-time stream processing.
A comprehensive and accessible consideration of categorical data, how it differs from numerical data, and why it is useful.
The same thing that makes categorical data so powerful makes it challenging. While it is easy for you and me to tell the relative difference between a dog and a plane versus a dog and a cat, doing so computationally is not so straightforward.
To express the difference between two pieces of categorical data, one must use graph-based analytical tools or have a background in graph theory.
thatDot's own Rob Malnati is featured in eWeek on the challenges current graph databases face scaling to real time and how streaming databases will solve the problems.
Enterprises want to act upon it immediately in event-processing pipelines because when certain events are not caught quickly, as they happen, the opportunity to act disappears. For example, security incidents, transaction processing (such as fraud or credit validations), and automated machine-to-machine actions.
Streaming graph analytics: ThatDot’s open-source framework Quine is gaining interest and is potentially a game-changer, according to George Anadiotis.
What do you get when you combine two of the most up-and-coming paradigms in data processing — streaming and graphs? Likely a potential game-changer, at least that’s what is being hinted at by the likes of DARPA and now CrowdStrike’s Falcon Fund, which are betting on ThatDot and its open-source framework Quine.
Datanami's managing editor Alex Woodie delivers a detailed yet clear overview of Quine streaming graph, why we built it, and what it does.
The previously separate worlds of graph databases and streaming data are coming together in an open source project called Quine... capable of returning Cypher queries on flowing data at the rate of a million events per second, which he says could eliminate the need to build and maintain elaborate data pipelines.
InfoQ's Sergio de Simone delivers a perceptive overview of Quine's architecture and importance, written from a developer's POV.
Developed at thatDot, Quine is an open source streaming graph solution aimed at high-volume event processing. Quine combines graph data and streaming technologies to enable the creation of real-time, complex event processing workflows at scale, says thatDot.
TechCrunch's Frederic Lardinois does a great job capturing the reason why there is urgency in the market for streaming graph.
Most current solutions, he argues, would be able to maybe handle a few thousand events per second, but the kind of customers thatDot is talking to would need a solution that can handle 250,000 events per second and Wright is confident that Quine can handle that — and a lot more.
VentureBeat wrote a great summary of Quine on the day of our announcement.
The offering comes as the answer to event processing frameworks such as Flink. Ryan Wright, the cofounder of ThatDot, notes that these previous generation solutions come with various limitations and spend enormous time — on the scale of months.
