An Introduction to Unified Real-Time Data Platforms

First, there were DBMSs and data warehouses. Then came data lakes and event stream processing platforms. Now, the most advanced data solutions are Unified Real-Time Data Platforms. But what are they?

Unified Real-Time Data Platforms simplify and optimize data architectures by combining transactional, stream, and analytical processing across data silos into a single “unified” platform. These platforms deliver ultra-low latencies with horizontal scalability, strong security, and disk-based durability, and do so across disparate, diverse, and distributed data sources.

Key Components of a Unified Real-Time Data Platform

Unified Real-Time Data Platforms

According to Gartner, “Unified platforms combine many or all the features of an Event Stream Processing (ESP) platform with a DBMS or in-memory data grid and a programmable application engine. Unified platforms are a relatively new kind of infrastructure software that supports operational or analytical business applications that need to process both streaming data in motion and historical data at rest.” 1

In other words, a Unified Real-Time Data Platform seamlessly processes data in-motion and data at- rest, as well as provides compute functionality, to enable enterprises to handle complex analytical, streaming, and transactional data workloads at ultra-low latencies. Uses of this technology may include anything from outlier detection – as in real-time, synchronous fraud detection use cases – to situational decision-making – as in the training and execution of machine learning models to enforce quality control standards.

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Unified Real-Time Data Platforms vs. Similar Data Technologies

So how does a Unified Real-Time Data Platform compare to other similar data technologies?

Let’s start with databases and data stores. Traditional DBMSs, data lakes, multi-model DBMSs, and in-memory databases are all simply data stores combined with a means of interacting with the stored data. By definition, they are granular data silos creating a boxed view of the data.

An in-memory data grid (IMDG) is a very efficient means of enabling data processing in-memory while providing horizontal scalability and parallel processing capabilities. It is a very powerful performance enhancer for executing large amounts of data processing events in parallel. While an IMDG wouldn’t necessarily work well as a low-latency data hub or a data store, it is something that could be used as an underlying architecture of a Unified Real-Time Data Platform to make it a more performant and scalable solution.

Event stream processing (ESP) platforms stream events through data pipelines from point A to point B. They perform calculations on the streaming data continuously as it is created, enabling immediate awareness and action around situations. However, ESP platforms do not store data and therefore are not able to apply or provide contextual or historical information for true real-time processing of events. Instead, ESP platforms rely on data enrichment (with data housed in data stores) and time windowing to analyze the event streams, all of which add latencies of several minutes or more.

A Unified Real-Time Data Platform is a comprehensive and flexible data processing and analytics technology with the ability to remove many of the constraints that these other technologies introduce by analyzing and acting on both streaming and stored data as part of processing complex workloads, all at ultra-low latencies.

To learn more, download the eBook, The Ultimate Guide to Unified Real-Time Data Platforms.

1 Gartner, Market Guide for Event Stream Processing, W. Roy Schulte, et. al., 15 May 2023

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