Turn Data Into Insights Faster With Grid Dynamics Analytical Data Platform Accelerator on AWS Cloud
Oct 20, 2020 • 6 min read
Oct 20, 2020 • 6 min read
The term “In-Stream Processing” means that a) the data is coming into the processing engine as a continuous “stream” of events produced by some outside system or systems, and b) the processing engine works so fast that all decisions are made without stopping the data stream and storing the information first.
You can think of an In-Stream Processing engine as a “cyber plant” for event processing. Imagine that events are coming in at high speeds to the front docks of the cyber plant where they are captured, sorted into queues, and sent on to the assembly lines for processing. Inside, on the cyber conveyor, specialized software robots perform analytical computations and transformations that filter, match, count, aggregate, and reason about the events as they are passed down the line. Whenever something interesting is discovered or computed — such as unmasking a fraud or computing a dynamic price — the notification is sent immediately to an external business system to do something about it. At the end of the conveyor, processed events are shipped to a warehouse where other systems can access them for other forms of processing.
Since many applications of In-Stream Processing are analytical in nature, some call these systems In-Stream Analytics. Alternatively, people sometimes drop the prefix “in-” and simply call it Stream Analytics or Stream Processing. Finally, it is worth mentioning that In-Stream Processing technologies fall into a wide class of approaches for dealing with large volumes of events, called Complex Event Processing, or CEP. Because In-Stream Processing is fast and aims to analyze data nearly instantaneously, it is sometimes described as Fast Data, a term that’s growing in use and popularity.
In-Stream Processing is only one rather specialized type of processing in a broader landscape of technologies, processes, tools and applications that are commonly referred to as Big Data.
In-Stream Processing typically happens on the front end of data acquisition, and serves a dual purpose of:
In-Stream Processing cannot exist by itself; it is integrated with the rest of the Big Data infrastructure to deliver real-time processing capabilities and can be added to an existing Big Data infrastructure as a new Big Data Service.
Typical In-Stream Processing happily handles workloads such as these:
For fewer than 1,000 events per second, In-Stream Processing might be overkill; modern microservice architecture can do the job. For a sustained rate of more than 100,000 events per second, In-Stream Processing will more than likely still work, but will require a customized design to accommodate specific requirements and infrastructure choices.
For applications with latency requirements under 2 seconds, In-Stream Processing will not be a viable option because the data is handed off too many times between the source system, the In-Stream Processing engine, and the application that actually acts on the insight.
For applications that can wait 60 minutes or more, batch analytics systems like Hadoop probably offer a cheaper, simpler, and more powerful solution than In-Stream Processing
In-Stream Processing is rapidly gaining popularity and finding applications in various business domains. In future posts we’ll describe the anatomy of specific use cases in detail to illustrate how In-Stream Processing works. For now, here is a short list of well-known, proven applications of In-Stream Processing:
While business domains are quite diverse, their usage patterns are actually very similar and come down to:
1. feeding events to the stream processing engine
2. implementing processing logic; and
3. delivering results to appropriate output systems that will act on the data insights developed by the processing engine
As In-Stream Processing technology matures and more organizations invest in digital transformation, new applications of stream analytics are being identified and implemented across a wide spectrum of industries.
Sergey Tryuber, Anton Ovchinnikov, Victoria Livschitz