Stream Processing: Who, How, and Why

What is Stream Processing? 

Stream processing is the processing of event data in real time or near real time. The goal is to continuously intake inputs from data (or “event”) streams and immediately process or transform the data into output streams. The input is typically one or more event streams of data in motion, containing information such as social media activity, IoT sensor data from physical assets (e.g., self-driving cars), insurance claims, customer orders, bank deposits/withdrawals, and even emails.

What Does Stream Processing do?

Stream processing is essentially processing data as it is made available.

Batch processing requires certain time boundaries, based on which it saves the corresponding range of data as a file and then sends it to a database or an even larger form of mass data storage such as a data lake.

To pull value from this data, engineers must select the data that they want, clean and prepare it, and run applications to query the data. Batch processing requires events to be collected and stored before queries can be run.

One of the biggest disadvantages of batch processing is the speed at which data is processed. With batch processing, the data is converted from continuous streams of events to events between certain points in time. Thus, an engineer must process data corresponding to a specific range of time, unlike stream processing, in which data is continuously processed.

For example, let’s look at streams of Twitter data. Every tweet is also related to a timestamp, retweets, retweet times, likes, impressions, etc. On average, 350,000 tweets are sent per minute. Now, let’s say an engineer wants to work with and analyze this data but can only do so using batches of the data. At some point, the engineer will need to determine a set of batch boundaries, query the included data, and then repeat this process. After several repetitions, the engineer will then have to consolidate the data to gain an overall picture of it.

In stream processing, tweet data can be processed as it becomes available. Since all data is processed in this way, it can be viewed cohesively and in its current format at any time. With stream processing, mundane engineering tasks like traditional ETL processes for integrating data can be relieved. Stream processing can be used to filter and enrich data as it comes in. In the Twitter data example, one or more conditions such as specific numbers of retweets, likes, and impressions can be applied to Twitter event stream data, providing only meaningful data from tweets meeting the specified conditions for analysis.

The biggest advantage of stream processing is that it provides real-time or near-real-time data insights. If you work with data, you know that the most important aspect of data is the value (or insights) that it provides. Although batch processing can happen quickly, it still provides snapshots of points in time—to view insights from the most recent data, you might have to wait for the next batch run. In stream processing, the most recent data is reflected almost immediately.

A Stream Processing Infrastructure for Data Integration

The primary function of any stream processing infrastructure is to ensure data flows from input to output efficiently in real-time or near real-time. According to Gartner’s Market Guide for Event Stream Processing, the focus of a stream data integration event stream processor such as Nexla, is “ingestion and processing of data sources targeting real-time extraction, transformation and loading (ETL) and data integration use cases. The products filter and enrich the data, and optionally calculate time-windowed aggregations before storing the results in a database, file system or some other store such as an event broker. Analytics, dashboards and alerts are a secondary concern.”

Things to look for in a strong stream processing infrastructure for data integration:

• Scalable: Stream processing infrastructure should scale horizontally with the increase in amounts of data flowing through the system
• Repeatable: The same process can be applied to many different event streams while maintaining the same level of efficiency
• Stream Transformations: Conditions can be applied to queries to filter out unwanted data in motion, meaning trends can be detected as the data is flowing
• Monitor/notifications: Any time event data is interrupted or any discrepancy arises, you should be notified immediately to fix the problem rather than waiting days to find out
• Timely: Applying and analyzing the data can happen instantly for anyone who needs it. Since the data is up-to-date in real-time, analytics can always rely on fresh data for the most accurate results

Who is Stream Processing for? 

Modern enterprises across industries are beginning to adopt stream processing as a way to accelerate the time-to-value of data. Stream processing is an ideal fit for anyone who is working with any type of real-time or near real-time data, including data about customer orders, insurance claims, bank deposits/withdrawals, tweets, Facebook postings, emails, financial or other markets, or sensor data from physical assets such as vehicles, mobile devices or machines across sectors like retail, healthcare, finance, or tech.

Stream processing also supports data engineering teams that have limited resources. As of 2021, 58% of companies are using real-time streaming, and more than half say at least 50% of their data is currently processed via real-time streaming. Maintaining a strong stream processing infrastructure is a critical part of the foundation of a modern unified data solution and necessary to extract the most value from data.

Recently recognized in the Data Integration Tools Magic Quadrant by Gartner, Nexla provides event stream data integration solutions for real-time streaming data. The Nexla data operations platform has an intuitive interface that allows analysts and business users to easily build data flows they need without requiring engineering expertise. Nexla provides self-service tools for data mapping, validation, error isolation, and data enrichment, making collaborative workflows available for real-time data.

If you’re ready to see how real-time streaming and a unified data solution can streamline your processes, get a demo or book your free data mesh consultation today and learn how much more your data can do when everyone can use it securely. For more on data, check out the other articles on Nexla’s blog.