Stream processing acts on the constant flow of real-time data allowing businesses to shift their operations toward real-time responsiveness.
The world beyond batch processing is promising, opening up countless possibilities for the ambitious and visionary. When else have people across organizations been able to establish systems that instantly respond to customer interactions, market fluctuations, IoT data in the field, and constantly updating regulatory environments at scale? Enter stream processing.
With stream processing taking action on the constant flow of real-time data, enterprise architects and senior technical leadership can shift their strategies and operations toward real-time responsiveness to deliver better customer experiences, handle disruptive fluctuations in demand, and even save lives. These new opportunities are all the more important with ongoing economic uncertainty and a tough labor market that restricts even the most generous of organizations from hiring the data analysts and tech-savvy knowledge workers they need to perform analysis and make data-driven decisions.
Must-have qualities of your stream processing platform
Even when architects understand the value of real-time responsiveness and recognize areas where they can gain competitiveness through better use of their data, they often hesitate to invest because they don’t know what qualities they should prioritize in a stream processor. Here are things your platform must do:
Horizontally scale with minimal latency: The sheer number of events processed per second indicates throughput, but look into the platform’s latency percentile figures, which show how often a request has worse latency and by how much. For example, 99.99% latency at 30 milliseconds means that, on average, 9,999 requests out of 10,000 are delayed by 30 milliseconds or less.
Make use of a minimal number of moving parts: IT teams are never eager to increase their maintenance burden, which means the ideal stream processing platform consolidates its features into as few clusters as possible. Simple architectures let teams focus on deploying new real-time responsiveness applications over troubleshooting issues.
Run wherever existing infrastructure already resides: Native images, compatible with the enterprise cloud-container environment already in use, are a must-have to unlock the potential of stream processing without having to migrate providers or deploy and manage complicated multi-cloud setups.
Rely on open-source foundations: Stream processing platforms that support open standards and vendor-agnostic containers are far more flexible and extensible and can run with best-in-breed technologies like Kubernetes.
Have resilience built into every transaction: Look for exactly-once processing guarantees, which ensure that even after a failure, the stream processing platform can restore from resilient backups without any data loss and replay streams and processors as though they were only run once. Beyond this, stream processing platforms should support multi-datacenter deployments for disaster recovery and high availability.
See also: Top Challenges of Using Real-Time Data
The case for stream processing
Making that shift from using batch processing methods to real-time responsiveness might seem implausible, but there’s good news for those already using real-time analytics. If they already have a Kafka storage and a data pipeline, or even an ESB that channels all event data, they’re already starting very much on the right foot to layer in real-time responsiveness. Stream processors work in synchronization with existing architectures to externalize mission-critical data, enhance it with context and AI, and deliver more robust results to the applications and people that can take action right away.
Real-time no longer means collecting and storing real-time data for batch analysis at the end of the day, week, month, or quarter. The new responsiveness paradigm has arrived for those willing to envision the possibilities of using data now—and not a millisecond later.