Senior Technologist,
Imply (

2023 is gearing up to be a huge year for streaming technology as the popularity of data streams continues to increase. Streaming data is no longer niche, with the vast majority (80 percent) of Fortune 100 companies now leveraging the most common streaming platform, Apache Kafka, according to the Apache Software Foundation. All of the key cloud providers, including Amazon, Google, and Microsoft, have streaming services on board.

This transformation makes sense in context. Consumers have raised the stakes on the speed, reliability, and scale that they expect from event delivery for both internal and external apps. Without the ability to provide customers with real-time data processing and dissemination in sub-second updates, organizations will lose out to competitors who can deliver it better and faster.

As streaming has become the go-to norm, companies are also changing the way they analyze data from streams, and the timeline has sped up the necessity to review events at their point of creation, resulting in real-time insights. All it takes is the proper tools, and you can immediately compare current actions with past ones, head off problems at the pass, and improve your decision-making on the fly.

Bottom line: As data streams have multiplied, it has heralded the emergence of fresh use cases and rules for real-time analytics. Data teams that want to maximize streaming’s potential in 2023 must take a new approach to the architecture of analytics. It’s no longer enough to rely on the traditional method of the batch-oriented stack—a streaming-native approach will serve you much better.

Shift Happened

To understand the new world in which we now find ourselves, it’s important to understand where we’ve come from. As the centerpiece of business operations, data has always been there, but not in quite the same way as it is today. The traditional role for business data was batch-dominant, with data infrastructure striving to pinpoint data at one point in time, storing it to use later. But as daily batch operations on mainframes evolved to the Internet-driven world, fixed data at rest began being replaced with fast-moving data in motion to reflect the always-on environment, with data flowing between applications and data systems, and within organizations as well as between them.

Is there still data at rest? Yes, it’s still a thing, and batch systems haven’t become completely archaic for certain types of reporting purposes. But this approach has become old school, as reality is neither fixed nor static. That’s why in light of this data transformation, the systems that data teams build must be expressly created for data in motion. This is the only way to satisfy consumer demands for data experiences that are both authentic and seamless.

The result of these changes has been the proliferation of streaming technology, which has in turn necessitated a new mindset for thinking about data. More streaming means that the platforms that stream the data must function more and more like the central nervous system of a company. To serve their purpose, these platforms must link all of the functions and also must drive mission-critical company operations. This is why more evolving technologies are now purpose-built to support stream processors and event databases, which are data-in-motion systems.

One real-time analytics database in the purpose-built category is Apache Druid. Druid makes it possible for users to query events right when an event enters the data stream. Druid also can enable this functionality at an enormous scale, along with sub-second queries on both batch and stream data.

The Future Is Now

Countless businesses now combine Druid’s use with streaming processors such as Amazon Kinesis and Kafka. The result is systems that make upwards of petabytes of streaming data instantly and easily accessed.

When it comes to data intelligence, this is clearly the next evolution. It’s all about the ability to react to events just as they happen. But we’re nowhere near the final destination on the journey of streaming adoption—we’re just at the start. We can see how rapidly we’re moving toward the time where streaming technology becomes the lynchpin of every company’s data architecture.

As we continue this clear and steady trajectory toward a completely revamped way of understanding data and its movement, analysis, and sharing, one thing is clear: Streaming technology has burst open the floodgates for a vast array of emerging use cases and products. This is why companies that adopt a streaming-native approach to analytics will come out ahead—this year and miles into the future.