The importance of real-time data and analytics, as well as its plethora of advantages, is clear—according to DBTA subscribers, real-time data and analytics ranked as their company’s #1 modernization priority. While capable of accelerating decision-making, increasing operational efficiency, and affording businesses with a competitive edge, data silos, data latency, and legacy infrastructures prevent real-time data and analytics from succeeding.
In DBTA’s special roundtable webinar, Unlocking the Power of Real-Time Data and Analytics, experts shared key solutions and best practices for effectively shifting data strategies to meet the latest modernization priorities.
Setting the scene for the motivation behind adopting real-time data and analytics, Colby Ing, senior product manager at MongoDB explained that “consumer expectations have evolved and continued to do so. They expect online experiences—like banking, shopping, and searching—all to be in real time with immediate feedback and processing.”
“This has put immense pressure on businesses to move from slower batch processing to real-time analytics, where decisions and interactions happen at the pace of consumer demand,” Ing continued.
Data must not only be captured and processed instantly but also immediately leveraged to drive decision making. A few second delay between insight and action can be the deciding factor of an enterprise’s ability to meet customer expectations—and maintain their relevance and profitability.
Ing presented MongoDB’s two answers to this real-time dilemma: Time Series—an optimized collection for storing and analyzing time-series data—and Atlas Streams—an enabler of real-time data streaming within MongoDB. Each of these innovations streamlines and enhances the way enterprises adopt real-time data and analytics—which is particularly relevant for preemptive maintenance and personalization and experience use cases.
Following Ing, Simon Prickett, senior product evangelist at CrateDB, examined today’s challenges standing in the way of real-time data and analytics. Ultimately, taking advantage of data is a complex, time consuming, and costly process, especially due to the expanded, diverse collection of tools and platforms that an enterprise uses.
CrateDB—the hyper-fast, open source database for real-time analytics and hybrid search—unifies data from an enterprise’s vast array of sources into a single location for analytics. Using standard SQL, CrateDB offers a representation of all types of data, powered by an “index everything” approach. This helps close the gap between where the data resides and when/where a decision needs to be made, lowering latency to drive real-time responsiveness.
According to Sharon Xie, head of product at Decodable, while stream processing is powerful, scalable, consistent, and flexible, it requires “a house of smart and hardworking data platform engineers. You can see this from a lot of companies like Apple, Netflix…all those companies [that] provide real-time use cases in their platform, they also have a very big data platform team.”
For many enterprises, this realization is a major roadblock for implementation; they simply cannot afford the costs of maintaining a large data engineering team for the sake of stream processing. Atop this, the necessary infrastructure shifts are too large, too complex to make the jump into real-time data and analytics feasible.
Xie explained that Decodable’s response to this phenomenon is threefold: raise the level of abstraction, remove unnecessary configuration, and make it a service.
Decodeable’s real-time data platform is a fully managed way for unifying the enterprise stack and eliminating infrastructure overhead, empowering businesses to build real-time pipelines as easily as possible. This is all done while delivering operational excellence, ensuring production-grade SLA and compliance is met.
Paige Roberts, head of technical evangelism at GridGain, echoed the previous speakers’ view of why enterprises are looking to implement real-time data and analytics.
“Right now, there is a lot of drive to accomplish more and more inside your business,” said Roberts, especially as it relates to maximizing operational efficiency, instantly responding to change, enhancing the customer experience, and staying ahead of fraud and compliance. Furthermore, the latest technological trends—such as AI, edge computing, and IoT—demands smart, fast decisions involving all relevant data.
The solution lies within a platform that addresses the variety of dimensions in which data introduces challenges, such as latency, workload complexity, multiple systems of records, scalability, and more.
Roberts introduced GridGain’s flagship technology, a unified data platform for processing all relevant data in low milliseconds at any scale. GridGain’s durable, distributed in-memory database is combined with event processing and colocated compute, enabling organizations to process streaming, transactional, and analytical workloads all in real time. With a simplified, non-intrusive architecture, GridGain can help enterprises achieve ultra-low latency response at any scale, processing for data both in-motion and at-rest, operationalize AI/ML workloads, reduce network data movement, and more.
For the full, in-depth webinar featuring use cases, detailed examinations, a Q&A, and more, you can view an archived version of the webinar here.