Demystifying Real-time Data Analytics: Understanding Definitions, Categories, and Strategies for Unlocking Value in the Data-driven Era
Is an analytical response within 300 milliseconds on data generated yesterday considered realtime? In today’s fast-paced digital landscape the concept of realtime data analysis has become increasingly prevalent and essential to business success. Yet, there’s a lot of confusion about what “realtime” really means.
Understanding the definitions when discussing real-time data analysis is crucial to unlocking the potential of realtime analytics to propel business growth in this data-driven era.
One refinement I propose is the need to differentiate between end-to-end realtime data analysis and fast response from already prepared data. Response latency refers to the time it takes for a system to process a request or query and respond. End-to-end realtime data analysis refers to the time between the generation of new data, the time it takes to transport it, transform it, and enhance it to prepare it for analysis, plus the time for the analysis itself.
Low Latency Realtime Data Analysis
The first category of definitions are related to response latency:
1. Sub-Second Response: Realtime often refers to responding anywhere from a few hundred milliseconds, common in a good analytical database, to a few microseconds, or nanoseconds, only attainable in a few highly specialized technologies. Applications like cybersecurity or stock exchange bidding systems necessitate this exacting category of near instantaneous response. Fraud detection would generally work fine with a response measured in milliseconds.
2. Interactive Response: This is from an analytics user’s perspective. Systems that respond to queries or actions such as clicking to drill down for more detailed information on an analytic graph are realtime. While a few seconds of latency might be acceptable at times, exceeding this threshold can result in user frustration or lost opportunities.
End-to-end Realtime Data Analysis:
The second category of definitions include processing the data from the source, not just getting a response from already prepared data:
1. Streaming: As opposed to “batch” where data accumulates, then is processed all at once, streaming involves processing and analyzing data as it flows in continuously, usually one unit at a time. Often, “micro-batches” process data from a small time window such as a few seconds or minutes. Many popular streaming data processing technologies actually process in micro-batches, so this is still considered streaming. Monitoring, or acting on data from sensors or other Internet of Things (IoT) devices is a common use case. Predictive maintenance or network optimization are good examples. Sentiment analysis on social media streams is another.
2. Event-Driven: This revolves around triggers or actions that initiate data analysis and response. Rather than adhering to scheduled intervals of time, responding promptly to specific events is the goal. Examples include change data capture which pulls changes to source databases as they happen. Another is loading, processing, and analyzing data as soon as it arrives from a third party. Performance expectations in event-driven scenarios rely on timely completion of processing before subsequent events occur so that the system is ready to process the new data.
How Can Organizations Unlock Real Value From Realtime Analytics?
The ability to swiftly process incoming data and deliver insights in a timely manner enables businesses to seize opportunities, detect anomalies, and drive proactive decision-making. To harness the real value of realtime data analysis, organizations must establish a strong foundation.
1. Realtime definition clarity: Consider the requirements of your use cases, whether you need sub-second or human interactive latency, and whether you need streaming or event-driven processing to get the data ready in a short time window. It’s not uncommon to need one strategy from each category to prepare data rapidly for analysis and analyze it at the speed the use case demands.
2. Infrastructure readiness: Invest in a robust infrastructure that supports your chosen definition of realtime processing. This includes selecting the right technologies such as streaming data platforms, analytical databases, and hardware or cloud instances.
3. Performance optimization: Fine tune your analytical systems to meet both end-to-end processing, and latency requirements. Any good data processing or analysis technology should give you extensive options for monitoring, locating, and refining any workloads that aren’t meeting latency needs. Throwing more hardware at the problem is not an ideal solution, and in the end, will increase both costs and energy burned in a world that needs energy conservation.
4. Pipeline speed focus: Fast response on stale data is no longer acceptable in businesses and use cases with modern realtime requirements. Instead of slow, batch data transformation in a staging area, best practices are moving toward automated loading of data from source systems directly into analytical databases. Organizations are increasingly turning to in-database data processing with SQL and already highly optimized database engines to reduce end-to-end realtime analytics response time.
Today’s organizations are sitting on massive amounts of data. But in the absence of a proper analytics foundation, much of this valuable data stays unusable. One obvious piece to a robust, realtime analytics foundation is having complete understanding of what customers expect when it comes to realtime. The other critical piece is having a platform that can reduce the time taken for data to be made ready for analysis as well as fast execution of the analysis itself.
Embracing realtime data analysis will empower organizations to respond swiftly, make informed decisions, and deliver exceptional experiences in an increasingly dynamic and interconnected world.
About the Author
Paige Roberts is Open Source Relations Manager for Vertica by OpenText. With 25 years of experience in data management and analytics, she has worked as an engineer, trainer, support technician, technical writer, marketer, product manager, and consultant. She’s contributed to “97 Things Every Data Engineer Should Know,” and co-authored “Accelerate Machine Learning with a Unified Analytics Architecture” both from O’Reilly publishing.
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