By Ed Anuff, Chief Product Officer, DataStax\n\n\n\nEnterprises across industries have been obsessed with real-time analytics for some time. The technology that powers this toolset that aims to make critical business decisions quickly is expected to amount to a $50.1 billion market by 2026.\n\nIt\u2019s no surprise. The insights provided by analytics \u201cin the moment\u201d can uncover valuable information in customer interactions and alert users or trigger responses as events happen. And these real-time responses are a critical part of building the kind of experiences your customers expect.\n\nBut this glittering prize might cause some organizations to overlook something significantly more important: constructing the kind of event-driven data architecture that supports robust real-time analytics.\n\n Learn more about DataStax Astra Streaming,\n\n which is now generally available \n\nAn enterprise that focuses on building an event-based architecture for real-time applications will be in a much better position to build a real-time analytics platform. Why? Because when your application architecture is closely mapped to your business activities (so-called \u201cevents\u201d), you produce the kind of real-time data you need to run real-time analytics in a more flexible and scalable way than traditional software architectures.\n\nLet\u2019s take a closer look at what real-time events mean in a digital business, and how building an open architecture to make the most of the data these events generate can create a better customer experiences and drive revenue.\n\nAll interactions are digital interactions\n\nIt\u2019s helpful to begin by thinking about what an event is. In a business context, this is defined as an interaction. Interactions with customers, partners, suppliers \u2013 your entire value chain \u2013 are what drive business. \n\nFor a digitally transformed business, all of the interactions are digitally mediated. This is true even when an interaction happens offline, in the physical world. Think about a courier company delivering a package, or an airliner touching down 30 minutes behind schedule: these are digitally mediated offline activities.\n\nThese interactions are represented, in a technological sense, as \u201cevents,\u201d with a certain amount of importance attributed to when they happen. We can, in the semantics of the software world, refer to digitally mediated business activities asreal-time events.\n\nHow do businesses manage and take advantage of real-time events? With an event-driven architecture: a software programming approach built around the capture, communication, processing, and persistence of these events \u2013 mouse clicks, sensor outputs, and the like. All in real-time, of course.\n\nProcessing streams of data in the moment involves taking actions on a series of data originating from a system that continuously creates events. When an airliner lands behind schedule, a wide range of real-time data could trigger actions: gate availability, fuel truck location, missed connections.\n\nThe ability to query a non-stop data stream and recognize that something important has happened or find anomalies, and act on them quickly and in a meaningful way (like booking a new flight for a passenger that\u2019s missed their connection), requires a specific technology stack.\n\n \n\nThe foundation of an event-driven architecture\n\nMany organizations understand the importance of event-driven architectures. Pretty much every aspect of our technological lives has been affected by the move toward event-driven, real-time data processing \u2013 the way we communicate, the way we work, the way we order food. The way businesses are run has evolved too: the availability of real-time inventory, sales, and demand data is driving real-time optimization of supply chains across industries.\n\nReturning to the package delivery company example, every interaction \u2013 a driver scanning a package, a user looking at a mobile app, a lost package \u2013 is an operational event that a software engineer needs to think about.\n\nIt\u2019s no surprise that the event-based paradigm has had a big impact on what today\u2019s software architectures look like. Organizations need a stack of technologies that make real-time data \u2013 whether it\u2019s \u201cin motion\u201d and streaming from IoT devices or within an enterprise data ecosystem, or \u201cat rest\u201d and captured in a database \u2013 available to be used in the moment.\n\nThere are some core components of a real-time data stack. They should include the ability to scale-out fast, and an elastic datastore capable of ingesting and distributing data as it streams in. Organizations dealing with real-time data streams have long leaned toward Apache Cassandra as the database of choice, thanks to its high throughput and scalability and its ability to intake and distribute data very fast.\n\nHigh-scale streaming technology, such as Apache Kafka or Apache Pulsar, is another key part of an event-driven architecture. Modern data apps require streaming technologies that can deliver the reactive engagement at the point of interaction that end users have come to expect.\n\nThe open data stack\n\nAt DataStax, our goal has been to build an open data stack that enables enterprises to mobilize real-time data to build high-scale data apps \u2013 but it\u2019s also a foundational, integrated set of technologies that can integrate with a host of other products and toolsets (including analytics platforms).\n\nThree important components make up the stack we offer: Astra DB, a database-as-a-service built on Cassandra; Astra Streaming, built on the advanced streaming technology of open source Pulsar; and Change Data Capture (CDC) for Astra DB, which enables the streaming of real-time operational data across an organization\u2019s data ecosystem.\n\nA key part of our stack is the word \u201copen\u201d \u2013 and this brings us back to the analytics discussion. Many enterprises find that there\u2019s an impedance mismatch between software systems that aren\u2019t event-based and the kind of real-time analytics that produce the most valuable insights. Companies are left to struggle with stale data that can only represent a view that\u2019s hours or even days old. As the demand skyrockets for up-to-the-moment accuracy to drive smarter, instantaneous decisions and customer experiences, the need to correct this misalignment becomes increasingly urgent.\n\nWith an open, real-time data stack, not only does that impedance mismatch problem go away, but organizations are open to integrate their platform and connect their data to any number of other technologies, platforms, and toolsets \u2013 including real-time analytics and data stores like Flink, Apache Pinot and Apache Druid to name just a few.\n\nFlexibility is built in with an open data stack. Let\u2019s say an organization\u2019s data science team needs to ask a specific business question (an \u201cad hoc query,\u201d in analytics parlance) of the operational data store \u2013 one that isn\u2019t answered by predefined or predetermined datasets. Ad hoc queries are often difficult to solve, particularly on large datasets.\n\nYet when a stack is built with openness and real-time data driven by events in mind, it becomes relatively simple to pipe data from an operational backend into any manner of data analytics platforms. In the case of DataStax\u2019s offerings, our recent introduction of CDC for Astra DB has essentially enabled us to embed a high-throughput, scale-out streaming capability into the database. This dramatically simplifies the ability to pipe any data, with millisecond-response times, from an operational backend (in our case, Cassandra) into Snowflake, or AWS Athena. It also makes it far easier to move data generated by analytical systems into edge datastores to help improve application performance.\n\nIn essence, an application developer doesn\u2019t have to worry that the database they\u2019ve chosen to power real-time user interactions is going to impede types of analytics that are necessary to drive the business forward.\n\nMeeting new expectations\n\nReal-time analytics is just one example of the kind of powerful tools an enterprise has at its fingertips when it builds an architecture that can take full advantage of the data generated by business events. An event-based, real-time data architecture is precisely how businesses today create the experiences that consumers expect.\n\nLearn more about DataStax Astra Streaming, which is now generally available\n\nAbout Ed Anuff:\n\nEd is chief product officer at DataStax. He has over 25 years experience as a product and technology leader at companies such as Google, Apigee, Six Apart, Vignette, Epicentric, and Wired.