The amount of data generated by devices and machines continues to rise. Exponential data growth places an enormous amount of pressure on systems that must keep that information moving at high speeds from machines to data lakes or warehouses and into analytics platforms. Data storage and management are only a subset of today’s data-driven requirements — deriving analytical value is also extremely important. That value is difficult to show without systems that can analyze streams of information in real time. In particular, organizations are trying to enable non-technical users to elicit value from their data using off-the-shelf visualization tools.
Enterprises are exploring a variety of architectures and technologies to incorporate real-time analytics on streaming data into their ecosystems. An emerging area is the use of general purpose visualization tools on streaming data. Instead of the traditional approach of custom coding and integrations that apply in only limited situations, this new paradigm simplifies the extraction of value from streaming data. Recent innovations are opening up a new range of capabilities for non-technical users. Such capabilities enable many valuable use cases: predictive maintenance, operations optimization, financial services risk reporting and cybersecurity, among others.
Ultimately, the usefulness of streaming technologies will be measured by the businesses who depend on them for critical capabilities and use cases. Here are a few examples of real-time capabilities and the use cases they enable that are critical for modern enterprises:
Streaming Analytics Capabilities Support Real-World Use Cases
Alerting is an obvious and critical capability for streaming analytics. You can receive automatic feedback when a certain event or trend occurs that indicates the need for attention. While alerting is not unique to streams, the fact that users can receive alerts in real time means that they can respond more quickly. There is no built-in delay due to technology processes like moving data into a large data store. The following use cases exemplify how this might work.
• Streaming analytics can be used in cybersecurity, where anomalous behavior should be immediately flagged for investigation. Cybersecurity environments are increasingly turning to machine learning for identifying anomalous, and thus potentially suspicious, behavior in a network. Using alert-based visualizations in conjunction with machine learning outputs is an ideal way to get a broader community of analysts to help with detecting cyberthreats, so you don’t have to depend only on security experts and developers.