Reports 11 Min Read

Why Real-Time Streaming Data Isn’t Delivering Real-Time Insights

Streaming (real-time) data has become the lifeblood of today’s enterprises. They are collecting and processing data in real-time, but they still struggle to deliver valuable streaming data insights quickly enough for meaningful action.

The reason? Traditional data processing uses storethen-process architectures that compound cost and complexity when handling large amounts of data at low latency. In an attempt to deliver real-time to the business, large enterprises spend valuable money, time, and talent on data source technologies in an effort to reduce latency, when in reality the multiple systems compound latency. For other enterprises, such solutions are cost-prohibitive

Streaming data applications built with Nstream are able to seamlessly combine streaming data and data at rest, and apply context and run business logic instantaneously in a process-then-store approach. These streaming data applications complete the data pipeline by running the entire application stack in stream. Here’s how they work to overcome the challenges of streaming data analytics.