Streaming Data is about processing and responding to incoming data, as it happens in real time.  Whereas most businesses run on delayed batch data, companies that implement streaming technology can benefit from improved customer experience.

Streaming Technology - Sourcing Events
In order to move towards Event Streaming and Event Based Architecture, the first thing we need to do is source data in real time and publish it onto an event stream. Even this first step can be difficult, because many applications do not “publish” events as they are
Free Guide - An Overview Of Streaming Data Technology
Many companies are looking to use their data more effectively in order to improve their customer experience and the efficiency of their business. Speed is an important part of this. The earlier you can respond to incoming data, the more opportunities you have to improve the customer experience. R…
Moving From Batch To Streaming Extract, Transform and Load
There are many situations in Enterprise IT where we need to move, copy or integrate data. For example, populating a centralised data warehouse or data lake, integrating two systems such as an ecommerce and CRM system, or exchanging data between partner organisations. Moving data in this manner …
Build Vs Buy For Real Time Event Streaming Platforms
Over the next few years, many businesses are going to be building real time streaming data platforms. The aim will be for data to stream in from many sources and be available earlier for analysis or to power user experiences or business processes in real time. Most of these event
Stateless and Stateful Event Processing
In previous blog articles, we discussed the potential of event stream processing as a means of improving customer experience and business efficiency. In this article, we wanted to look deeper at the type of analysis we might want to do on these event streams to bring the opportunity to life.
The Challenges Of Stream Processing Applications
The idea of processing a stream of events to generate business value is a relatively simple one to understand. However, to deliver on this technically quickly becomes complex . Some of the key challenges you will encounter include: Exactly Once Processing – If we are processing a stream o…
Materialised Views On Event Streams
Imagine we have a stream of events representing new orders: Order { ID : 1, Category : Electronics, Value : 123 }Order { ID : 2, Category : Homeware, Value : 456 } Much of the move to event driven architecture is about responding to these individual events and building analytics over them in order …