Modern software increasingly operates on data in near real-time. There is business value in sub-second responses to changing information and stream processing is one way to help turn data into knowledge as fast as possible, Kevin Webber explains in an introduction to Reactive Streams.
To make microservices awesome Domain-Driven Design (DDD) is needed, the same mistakes made 5-10 years ago and solved by DDD are made again in the context of microservices, David Dawson claimed in his presentation at this year’s DDD Exchange conference in London.
Structuring data as a stream of events is an idea appearing in many areas and is the ideal way of storing data. Aggregating a read model from these events is an ideal way to present data to a user, Martin Kleppmann claims explains when describing the fundamental ideas behind Stream Processing, Event Sourcing and Complex Event Processing (CEP).
At the Bacon Conference last May, bitly Lead Application Developer Sean O'Connor explained the most relevant lessons bitly developers learned while building a distributed system that handles 6 billions clicks per month.
Last week at the Microsoft Worldwide Partner Conference, Microsoft took the wraps off of Azure Event Hubs. This service – in preview release until General Availability next month – is for high throughput ingress of data streams generated by devices and services. Event Hubs resembles Amazon Kinesis and uses an identical pricing scheme based on data processing units and transaction volume.
DataTorrent is a real-time streaming and analyzing platform that can process over 1B real-time events/sec.
Typesafe has announced the early preview of Akka Streams, an open source implementation of the Reactive Streams draft specification using an Actor-based implementation. Reactive Streams is an initiative to provide a standard for asynchronous stream processing with non-blocking back pressure on the JVM. Back pressure in needed to make sure the data producer doesn't overwhelm the data consumer.
The New York Times R&D Lab has released streamtools, a general purpose, graphical tool for dealing with streams of data, under Apache 2 license.
Complex Event Processing, CEP, can be very useful for problems that have to do with time e.g. querying over historical data when you want to correlate things that have happened at different times, Greg Young explained in a recent presentation.
Last week Yahoo! announced the open source release of Storm on Hadoop cluster. This implementation enables Storm applications to utilize the computational resources of a Hadoop cluster along with accessing Hadoop’ storage resources such as HBase and HDFS.
A new open source project – Dempsy adds one more option for people trying to do real time processing of big data. Comparable to Storm and S4 Dempsy is most applicable to near real time stream processing where latency is more important than guaranteed delivery.
At the peak of the SOA hype, Complex Event Processing (CEP) was hailed as SOAs "next big thing". Since then several CEP solutions have come and gone, and the term CEP is not used as much as it once was. Has it failed to deliver on initial claims or has it simply become a core part of most SOA infrastructures that we take it for granted? And does CEP have anything to offer the Cloud?
This month, Yahoo! released a new open source framework for "processing continuous, unbounded streams of data." The framework, named S4, allows for massively distributed computations over data that is constantly changing. InfoQ examines some of the examples and compares S4 to other technologies.
App Arch Guide 2.0 (Microsoft patterns&practices), Chapter 6, talks about architectural styles like Message-Bus, Layered Architecture, SOA. Beside those styles there are numerous architectural patterns like Plug-in, Peer-to-Peer, Publish-Subscribe. Some authors make a difference between architectural styles, patterns and metaphors.
Dan Pritchett suggests that analyzing streams of events using Event Stream Processor could be an interesting alternative solution to data warehousing applications, which have, in his opinion, important downsides in terms of cost, scalability and reactivity.