Version 1.0 is "a major milestone in the evolution of Apache Storm", writes Apache Software Foundation VP for Apache Storm P. Taylor Goetz, and it includes many new features and improvements. In particular, Goetz claims a 3x–16x boost in performance.
Embrace decentralization, build service-based systems and attack the problems that come with distributed state using stream processing tools, Ben Stopford urged in his presentation at the recent QCon London conference.
With many databases in a system they are rarely independent from each other, instead pieces of the same data are stored in many of them. Using transactions to keep everything in sync is a fragile solution. Working with a stream of changes in the order they are created is a much simpler and more resilient solution, Martin Kleppmann stated in his presentation at the recent QCon London conference.
Netflix has shed light on how the company uses the latest version of their Keystone Data Pipeline, a petabyte-scale real-time event stream processing system for business and product analytics. This news summarizes the three major versions of the pipeline, now used by almost every application at Netflix.
Architecting a scalable and dynamic system without caching is explained by Peter Morgan, head of engineering for the sports betting company William Hill. The values of the bets on sporting events change constantly. No data can be cached; all system values must be current. Distributed Erlang processes model domain objects which instantly recalculate system values based on data streams from Kafka.
A key problem with the whole Reactive space and why it’s so hard to understand is the vocabulary with all the terms and lots of different interpretations of what it means, Peter Ledbrook claims and also a reason for why he decided to work out what it’s all about and sharing his knowledge in a presentation.
Yahoo! has benchmarked three of the main stream processing frameworks: Apache Flink, Spark and Storm.
Storing events in a relational database and creating the event identity as a globally unique and sequentially increasing number is an important and maybe uncommon decision when working with an event-sourced Command Query Responsibility Segregation (CQRS) system Konrad Garus writes in three blog posts describing his experiences from a recent project building a system of relatively low scale.
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.