In her presentation "Large-Scale Stream Processing with Apache Kafka" at QCon New York 2016, Neha Narkhede introduces Kafka Streams, a new feature of Kafka for processing streaming data. According to Narkhede stream processing has become popular because unbounded datasets can be found in many places. It is no longer a niche problem like, for example, machine learning.
Event sourcing and CQRS are two patterns that has emerged in the Domain-Driven Design (DDD) community. Stream processing builds on similar ideas but has emerged in a different community, Martin Kleppmann noted in his presentation at the Domain-Driven Design Europe conference earlier this year comparing event sourcing with stream processing.
On Thursday, April 21 Microsoft announced the integration between Azure Stream Analytics and Power BI has reached General Availability (GA). Using this capability, customers can gain real-time insight into their business performance by analyzing in-flight data streams.
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.