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Architecting Scalable, Dynamic Systems when Eventual Consistency Won’t Work

by Michael Stiefel on  Jan 20, 2016

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.

The Basics of Being Reactive

by Jan Stenberg on  Jan 20, 2016

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! Benchmarks Apache Flink, Spark and Storm

by Abel Avram on  Dec 23, 2015

Yahoo! has benchmarked three of the main stream processing frameworks: Apache Flink, Spark and Storm.

CQRS, Read Models and Persistence

by Jan Stenberg on  Oct 20, 2015 4

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.

Introducing Reactive Streams

by Jan Stenberg on  Sep 30, 2015

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.

DDD, Events and Microservices

by Jan Stenberg on  Jun 29, 2015 1

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.

Making Sense of Event Stream Processing

by Jan Stenberg on  Mar 22, 2015 1

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).

Lessons Learned Building Distributed Systems at Bitly

by Sergio De Simone on  Jul 23, 2014

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.

Microsoft Tackles Internet-of-Things With New Data Stream Processing Service

by Richard Seroter on  Jul 22, 2014 1

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 1.0 Handles >1B Real-time Events/sec

by Abel Avram on  Jun 03, 2014 7

DataTorrent is a real-time streaming and analyzing platform that can process over 1B real-time events/sec.

Reactive Streams with Akka Streams

by Bienvenido David on  Apr 21, 2014

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.

New York Times Lab Introduces Visual Stream Processing Tool

by Michael Hausenblas on  Apr 01, 2014 1

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.

Greg Young on Using Complex Event Processing

by Jan Stenberg on  Jan 29, 2014 4

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.

Yahoo! Open Sources Storm on Hadoop

by Boris Lublinsky on  Jun 17, 2013 2

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.

Dempsy – a New Real-time Framework for Processing BigData

by Boris Lublinsky on  Apr 25, 2012 8

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.

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