InfoQ Homepage Architecture & Design Content on InfoQ
-
R for Big Data
Indrajit Roy presents HP Labs’ attempts at scaling R to efficiently perform distributed machine learning and graph processing on industrial-scale data sets.
/presentations/r-big-data/en/mediumimage/Roybig.jpg)
-
Real-World Datomic: An Experience Report
Craig Andera explains Datomic from the perspective gained in implementing and optimizing a real-world production system, detailing the Datomic indexing process.
/presentations/datomic-use-case/en/mediumimage/Craigbig.jpg)
-
Tracking Millions of Ganks in Near Real Time
Garrett Eardley explores how Riot Games is using Riak for their stats system, discussing why they chose Riak, the data model and indexes, and strategies for working with eventually consistent data.
/presentations/riak-game-scale/en/mediumimage/Garbig.jpg)
-
Evolution of the Netflix API
Ben Christensen describes Netflix API's evolution to a web service platform serving all devices and users, the challenges met in operations, deployment, performance, fault-tolerance, and innovation.
/presentations/netflix-api-evolution/en/mediumimage/Benbig.jpg)
-
REEF: Retainable Evaluator Execution Framework
Rusty Sears introduces REEF along with examples of computational frameworks, including interactive sessions, iterative graph processing, bulk synchronous computations, Hive queries, and MapReduce.
/presentations/reef/en/mediumimage/rustySears-large.jpg)
-
Fast and Dynamic
Maxime Chevalier-Boisvert discusses making dynamic languages faster providing various examples of optimizations: SmallTalk, LISP machine, Google V8 and others.
/presentations/dynamic-performance/en/mediumimage/Maxbig.jpg)
-
Evolving Mobile Architectures at MI9
Cameron Barrie, James Brett, Stewart Gleadow share lessons learned using Agile methodologies to build an iOS application, discussing its architecture and the benefits of hybrid apps.
/presentations/jump-in-mi9/en/mediumimage/3 big.jpg)
-
RESTful Groovy
Kyle Boon reviews 3 frameworks for building RESTful WS- Grails, Dropwizard and Ratpack-, comparing their code readability, maintainability, deployment, metrics collection, scalability and testability.
/presentations/rest-groovy-framework/en/mediumimage/Kylebig.jpg)
-
How a Small Team Scales Instagram
Mike Krieger discusses Instagram's best and worst infrastructure decisions, building and deploying scalable and extensible services.
/presentations/scaling-instagram/en/mediumimage/Mikebig.jpg)
-
Scaling AncestryDNA using Hadoop and HBase
Bill Yetman and Jeremy Pollack discuss using Agile techniques -start simple, get going, iterate- and the “measure everything” principle to create the architecture behind the Family History website.
/presentations/scaling-ancestry-dna/en/mediumimage/Jerbig.jpg)
-
Apache Tez: Accelerating Hadoop Query Processing
Bikas Saha and Arun Murthy detail the design of Tez, highlighting some of its features and sharing some of the initial results obtained by Hive on Tez.
/presentations/apache-tez/en/mediumimage/Bikbig.jpg)
-
Why Ruby Isn't Slow
Alex Gaynor explains how he solved the usual Ruby VM speed problems with Topaz, a high performance VM built on the same technologies that power PyPy.
/presentations/ruby-performance/en/mediumimage/Alexbig.jpg)