InfoQ Homepage Database Content on InfoQ
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Grails Transactions
Burt Beckwith discusses performing transactions in Grails, covering services, customizing transaction attributes (isolation, propagation levels), two-phase commit, using JMS, and testing the code.
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Deploying, Scaling, and Running Grails on AWS and VPC
Ryan Vanderwerf explains how to create and deploy a Grails application on AWS VPC using various services such as RDS, S3, autoscaling, S3FS, EBS, etc.
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Building a Multi-Master Distributed Redis in Erlang
Chad DePue presents the process of building Edis, a Redis clone written in Erlang, allowing pluggable backends and implementing the Paxos algorithm.
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From The Lab To The Factory: Building A Production Machine Learning Infrastructure
Josh Wills discusses using Hadoop technologies to build real-time data analysis models with a focus on strategies for data integration, large-scale machine learning, and experimentation.
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Scaling Pinterest
Details on Pinterest's architeture, its systems -Pinball, Frontdoor-, and stack - MongoDB, Cassandra, Memcache, Redis, Flume, Kafka, EMR, Qubole, Redshift, Python, Java, Go, Nutcracker, Puppet, etc.
/presentations/scaling-pinterest/en/mediumimage/Marbig.jpg)
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Grails Transactions
Burt Beckwith discusses performing transactions in Grails, covering services, customizing transaction attributes (isolation, propagation levels), two-phase commit, using JMS, and testing the code.
/presentations/grails-transaction-2gx/en/mediumimage/Burtbig.jpg)
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Graph Computing at Scale
Matthias Broecheler discusses graph computing, introducing the Aurelius graph cluster enabling graph computing at scale by building on distributed systems like Cassandra, HBase, and Hadoop.
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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.
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Search for the Holy Grail (and test it once found)
Baruch Sadogursky overviews and compares search and testing tools available to Grails developers.
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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.
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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.
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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.
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