InfoQ Homepage Database Content on InfoQ
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An API for Distributed Computing
Cliff Click introduces a coding style & API for in-memory analytics that handles datasets from 1K to 1TB without changing a line of code and clusters with TB of RAM and hundreds of CPUs.
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How Developers Can Treat Ovarian Cancer
Mridula Jayaraman shares from her experience developing a next generation sequencing solution used to customize cancer treatment based on patient's genetic makeup.
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Making Java Groovy
Ken Kousen advises Java developers how to do similar tasks in Groovy: building and testing applications, accessing both relational and NoSQL databases, accessing web services, and more.
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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.
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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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