InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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NoSQL: Past, Present, Future
Eric Brewer takes a look at NoSQL’s history and considers what should be done so the current NoSQL solutions to evolve in order to address the full range of the application needs.
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Big Data, Small Computers
Cliff Click discusses RAIN, H2O, JMM, Parallel Computation, Fork/Joins in the context of performing big data analysis on tons of commodity hardware.
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Introducing Apache Hadoop: The Modern Data Operating System
Eli Collins introduces Hadoop: why it came about, the benefits it produces, its history, its architecture, use cases and applications.
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Petabyte Scale Data at Facebook
Dhruba Borthakur discusses the different types of data used by Facebook and how they are stored, including graph data, semi-OLTP data, immutable data for pictures, and Hadoop/Hive for analytics.
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Facebook News Feed: Social Data at Scale
Serkan Piantino discusses news feeds at Facebook: the basics, infrastructure used, how feed data is stored, and Centrifuge – a storage solution.
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Keynote: Spring 2012 and Beyond
Adrian Colyer, Juergen Hoeller, Mark Pollack and Graeme Rocher present SpringSource’s Unifying Component Model, current developments regarding Big Data, and betting on Grails.
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Eventually-Consistent Data Structures
Sean Cribbs discusses Convergent Replicated Data Types, data structures that tolerate eventual consistency.
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Embracing Concurrency at Scale
Justin Sheehy discusses designing reliable distributed systems that can scale in order to deal with concurrency problems and the tradeoffs required by such systems.
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Understanding Indexing Without Needing to Understand Data Structures
Zardosht Kasheff suggest using 3 rules for indexing SQL databases: Retrieve less data, Avoid point queries, and Avoid sorting.
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Not Your Father’s Transaction Processing
Michael Stonebraker compares how RDBMS, NoSQL and NewSQL support today’s big data transaction processing needs. He also introduces VoltDB, an in-memory NewSQL database.
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Runaway Complexity in Big Data, and a Plan to Stop It
Nathan Marz outlines several sources of complexity introduced in data systems - Lack of human fault-tolerance, Conflation of data and queries, Schemas done wrong - and what can be done to avoid them.
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Spring Data - NoSQL - No Problems...
Peter Bell introduces 4 NoSQL categories –Key-Value, Document, Column, Graph - and explains how one can use Spring Data to work with such data stores.
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