InfoQ Homepage Data Analysis Content on InfoQ
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Introduction to Data Science
Bryan Nehl makes an introduction to the data science: data formats, ETL tools, NoSQL databases, languages, libraries, techniques and approaches for exploring data and extracting value from it.
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Persistence: A View from Stratosphere
Stefan Edlich discusses big data systems -Spanner, Presto- and the future of data persistence, data analytics, data formats and of NoSQL/NewSQL in general.
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Big Data in Capital Markets
The authors present design patterns and use cases of capital market firms that are incorporating big data technologies into their credit risk analysis, price discovery or sentiment analysis software.
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Haskell in the Newsroom
Erik Hinton discusses the successes and failures of making a cultural shift in the newsroom at NYT to accept Haskell and some of the projects Haskell has been used for.
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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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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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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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Deploying Machine Learning and Data Science at Scale
Nick Kolegraff discusses common problems and architecture to support all the phases of data science and how to start a data science initiative, sharing lessons from Accenture, Best Buy, and Rackspace.
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Functional Programming for Optimization Problems with City of Palo Alto Open Data
Paco Nathan reviews an example data analysis application written in Cascalog used for a recommender system based on City of Palo Alto Open Data.
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Add ALL the Things: Abstract Algebra Meets Analytics
Avi Bryant discusses how the laws of group theory provide a useful codification of the practical lessons of building efficient distributed and real-time aggregation systems.
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"Big Data" Agile Analytics
Ken Collier discusses Agile Analytics, a combination of sophisticated analytics techniques, lean learning principles, agile delivery methods, and "big data" technologies.
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Making the Internet a Better Place: Scaling AppNexus
Mike Nolet shares lessons learned scaling AppNexus and architectural details of their system processing 30TB/day: Hadoop, DNS built in GSLB and Keepalived, and real-time data streaming built in C.