InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Deconstructing the Database
Rich Hickey deconstructs the monolithic database into separate services, transactions, storage, query, combining them with a data model based on atomic facts to provide new capabilities and tradeoffs.
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How to Build Big Data Pipelines for Hadoop Using OSS
Costin Leau discusses Big Data, current available tools for dealing with it, and how Spring can be used to create Big Data pipelines.
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Reverend Bayes, Meet Countess Lovelace: Machine Learning and Programming
Andy Gordon discusses machine learning using functional programming, explaining how Infer.NET Fun turns the succinct syntax of F# into an executable modeling language for Bayesian machine learning.
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F# Big Data Scripting
Matthew Moloney shares some of the F# tools built at Microsoft Research for dealing with Big Data.
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The Evolving Panorama of Data
Rebecca Parsons proposes taking a different look at data, using different approaches and tools, then looks at some of the ways social data is used these days.
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Scaling Scalability: Evolving Twitter Analytics
Dmitriy Ryaboy shares some of the lessons learned scaling Twitter’s analytics infrastructure: Data loves a schema, Make data sources discoverable, and Make costs visible.
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Lean Data Architecture: Minimize Investment, Maximize Value
Manvir Singh Grewal and Brandon Byars propose a business intelligence workflow along with Lean principles and practices for implementing a data warehouse and reporting capability.
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Storm: Distributed and Fault-Tolerant Real-time Computation
Nathan Marz introduces Twitter Storm, outlining its architecture and use cases, and takes a look at future features to be made available.
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Postgres Demystified
Craig Kerstiens presents the history of Postgres, the basics of developing with Postgres, notes on its performance, and tips on querying it.
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Extending the Enterprise Data Warehouse with Hadoop
Rob Lancaster explains the steps made by Orbitz in order to bridge the gap between their data in the data warehouse and the data in Hadoop.
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Big Data Problems in Monitoring at eBay
Bhaven Avalani and Yuri Finklestein discuss 4 aspects encountered at eBay when dealing with monitoring data: reduction of data entropy, robust data distribution, metric extraction, efficient storage.
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100% Big Data, 0% Hadoop, 0% Java
Pavlo Baron presents a big data case, a solution and the tools for collecting, mining and storing large amounts of data without using Hadoop or Java.