InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Machine Learning for Programming
Peter Norvig keynotes on using machine learning techniques to solve more general software problems, helping both the advanced programmer and the novice one.
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SQL Strikes Back! Recent Trends in Data Persistence and Analysis
Dean Wampler takes a look at SQL’s resurgence and specific example technologies, including: NewSQL, Hybrid SQL, SQL abstractions on top of file-based data, SQL as a functional programming language.
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NoSQL Is Dead
Eric Redmond explains the differences and commonalities amongst many kinds of databases and takes a stab at the marketing term “NoSQL.”
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Intro to Datomic
Stuart Sierra provides an introduction to Datomic's data model, architecture, query syntax, and transactions.
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High Performance Computing Contributions to the World of Big Data
Sharan Kalwani presents the history of HPC and the technologies and trends which have contributed to creating the world of big data, covering applications of HPC resulting in big data technologies.
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A Distributed Transactional Database on Hadoop
John Leach explains using HBase co-processors to support a full ANSI SQL RDBMS without modifying the core HBase source, showing how Hadoop/HBase can replace traditional RDBMS solutions.
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Why Would You Integrate Solr and Hadoop?
Yann Yu discusses how Solr and Hadoop complement each other, and how to use Solr as a real-time, analytical, full-text search front-end to data stored in Hadoop.
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1.5 Million Log Lines Per Second: Building and Maintaining Flume Flows at Conversant
Mike Keane presents how Conversant migrated to Flume, managing 1000 agents across 4 data centers, processing over 50B log lines per day with peak hourly averages of over 1.5 million log lines/sec.
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The Big Data Imperative: Discovering & Protecting Sensitive Data in Hadoop
Jeremy Stieglitz discusses best practices for a data-centric security , compliance and data governance approach, with a particular focus on two customer use cases.
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Why Spark Is the Next Top (Compute) Model
Dean Wampler argues that Spark/Scala is a better data processing engine than MapReduce/Java because tools inspired by mathematics, such as FP, are ideal tools for working with data.
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Unified Big Data Processing with Apache Spark
Matei Zaharia talks about the latest developments in Spark and shows examples of how it can combine processing algorithms to build rich data pipelines in just a few lines of code.
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Customer Analytics on Hadoop
Bob Kelly presents case studies on how Platfora uses Hadoop to do analytics for several of their customers.
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