InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Ground-up Introduction to In-memory Data
Viktor Gamov covers In-Memory technology, distributed data topologies, making in-memory reliable, scalable and durable, when to use NoSQL, and techniques for Big In-Memory Data.
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Pulsar: Real-time Analytics at Scale
Sharad Murthy & Tony Ng present Pulsar, a real-time streaming system which can scale to millions of events per second with high availability and 4GL language support.
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Exploratory Data Analysis with R
Matthew Renze introduces the R programming language and demonstrates how R can be used for exploratory data analysis.
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Spreadsheets for Developers
Felienne Hermans presents various algorithms that outlining the power of Excel, showing that spreadsheets are fit for TDD and rapid prototyping.
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Machine Learning and IoT
Ajit Jaokar discusses data science and IoT: sensor data, real-time processing, cognitive computing, integration of IoT analytics with hardware, IoT’s impact on healthcare, automotive, wearables, etc.
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The Many Faces of Apache Kafka: How is Kafka Used in Practice
Neha Narkhede discusses how companies are using Apache Kafka and where it fits in the Big Data ecosystem.
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Interactive Analytics at Scale with Druid
Julien Lavigne du Cadet discusses how Criteo uses Druid: an open-source, real-time data store designed to power interactive applications at scale, covering Druid's architecture and internals.
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Mini-talks: Machine Intelligence, Algorithms for Anti-Money Laundering, Blockchain
Mini-talks: The Machine Intelligence Landscape: A Venture Capital Perspective. The future of global, trustless transactions on the largest graph: blockchain. Algorithms for Anti-Money Laundering
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Financial Modeling with Apache Spark: Calculating Value at Risk
Sandy Ryza aims to give a feel for what it is like to approach financial modeling with modern big data tools, using the Monte Carlo method for a a basic VaR calculation with Spark.
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LDAP at Lightning Speed
Howard Chu covers highlights of the LMDB design and discusses some of the internal improvements in slapd due to LMDB, as well as the impact of LMDB on other projects.
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Translating Imperative Code to MapReduce
The authors present an approach for automatic translation of sequential, imperative code into a parallel MapReduce framework using Mold, translating Java code to run on Apache Spark.
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The Deep Learning Revolution: Rethinking Machine Learning Pipelines
Soumith Chintala introduces deep learning, what it is, why it has become popular, and how it can be fitted into existing machine learning solutions.
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