InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Refactoring to Reactive - Anatomy of a JDBC migration
Reactive programming offers built-in solutions for some of the most difficult challenges in programming, including concurrency management and flow control. So you might ask - how do I get there; can I introduce it in phases? In this article we transform a legacy application to a reactive model using RxJava.
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Key Takeaway Points and Lessons Learned from QCon San Francisco 2016
The 10th annual QCon San Francisco was the biggest yet, bringing together over 1500 team leads, architects, project managers, and engineering directors. Over 125 practitioner-speakers presented 92 full-length technical sessions and 32 in-depth tutorials, providing deep insights into real-world architectures and state of the art software development practices from a practitioner’s perspective.
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Article Series: Getting a Handle on Data Science as a Software Developer
Software developers and managers are realizing that they need data science among their skills, to be able to tackle pressing problems. In this series, field experts provide guidance to help us navigate among the available data analysis options. They explore ways of understanding where data science is needed and where it’s not, and how to turn it into an asset.
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Data Science up and down the Ladder of Abstraction
Although Clojure lacks the extensive toolbox and analytic community of the most popular data science languages, R and Python, it provides a powerful environment for developing statistical thinking and for practicing effective data science.
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Reactor by Example
Reactor, like RxJava 2, is a fourth generation reactive library launched by Spring custodian Pivotal. It builds on the Reactive Streams specification, Java 8, and the ReactiveX vocabulary. In this article, we’ll draw a parallel between Reactor and RxJava, and showcase the common elements as well as the differences.
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Getting Started with Machine Learning
A quick introduction to the machine learning field, exploring both supervised and unsupervised approaches.
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Case Study: Selecting Big Data and Data Science Technologies at a large Financial Organisation
Adopting Big Data and Data Science technologies into an organisation is a transformative project similar to an agile transformation and with many similar challenges. In this article, the author describes such a project for a FTSE100 financial services company.
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Solving Business Problems with Data Science
Enterprises are increasingly realising that many of their most pressing business problems could be tackled with the application of a little data science. This article, the first in a series, looks at the foundations of a successful business-orientated data science project.
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Testing RxJava
You are ready to explore reactive opportunities in your code but you are wondering how to test out the reactive idiom in your codebase. In this article Java Champion Andres Almiray provides techniques and tools for testing RxJava.
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Peter Cnudde on How Yahoo Uses Hadoop, Deep Learning and Big Data Platform
Yahoo uses Hadoop for different use cases in big data & machine learning areas. They also use deep learning techniques in their products like Flickr. InfoQ spoke with Peter Cnudde on how Yahoo leverages big data platform technologies.
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A Quick Primer on Isolation Levels and Dirty Reads
Recently MongoDB found itself at the top of Reddit again when developer David Glasser learned the hard way that MongoDB performs dirty reads by default. In this article we will explain what isolation levels and dirty reads are and how they are implemented in popular databases.
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Traffic Data Monitoring Using IoT, Kafka and Spark Streaming
Internet of Things (IoT) is an emerging disruptive technology and becoming an increasing topic of interest. One of the areas of IoT application is the connected vehicles. In this article we'll use Apache Spark and Kafka technologies to analyse and process IoT connected vehicle's data and send the processed data to real time traffic monitoring dashboard.