InfoQ Homepage Infrastructure Content on InfoQ
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But is it Safe?
While it is rare to hear the question, "Is this software safe?", the safety aspects of software are becoming increasingly important. The proliferation of IoT devices increases the widespread impact a small problem can cause. Several techniques exist to help developers analyze and improve the safety of software they create.
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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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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.
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Big Data Processing with Apache Spark - Part 5: Spark ML Data Pipelines
With support for Machine Learning data pipelines, Apache Spark framework is a great choice for building a unified use case that combines ETL, batch analytics, streaming data analysis, and machine learning. In this fifth installment of Apache Spark article series, author Srini Penchikala discusses Spark ML package and how to use it to create and manage machine learning data pipelines.
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Spark GraphX in Action Book Review and Interview
“Spark GraphX in Action” book from Manning Publications, authored by Michael Malak and Robin East, provides a tutorial based coverage of Spark GraphX, the graph data processing library from Apache Spark framework. InfoQ spoke with authors about the book and Spark GraphX library as well as overall Spark framework and what's coming up in the area of graph data processing and analytics.
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Chris Fregly on the PANCAKE STACK Workshop and Data Pipelines
InfoQ Interviews Chris Fregly, organizer for the 4000+ member Advanced Spark and TensorFlow Meetup about the PANCAKE STACK workshop, Spark and building data pipelines for a machine learning pipeline
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Christine Doig on Data Science as a Team Discipline
Christine Doig spoke at this year's OSCON Conference about data science as a team discipline and how to navigate the data science Python ecosystem. InfoQ spoke with Christine about challenges data science teams need to address to be more effective.
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Book Review and Excerpt: Infrastructure as Code
In this article we review the book Infrastructure as Code - Managing Servers in the Cloud written by Kief Morris, who is leading Continuous Delivery and DevOps at ThoughtWorks Europe. In over 300 pages, Morris lays down the foundation for Infrastructure as Code and outlines the main patterns and practices recommended for building it.
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Article Series: Cloud and "Lock-in"
With the fast-pace of cloud changes (new services, providers entering and exiting), cloud lock-in remains a popular refrain. But what does it mean, and how can you ensure you're maximizing your cloud investment while keeping portability in mind?
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Big Data Analytics with Spark Book Review and Interview
Big Data Analytics with Spark book, authored by Mohammed Guller, provides a practical guide for learning Apache Spark framework for different types of big-data analytics projects, including batch, interactive, graph, and stream data analysis as well as machine learning. InfoQ spoke with author about the book & development tools for big data applications.
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Virtual Panel on (Cloud) Lock-In
There's no shortage of opinions on the topic of technology lock-in. InfoQ reached out to four software industry leaders to participate in a lively virtual panel on this topic: Joe Beda, Simon Crosby, Krish Subramanian, and Cloud Opinion.
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Big Data Processing with Apache Spark - Part 4: Spark Machine Learning
In this fourth installment of Apache Spark article series, author Srini Penchikala discusses machine learning concepts and Spark MLlib library for running predictive analytics using a sample application.