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InfoQ Homepage Database Content on InfoQ

  • The InfoQ eMag: Perspectives on GDPR

    This eMag addresses three overlapping but distinct perspectives on the impact of GDPR - customers will be thankful, our reputation will be safeguarded, and crippling fines will be avoided.

  • The InfoQ eMag: Microservices - Patterns and Practices

    While the underlying technology and patterns are certainly interesting, microservices have always been about helping development teams be more productive. Experts who spoke about microservices at QCon SF 2017 did not simply talk about the technical details of microservices, but included a focus on the business side and more human-oriented aspects of developing distributed software systems.

  • Big Data Processing with Apache Spark

    In this mini-book, the reader will learn about the Apache Spark framework and will develop Spark programs for use cases in big-data analysis. The book covers all the libraries that are part of Spark ecosystem, which includes Spark Core, Spark SQL, Spark Streaming, Spark MLlib, and Spark GraphX.

  • The InfoQ eMag: Cloud Native Patterns & Practices

    In this eMag, the InfoQ team pulled together stories that best help you understand this cloud-native revolution, and what it takes to jump in. It features interviews with industry experts, and articles on key topics like migration, data, and security.

  • The Morning Paper Issue 6 - Computer Science Applied

    Subtle production failures running Microsoft Azure, identifying users from aggregated mobile data,  taking over a city with Philips Hue lightbulbs, processing a trillion edge graph on a single machine, and why Rust should be your systems language of choice.

  • The InfoQ eMag: Getting a Handle on Data Science

    This eMag looks at data science from the ground up, across technology selection, assembling raw and unstructured data, statistical thinking, machine learning basics, and the ethics of applying these new weapons.

  • The InfoQ eMag: The Current State of NoSQL Databases

    This eMag focuses on the current state of NoSQL databases. It includes articles, a presentation and a virtual panel discussion covering a variety of topics ranging from highly distributed computations, time series databases to what it takes to transition to a NoSQL database solution.

  • The Morning Paper Quarterly Review Issue 2

    A summary of five CS papers chosen from the 55 that Adrian Colyer has reviewed for his Morning Paper blog during Q2 2016. Amongst the five papers in the magazine Colyer takes a look at how Facebook collect and analyse over 1 trillion data points per day across 2 billion unique time series, and the technology behind bots on Q&A systems like Siri, Cortana, Alexa et al.

  • The Morning Paper Quarterly Review Issue 1

    A summary of five CS papers chosen from the 66 that Adrian Coyler has reviewed for his Morning Paper blog during the first quarter of 2016. Topics include distributed transactions, transaction recovery, and Hyperloglog.

  • InfoQ eMag: Graph Databases

    This eMag focuses on the graph database landscape and the real world use cases of graph databases. It includes articles and interviews covering topics like data modeling in graph databases and how companies use graph databases in their application. It also includes an article on full stack web development using a graph database.

  • InfoQ eMag: Hadoop

    Apache Hadoop is proving useful in deriving insights out of large amounts of data, and is seeing rapid improvements. Hadoop 2 now goes beyond Map-Reduce; it is more modular, pluggable and flexible and it fits a variety of use cases better. We explore this as well as some tools that can help utilize Hadoop better.

  • InfoQ eMag: Scalability

    This eMag examines topics such as how Twitter re-architected its code-base to improve stability and performance, the approaches Netflix uses to be hyper-resilient, and how Java is replacing C++ for low latency coding. We also look at some lower level tricks such as feedback controls for auto-scaling, and using memory and execution profiling to identify performance bottlenecks in Java.

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