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  • Big Data as a Service, an Interview with Google's William Vambenepe

    Many of the Big Data technologies in common use originated from Google and have become popular open source platforms, but now Google is bringing an increasing range of big data services to market as part of its Google Cloud Platform. InfoQ caught up with Google's William Vambenepe, who's lead product manager for Big Data services to ask him about the shift towards service based consumption.

  • The Promise of Healthcare Analytics

    Data analytics play a central role in the healthcare system by improving outcomes and quality of life while helping to control costs. In this article, author describes the role analytics can play with the emerging wearable technologies with biophysical interfaces, physiological sensors, and embedded diagnostic tools.

  • NoSQL For Mere Mortals Review and Author Q&A

    Addison-Wesley Professional NoSQL for Mere Mortals provides an introduction to NoSQL databases spanning across the major types of databases that fall under the NoSQL umbrella and explaining both advantages and shortcomings that each database type offers. InfoQ has spoken with the book's author, Dan Sullivan.

  • Big Data and IT-Enabled Services: Ecosystem and Coevolution?

    In this article, based on a research study, author presents big data as a service-oriented and evolutionary case of disruptive IT-enabled services (IESs) rather than as datasets. Big data services emerge from combining diverse resources from an ecosystem of technologies, market needs, social actors, and other institutional contexts.

  • Analytics, Machine Learning, and the Internet of Things

    In this article, author discusses the evolving technologies like Machine Learning and Internet of Things and how to exploit them for data analytics. He also talks about how organizations can benefit from these new sources of information and intelligence embedded in their environments.

  • High Tech, High Sec.: Security Concerns in Graph Databases

    Graph NoSQL databases support data models with connected data and relationships. In this article, author discusses the security implications of graph database technology. He talks about the privacy and security concerns in use cases like graph discovery, knowledge management, and prediction.

  • Full Stack Web Development Using Neo4j

    When building a web application there are a lot of choices for the database. In this article, author discusses why Neo4j Graph database is a good choice as a data store for your web application if your data model contains lot of connected data and relationships.

  • Shaping Big Data Through Constraints Analysis

    In this article, author Carlos Bueno describes a method for analyzing constraints on the shape and flow of data in systems. He talks about the factors useful for system analysis like working set & average transaction sizes, request & update rates, consistency, locality, computation, and latency. He also discusses big data architecture details of two use cases, movie streaming and face recognition.

  • Big Data Processing with Apache Spark - Part 2: Spark SQL

    Spark SQL, part of Apache Spark big data framework, is used for structured data processing and allows running SQL like queries on Spark data. In this article, Srini Penchikala discusses Spark SQL module and how it simplifies running data analytics using SQL interface. He also talks about the new features in Spark SQL, like DataFrames and JDBC data sources.

  • Highly Distributed Computations Without Synchronization

    Synchronization of data across systems is expensive and impractical when running systems at scale. Traditional approaches for performing computations or information dissemination are not viable. In this article Basho Sr. Software Engineer Chris Meiklejohn explores the basic building blocks for crafting deterministic applications that guarantee convergence of data without synchronization.

  • Big Data Processing with Apache Spark – Part 1: Introduction

    Apache Spark is an open source big data processing framework built around speed, ease of use, and sophisticated analytics. In this article, Srini Penchikala talks about how Apache Spark framework helps with big data processing and analytics with its standard API. He also discusses how Spark compares with traditional MapReduce implementation like Apache Hadoop.

  • Building a Mars Rover Application with DynamoDB

    DynamoDB is a NoSQL database service that aims to be easily managed, so you don't have to worry about administrative burdens such as operating and scaling. This article shows how to use Amazon DynamoDB to create a Mars Rover application. You can use the same concepts described in this post to build your own web application.

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