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  • Big Data Processing with Apache Spark - Part 5: Spark ML Data Pipelines

    by Srini Penchikala on  Sep 24, 2016

    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.

  • Book Review: Site Reliability Engineering - How Google Runs Production Systems

    by João Miranda on  Sep 21, 2016

    "Site Reliability Engineering - How Google Runs Production Systems" is an open window into Google's experience and expertise on running some of the largest IT systems in the world. The book describes the principles that underpin the Site Reliability Engineering discipline. It also details the key practices that allow Google to grow at breakneck speed without sacrificing performance or reliability.

  • Spark GraphX in Action Book Review and Interview

    by Srini Penchikala on  Sep 12, 2016

    “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.

Chris Fregly on the PANCAKE STACK Workshop and Data Pipelines

Posted by Dylan Raithel on  Aug 29, 2016

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

Christine Doig on Data Science as a Team Discipline

Posted by Srini Penchikala on  Aug 26, 2016

Christine Doig spoke at OSCON Conference about data science as a team discipline and how to navigate data science Python ecosystem. InfoQ spoke with Christine about challenges of data science teams.

Book Review and Excerpt: Infrastructure as Code

Posted by Abel Avram on  Jul 25, 2016

We review the book Infrastructure as Code by Kief Morris, who lays down the foundation for Infrastructure as Code and outlines the main patterns and practices recommended for building it.

Big Data Analytics with Spark Book Review and Interview

Posted by Srini Penchikala on  Jun 23, 2016

Big Data Analytics with Spark, authored by Mohammed Guller, provides a practical guide for learning Apache Spark. InfoQ and the author discuss the book & development tools for big data applications.

Big Data Processing with Apache Spark - Part 4: Spark Machine Learning

Posted by Srini Penchikala on  May 15, 2016

In this fourth installment of Apache Spark article series, author Srini Penchikala discusses machine learning concept & Spark MLlib library for running predictive analytics using a sample application.

The Holistic Approach: Preventing Software Disasters

Posted by Olivier Bonsignour on  Apr 28, 2016

Olivier Bonsignour on what "X-Raying" software means, how it can help prevent software disasters and why CIOs should care. 3

Is HyperContainer the Answer for Cloud Native Applications?

Posted by Xu Wang on  Mar 31, 2016

Xu Wang introduces HyperContainer, a runtime for running Docker images on any hypervisor, providing hardware-enforced isolation for multi-tenant environments.

The Role of a Data Scientist in 2016

Posted by Ed Jones on  Mar 27, 2016

Data Science has been getting lot of attention as organizations are starting to use data analytics to gain insights into their data. This article takes a closer look at Data Scientist role in 2016.

Spark in Action Book Review & Interview

Posted by Srini Penchikala on  Mar 18, 2016

In Spark in Action book, authors Petar and Marko discuss Apache Spark for data processing batch & streaming data. InfoQ spoke with them about Spark framework, developer tools, and upcoming features.

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