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  • Traffic Data Monitoring Using IoT, Kafka and Spark Streaming

    by Amit Baghel on  Sep 28, 2016 1

    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.

  • 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.

  • 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.

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 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.

Unified Data Modeling for Relational and NoSQL Databases

Posted by Allen Wang on  Feb 28, 2016

Current enterprise data architectures include NoSQL databases co-existing with RDBMS. In this article, author discusses a solution for managing NoSQL & relational data using unified data modeling. 5

Getting Ready for IoT’s Big Data Challenges with Couchbase Mobile

Posted by Ralph Winzinger on  Jan 20, 2016

Our physical world is about to become digitally enabled and according to various predictions, there will be many billions of IoT devices going online and collecting data in the coming years. 1

Big Data Processing with Apache Spark - Part 3: Spark Streaming

Posted by Srini Penchikala on  Jan 07, 2016

In this article, third installment of Apache Spark series, author discusses Apache Spark Streaming framework for processing real-time streaming data using a log analytics sample application. 5

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