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.
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.
InfoQ spoke with authors of Spark GraphX in Action book, Apache Spark framework and what's coming up in the area of graph data processing and analytics.
Containers are just around the corner for the Windows community, and this article takes a closer look at using SQL Server containers.
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 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.
Kostiantyn Cherniavskyi looks at some of the issues surrounding the object-relation impedance mismatch and how many of them can be solved with hybrid databases such as Starcounter. 5
NoSQL databases have been around for several years and have become a preferred choice for managing unstructured data. InfoQ spoke with four panelists about the current state of NoSQL databases. 1
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.
It makes no difference how hard you try- some form of lock-in is unavoidable. What matters most is understanding the layers of lock-in, and how to assess and reduce your switching costs.
Datastax recently announced DataStax Graph to support graph data models. InfoQ spoke with Martin Van Ryswyk from DataStax team about the new product. 1
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.