Author Archives: Amund Tveit

Continuous Deployment for Python/Tornado with Jenkins, Selenium and PhantomJS

Posted on May 28, 2013 by Amund Tveit

The purpose of Continuous Deployment is to increase Quality and Efficiency, see e.g. The Software Revolution behind Linkedin’t Gushing Profits or read on This posting presents an overview of Atbrox’ ongoing work on Automated Continuous Deployment. We develop in several … Continue reading →

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Combining Hadoop/Elastic Mapreduce with AWS Redshift Data Warehouse

Posted on February 25, 2013 by Amund Tveit

There are currently interesting developments of scalable (up to Petabytes), low-latency and affordable datawarehouse related solutions, e.g. AWS Redshift (cloud-based) [1] Cloudera’s Impala (open source) [2,3] Apache Thrill (open source) [4] This posting shows how one of them – AWS … Continue reading →

Posted in analytics, cloud computing, Hadoop and Mapreduce | Tagged , , , , | 5 Comments

Mapreduce Algorithms – Presentation held at O’Reilly Strata Conference

Posted on October 1, 2012 by Amund Tveit

My presentation held at O’Reilly Strata Conference in London, UK, October 1st 2012 Best regards, Amund Tveit

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Atbrox @ O’Reilly Strata Conference in London

Posted on September 26, 2012 by Amund Tveit

Atbrox is participating and holding a Hadoop/Mapreduce algorithm related presentation at the O’Reilly Strata Conference in London October 1st and 2nd. If you are there and would like to meet Atbrox send an email to info@atbrox.com

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A large-scale in-memory storage example – social network data

Posted on May 16, 2012 by Amund Tveit

This posting is a follow-up to the large-scale low-latency (RAM-based) storage related price estimates in my previous posting Main takeaways from Accel’s Big Data Conference. Assume you were to store and index large amounts of social network updates in-memory, e.g. … Continue reading →

Posted in in-memory, information retrieval, infrastructure, RAM | Leave a comment