Thanks to Jeff Palmucci, Director of Machine Learning at TripAdvisor, for permission to republish the following (originally appeared in TripAdvisor’s Engineering/Operations blog).
Here at TripAdvisor we have a lot of reviews, several hundred million according to the last announcement. I work with machine learning, and one thing we love in machine learning is putting lots of data to use.
I’ve been working on an interesting problem lately and I’d like to tell you about it.
Big Industries, Cloudera systems integration and reseller partner for Belgium and Luxembourg, has developed an integration of Apache Mesos and CDH that can be deployed and managed through Cloudera Manager. In this post, Big Industries’ Rob Gibbon explains the benefits of deploying Mesos on your cluster and walks you through the process of setting it up.
[Editor’s Note: Mesos integration is not currently supported by Cloudera, thus the setup described below is not recommended for production use.]
Apache Mesos is a distributed,
Thanks to Barclays employees Sam Savage, VP Data Science, and Harry Powell, Head of Advanced Analytics, for the guest post below about the Barclays use case for Apache Spark and its Scala API.
At Barclays, our team recently built an application called Insights Engine to execute an arbitrary number N of near-arbitrary SQL-like queries and execute them in a way that can scale with increasing N.
Our thanks to Karthik Vadla and Abhi Basu, Big Data Solutions engineers at Intel, for permission to re-publish the following (which was originally available here).
Data science is not a new discipline. However, with the growth of big data and adoption of big data technologies, the request for better quality data has grown exponentially. Today data science is applied to every facet of life—product validation through fault prediction,
Thanks to Wuheng Luo, a Hadoop and big data architect at Sears Holdings, for the guest post below about Pig job-level performance tuning
Many factors can affect Apache Pig job performance in Apache Hadoop, including hardware, network I/O, cluster settings, code logic, and algorithm. Although the sysadmin team is responsible for monitoring many of these factors, there are other issues that MapReduce job owners or data application developers can help diagnose,