As part of the drumbeat for Spark Summit West in San Francisco (June 6-8), learn how analyzing stats from professional sports leagues is an instructive use case for data analytics using Apache Spark with SQL.
In the United States, many diehard sports fans morph into amateur statisticians to get an edge over the competition in their fantasy sports leagues. Depending on one’s technical chops, this “edge” is usually no more sophisticated than simple spreadsheet analysis,
Thanks to Karthik Vadla, Abhi Basu, and Monica Martinez-Canales of Intel Corp. for the following guest post about using CDH for cost-effective processing/indexing of DICOM (medical) images.
Medical imaging has rapidly become the best non-invasive method to evaluate a patient and determine whether a medical condition exists. Imaging is used to assist in the diagnosis of a condition and, in most cases, is the first step of the journey through the modern medical system.
Thanks to Pedro Boado and Abel Fernandez Alfonso from Santander’s engineering team for their collaboration on this post about how Santander UK is using Apache HBase as a near real-time serving engine to power its innovative Spendlytics app.
The Spendlytics iOS app is designed to help Santander’s personal debit and credit-card customers keep on top of their spending, including payments made via Apple Pay. It uses real-time transaction data to enable customers to analyze their card spend across time periods (weekly,
In this guest post, members of the Barclays Advanced Data Analytics Team describe the results of an offsite hackathon to develop a recommendation system using Apache Spark.
In the depths of the cold, wet British winter, the Advanced Data Analytics team from Barclays escaped to a villa on Lanzarote, Canary Islands, for a week to collaboratively solve a key business problem: how to design a better customer experience. We framed the problem in the context of using customer shopping behavior data to build a personalized recommender system.
Users of the latest release of the Genome Analysis Toolkit, an open source framework for analyzing high-throughput DNA sequencing data, can now choose Apache Spark for data processing.
Ever since the Human Genome Project produced the first draft sequence of the human genome in 2000, the cost of sequencing has dropped exponentially, from around US$100 million per genome then to around US$1,000 today. Over the same period, we have seen massive growth in the storage and processing capabilities of big data technologies like Apache Hadoop.