To design effective fraud-detection architecture, look no further than the human brain (with some help from Spark Streaming and Apache Kafka).
At its core, fraud detection is about detection whether people are behaving “as they should,” otherwise known as catching anomalies in a stream of events. This goal is reflected in diverse applications such as detecting credit-card fraud, flagging patients who are doctor shopping to obtain a supply of prescription drugs,
Thanks to Torsten Kilias and Alexander Löser of the Beuth University of Applied Sciences in Berlin for the following guest post about their INDREX project and its integration with Impala for integrated management of textual and relational data.
Textual data is a core source of information in the enterprise. Example demands arise from sales departments (monitor and identify leads), human resources (identify professionals with capabilities in ‘xyz’), market research (campaign monitoring from the social web),
Thanks to Sam Shuster, Software Engineer at Edmunds.com, for the guest post below about his company’s use case for Spark Streaming, SparkOnHBase, and Morphlines.
Every year, the Super Bowl brings parties, food and hopefully a great game to appease everyone’s football appetites until the fall. With any event that brings in around 114 million viewers with larger numbers each year, Americans have also grown accustomed to commercials with production budgets on par with television shows and with entertainment value that tries to rival even the game itself.
Our thanks to Micah Whitacre, a senior software architect on Cerner Corp.’s Big Data Platforms team, for the post below about Cerner’s use case for CDH + Apache Kafka. (Kafka integration with CDH is currently incubating in Cloudera Labs.)
Over the years, Cerner Corp., a leading Healthcare IT provider, has utilized several of the core technologies available in CDH, Cloudera’s software platform containing Apache Hadoop and related projects—including HDFS,
This Spark Streaming use case is a great example of how near-real-time processing can be brought to Hadoop.
Spark Streaming is one of the most interesting components within the Apache Spark stack. With Spark Streaming, you can create data pipelines that process streamed data using the same API that you use for processing batch-loaded data. Furthermore, Spark Steaming’s “micro-batching” approach provides decent resiliency should a job fail for some reason.