EAP - Accelerating behavorial analytics at PayPal using Hadoop
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PayPal today generates massive amounts of data?from clickstream logs to transactions and routine business events. Analyzing customer behavior across this data can be a daunting task. Data Technology ...
PayPal today generates massive amounts of data?from clickstream logs to transactions and routine business events. Analyzing customer behavior across this data can be a daunting task. Data Technology team at PayPal has built a configurable engine, Event Analytics Pipeline (EAP), using Hadoop to ingest and process massive amounts of customer interaction data, match business-defined behavioral patterns, and generate entities and interactions matching those patterns. The pipeline is an ecosystem of components built using HDFS, HBase, a data catalog, and seamless connectivity to enterprise data stores. EAP?s data definition, data processing, and behavioral analysis can be adapted to many business needs. Leveraging Hadoop to address the problems of size and scale, EAP promotes agility by abstracting the complexities of big-data technologies using a set of tools and metadata that allow end users to control the behavioral-centric processing of data. EAP abstracts the massive data stored on HDFS as business objects, e.g., customer and page impression events, allowing analysts to easily extract patterns of events across billions of rows of data. The rules system built using HBase allows analysts to define relationships between entities and extrapolate them across disparate data sources to truly explore the universe of customer interaction and behaviors through a single lens.
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