Learn about the near real-time data ingest architecture for transforming and enriching data streams using Apache Flume, Apache Kafka, and RocksDB at Santander UK.
Cloudera Professional Services has been working with Santander UK to build a near real-time (NRT) transactional analytics system on Apache Hadoop. The objective is to capture, transform, enrich, count, and store a transaction within a few seconds of a card purchase taking place. The system receives the bank’s retail customer card transactions and calculates the associated trend information aggregated by account holder and over a number of dimensions and taxonomies.
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,
Evaluating which streaming architectural pattern is the best match to your use case is a precondition for a successful production deployment.
The Apache Hadoop ecosystem has become a preferred platform for enterprises seeking to process and understand large-scale data in real time. Technologies like Apache Kafka, Apache Flume, Apache Spark, Apache Storm, and Apache Samza are increasingly pushing the envelope on what is possible. It is often tempting to bucket large-scale streaming use cases together but in reality they tend to break down into a few different architectural patterns,
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.
The new integration between Flume and Kafka offers sub-second-latency event processing without the need for dedicated infrastructure.
In this previous post you learned some Apache Kafka basics and explored a scenario for using Kafka in an online application. This post takes you a step further and highlights the integration of Kafka with Apache Hadoop, demonstrating both a basic ingestion capability as well as how different open-source components can be easily combined to create a near-real time stream processing workflow using Kafka,