Streaming Big Data – An integration story of Apache Spark & Kafka

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Partner Post - Billy Mobile The Leading Affiliate Platform

Posted: December 11, 2017

Companies operating big data infrastructures frequently face the challenge to process massive streaming data. By implementing real-time, or near real-time, processing systems, technology firms may have numerous competitive advantages. Enhanced agility is here a key asset. Because businesses can respond more quickly to threats in the making, but also to potential opportunities. Complex Event Processing, for instance, allows for the instantaneous analysis of data streams for certain events. In the advertising industry, one use case is the monitoring of fraudulent activities, for example conversion anomalies. So real-time streaming and real-time analytics go hand in hand.
At Billy Mobile, streaming services have beefed up real-time processing, such as the offer prediction model, the tracking system, the ingestion and aggregation of real-time stats and reporting systems, or also anomaly detection. With this brand new article series, Billy presents two extraordinary components in their big data infrastructure realizing real-time streaming: How to integrate Apache Spark Streaming with Kafka 0.10. The series will introduce both technologies (Spark Streaming and Kafka) separately, and will explain how to join both together. Billy is running these technologies together since 2016.
For the upcoming years, Apache Spark seems to become the platform of choice for data engineers. Spark is already the trending big data technology right now. It was disruptive to the established Big Data and Data Science ecosystem. And sure, it possesses amazing capabilities. The same is true for Apache Kafka. Although Kafka has evolved during the years, and right now it becomes a distributed streaming platform, in its guts, it’s still a publish-subscribe messaging system. There are a lot of different connectors already provided by the open source community, for all kind of streaming technologies and frameworks. So, usually it is easy and fast to integrate your own applications with Kafka and Spark Streaming.
Read the entire series: Apache Spark Streaming + Kafka 0.10: an integration love story