Fast data architectures provide an answer to enterprises’ increasing need to process and analyze continuous streams of data, which helps accelerate decision making and enables faster responses to changing characteristics of their market. Apache Spark is a popular framework for data analytics. Its capabilities in the streaming domain are represented by two APIs: the low-level Spark Streaming and the more declarative Structured Streaming, which builds upon the recent advances in Spark SQL query optimization and code generation.
Gerard Maas offers a critical overview of the differences in these APIs, from the API user experience to dealing with time and with state and machine learning capabilities, and shares practical guidance on picking one or combining both to implement resilient streaming pipelines.
Gerard Maas is a senior software engineer at Lightbend, where he contributes to the Fast Data Platform and focuses on the integration of stream processing technologies. Previously, he held leading roles at several startups and large enterprises, building data science governance, cloud-native IoT platforms, and scalable APIs. He is the coauthor of Stream Processing with Apache Spark from O’Reilly. Gerard is a frequent speaker and contributes to small and large open source projects. In his free time, he tinkers with drones and builds personal IoT projects.
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