Fast Data architectures are the answer to the increasing need for the enterprise to process and analyze continuous streams of data to accelerate decision making and become reactive to the particular characteristics of their market.

Apache Spark is a popular framework for data analytics. Its capabilities include SQL-based analytics, dataflow processing, graph analytics and a rich library of built-in machine learning algorithms. These libraries can be combined to address a wide range of requirements for large-scale data analytics.

To address Fast Data flows, Spark offers two API's: The mature Spark Streaming and its younger sibling, Structured Streaming. In this talk, we are going to introduce both APIs. Using practical examples, you will get a taste of each one and obtain guidance on how to choose the right one for your application.