Gehören Sie zu den Ersten, denen das gefällt!
Querying streaming data with SQL to derive actionable insights at the point of impact in a timely and continuous fashion offers various benefits over querying data in a traditional database. However, although it is desirable for many use cases to transition to a stream based paradigm, stream processing systems and traditional databases are fundamentally different: in a database, the data is (more or less) fixed and the queries are executed in an ad-hoc manner, whereas in stream processing systems, the queries are fixed and the data flows through the system in real-time. This leads to different primitives that are required to model and query streaming data.
In this session, we will introduce basic stream processing concepts and discuss strategies that are commonly used to address the challenges that arise from querying of streaming data. We will discuss different time semantics, processing guarantees and elaborate how to deal with reordering and late arriving of events. Finally, we will compare how different streaming use cases can be implemented on AWS by leveraging Amazon Kinesis Data Analytics and Apache Flink.