The need to respond quickly to customer insights has driven increasing adoption of event-driven architectures and stream processing. Frameworks such as Spark, Flink or Kafka Streams offer a paradigm where simple event consumers and producers can cooperate in complex networks to deliver real-time insights. But this programming style takes time and effort to master and when implemented as single-point applications, it lacks interoperability. Making stream processing work universally on a large scale can require a significant engineering investment. Now, a new crop of tools is emerging that offers the benefits of stream processing to a wider, established group of developers who are comfortable using SQL to implement analytics. Standardizing on SQL as the universal streaming language lowers the barrier for implementing streaming data applications. Tools like ksqlDB and Materialize help transform these separate applications into unified platforms. Taken together, a collection of SQL-based streaming applications across an enterprise might constitute a streaming data warehouse.
