Sahaana Suri offers an overview of MacroBase, a new analytics engine from Stanford designed to prioritize the scarcest resource in large-scale, fast-moving data streams: human attention. MacroBase allows reconfigurable, real-time root-cause analyses that have already diagnosed issues in production streams in mobile, data center, and industrial applications.

Karthik Ramasamy, Sanjeev Kulkarni, Avrilia Floratau, Ashvin Agrawal, Arun Kejariwal, and Sijie Guo walk you through state-of-the-art streaming systems, algorithms, and deployment architectures, covering the typical challenges in modern real-time big data platforms and offering insights on how to address them.

While stream processing is now popular, streaming architectures must be more reliable and scalable than ever beforemore like microservice architectures in fact. Dean Wampler defines "stream" based on characteristics for such systems, using specific tools like Kafka, Spark, Flink, and Akka as examples, and argues that big data and microservices architectures are converging.

Although the most widely used language for data analysis, SQL is only slowly being adopted by open source stream processors. One reason is that SQL's semantics and syntax were not designed with streaming data in mind. Fabian Hueske explores Apache Flink's two relational APIs for streaming analyticsstandard SQL and the LINQ-style Table APIdiscussing their semantics and showcasing their usage.

Although the most widely used language for data analysis, SQL is only slowly being adopted by open source stream processors. One reason is that SQL's semantics and syntax were not designed with streaming data in mind. Fabian Hueske explores Apache Flink's two relational APIs for streaming analyticsstandard SQL and the LINQ-style Table APIdiscussing their semantics and showcasing their usage.

"Stream" is a buzzword for several things that share the idea of timely handling of neverending data. Big data architectures are evolving to be stream oriented. Microservice architectures are inherently message driven. Dean Wampler defines "stream" based on characteristics for such systems, using specific tools as examples, and argues that big data and microservices architectures are converging.

Life doesnt happen in batches. Being able to work with data from continuous events as data streams is a better fit to the way life happens, but doing so presents some challenges. Ellen Friedman examines the advantages and issues involved in working with streaming data, takes a look at emerging technologies for streaming, and describes best practices for this style of work.