Building near real-time analytical applications that combine real-time data inserts, updates, and fast analytics is almost impossible with any single Hadoop storage technology. The introduction of Apache Kudu and the "KIKS" stack breaks through this barrier, making it possible to build near real-time analytical applications that are simple, fast, and reliable. In this course, designed for developers, architects, and engineers with some experience working with common Hadoop components (Kafka, Hive, Spark, Impala, etc.), you'll use "KIKS" to create an app that demonstrates the real-time ingestion, persistence, and visualization of time-series events.
Kudu is at the center of this architecture. It combines real-time inserts, random lookups, and fast analytics into a single storage layer without the need for the complexities of the lambda architecture, making time-series and IOT use-cases much easier to conquer than with previous generation big data technologies. The app you'll build uses real-time financial data, but it also applies to use cases in IOT, retail, manufacturing, and other industries with real-time analytical needs.