Big Data is no longer equivalent to Hadoop in the industry

For a long time, industry projects solved big data problems with Hadoop. The massive scalability of MapReduce algorithms and the HBase database brought solutions to an unanticipated level of computing. But this obstructs the view for the need of change. Business goals that emerge from Industry 4.0 or IoT have long been addressed with a suboptimal architecture. New business goals require a rethinking of the big data architecture instead of being driven by the known Hadoop ecosphere. We discuss the transformation of a Hadoop-centric middleware solution to a streaming architecture from a business value perspective. The new architecture also replaces a single NoSQL database by polyglot persistence that allows to focus on best performance and quality of each data processing step. We also discuss alternative architecture approaches like Lambda that were evaluated in the course of the transformation. We show that a single technology choice likely leads to a solution that is suboptimal.