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There was a time when a data warehouse architecture consisted of a chain of databases all running on one or two machines in our own data center. Handwritten ETL programs were used to copy and transform data from one database to another. But so much new technology offering innovative opportunities has become available, there are so many new BI requirements, and we have new ways to design our data warehouse architectures. Data warehouse architects are struggling with all these new developments. They have to find answers for an almost endless list of questions. Should the data warehouse be developed with Hadoop? Do we still need data marts if the BI tools read data into memory? Can we use Spark as query performance booster? What does it mean to design datavault-based data warehouses? How does data streaming and the IoT work together with the data warehouse? Should we move the entire architecture into the cloud? Can we replace the data warehouse by a data lake? What is the role of the logical data warehouse? Will an analytical SQL database server solve all our query performance problems? And so on, and so on.
This session discusses all the architectural and technical developments. How are they interrelated? How to migrate to a modern architecture? What are the pros and cons of all these developments?