August 8, 2018
9:00 am - 5:30 pm
Duration: Full Day Course
Rick van der Lans
Data analysts and data scientists don’t understand the concept of data overload. The more data they can get their hands on, the happier they are. They want to analyze data from internal transaction systems, social media, external open data, clickstream data, and big data sources. They want it fast and flexible, and they want to do it themselves. Most classic data warehouse (DW) architectures, however, have been designed to support standard forms of reporting and analytics and aren’t ready for new forms of data usage such as data science and investigative and complex analytics.
Data lakes have been introduced as a storage environment that can support all the data scientist’s data requests. Unfortunately, lakes are not as easy to setup as some would have you believe. A more practical solution is the logical data warehouse (LDW), which has shown to be a more agile foundation for delivering and transforming data and makes it easy to quickly plug in new data sources. Mature technology in the form of data virtualization servers exists to develop an LDW. Products from DataVirtuality, Denodo, IBM, RedHat, StoneBond, and Tibco offer proven support for data science and self-service and advanced analytics.
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