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 set up 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.
You Will Learn
- The benefits of LDW architecture and how it differs from classic DW architecture
- The challenges of data lakes
- How easily new data sources can be made available for analytics and data science
- How self-service analytics can be supported by an LDW, and how it helps to share specifications across different analytics tools
- How your organization can successfully migrate to a flexible LDW architecture in a step-by-step fashion
- The possibilities and limitations of available data virtualization products and how these products work
- How access to big data stored in Hadoop and NoSQL systems can be made available to analysts easily and transparently
- The differences between a physical data lake and a logical data lake
- How LDWs help integrate self-service analytics with classic forms of business intelligence
- The real-life experiences of organizations that have implemented an LDW
- Business intelligence specialists; data analysts; data warehouse designers; business analysts; data scientists; technology planners; technical architects; enterprise architects; IT consultants; IT strategists; systems analysts; database developers; database administrators; solutions architects; data architects; IT managers