http://insights.iolap.com/author/ajacob/Recent years have seen the emergence of self-service and data visualization vendors such as Tableau, Power BI and Qlik Sense that has forced enterprise BI players such as MicroStrategy to improvise their tools to compete with these vendors. I have been a solutions architect in the field of BI been using MicroStrategy for more than 10 years. From my perspective as an architect, I appreciate the power of the underlying SQL engine in MicroStrategy and do not foresee self-service vendors replacing MicroStrategy as an enterprise BI tool in the coming years. What thrills me most as a Solutions Architect in
Introduction Netezza is a data warehousing appliance that uses an Asymmetric Massive Parallel Processing Architecture. The Netezza architecture is driven by two fundamental principles- process close to the data source and do not move data unless absolutely necessary. Among the two principles, the latter is implemented primarily by commodity hardware Field Programmable Gate Arrays (FPGAs). FPGA plays the pivotal role in filtering out data as soon as possible, removing I/O bottlenecks, freeing up valuable downstream components such as memory and processor. Zone Maps are internal data structures of Netezza that enables FPGAs to filter out data. In simplistic terms, a
Business Intelligence as a field has gained rapid maturity over a period of time. We are living in an era when we constantly hear buzzwords like Big Data, Prescriptive Analytics and Data Science. In the midst of these catchy phrases, the one phrase that still stands out for me is Self-Service BI. Self-Service BI is defined as an approach that enables business users to access and work with corporate data even if they do not have a background in Business Intelligence. This approach is primarily intended to reduce dependency of business users on IT for creating their reports. In the
Recently, I had an interesting conversation with my project’s data architect regarding possible back-dated changes for a primary dimension — Employee — in the data warehouse. In our existing data model, Employee was maintained as a Type 2 slowly changing dimension.
Six months into deployment, it was confirmed by business management that employee changes could be back-dated. This conversation reminded me of a project that I was part of three years earlier where such back-dated scenarios happened frequently.