Skip to main content

Analytics on Fly - Read It

The basis for real-time analytics is to have all resources at disposal in the moment they are called for . So far, special materialized data structures, called cubes, have been created to efficiently serve analytical reports. Such cubes are based on a fixed number of dimensions along which analytical reports can define their result sets. Consequently, only a particular set of reports can be served by one cube. If other dimensions are needed, a new cube has to be created or existing ones have to be extended. In the worst case, a linear increase in the number of dimensions of a cube can result in an exponential growth of its storage requirements. Extending a cube can result in a deteriorating performance of those reports already using it. The decision to extend a cube or build a new one has to be considered carefully. 

In any case, a wide variety of cubes may be built during the lifetime of a system to serve reporting, thus increasing storage requirements and also maintenance efforts.

Instead of working with a predefined set of reports, business users should be able to formulate ad-hoc reports. Their playground should be the entire set of data the company owns, possibly including further data from external sources. Assuming a fast in-memory database, no more pre-computed materialized data structures are needed. As soon as changes to data are committed to the database, they will be visible for reporting. 

The preparation and conversion steps of data if still needed for reports are done during query execution and computations take place on the fly. Computation on the fly during reporting on the basis of cubes that do not store data, but only provide the interface for reporting, solves a problem that has existed up to now and allows for performance optimization of all analytical reports likewise

Comments

Popular posts from this blog

R Vs SAS differences to read today

Statistical analysis should know by every software engineer. R is an open source statistical programming language. SAS is licensed analysis suite for statistics. The two are very much popular in Machine learning and data analytics projects.
SAS is analysis suite software and R is a programming language R ProgrammingR supports both statistical analysis and GraphicsR is an open source project.R is 18th most popular LanguageR packages are written in C, C++, Java, Python and.NetR is popular in Machine learning, data mining and Statistical analysis projects. SASSAS is a statistical analysis suite. Developed to process data sets in mainframe computers.Later developed to support multi-platforms. Like  Mainframe, Windows, and LinuxSAS has multiple products. SAS/ Base is very basic level.SAS is popular in data related projects. Learn SAS vs R Top Differences between SAS Vs R Programming SAS AdvantagesThe data integration from any data source is faster in SAS.The licensed software suite, so you…

Blue Prism complete tutorials download now

Blue prism is an automation tool useful to execute repetitive tasks without human effort. To learn this tool you need the right material. Provided below quick reference materials to understand detailed elements, architecture and creating new bots. Useful if you are a new learner and trying to enter into automation career. The number one and most popular tool in automation is a Blue prism. In this post, I have given references for popular materials and resources so that you can use for your interviews.
RPA Blue Prism RPA blue prism tutorial popular resources I have given in this post. You can download quickly. Learning Blue Prism is a really good option if you are a learner of Robotic process automation.
RPA Advantages The RPA is also called "Robotic Process Automation"- Real advantages are you can automate any business process and you can complete the customer requests in less time.

The Books Available on Blue Prism 
Blue Prism resourcesDavid chappal PDF bookBlue Prism BlogsVi…

Top Differences Read Today Agile vs Waterfall model

The Agile and Waterfall both models are popular in Software development. The Agile model is so flexible compared to waterfall model. Top differences on Waterfall vs Agile give you clear understanding on both the processes. Waterfall ModelThe traditional model is waterfall. It has less flexibility.Expensive and time consuming model.Less scalable to meet the demand of customer requirements.The approach is top down. Starting from requirements one has to finish all the stages, till deployment to complete one cycle.A small change in requirement, one has to follow all the stages till deployment.Waterfall model creates idleness in resource management. Agile ModelAgile model is excellent for rapid deployment of small changesThe small split-requirements you can call them as sprintsLess idleness in resource management.Scope for complete team involvement.Faster delivery makes client happy.You can deploy changes related to compliance or regulatory quickly.Collaboration improves among the team.