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This article mainly introduces "what are the knowledge points of OLAP and data analysis". In the daily operation, I believe that many people have doubts about the knowledge points of OLAP and data analysis. The editor consulted all kinds of materials and sorted out simple and easy-to-use methods of operation. I hope it will be helpful to answer the questions of "what are the knowledge points of OLAP and data analysis?" Next, please follow the editor to study!

OLAP literally refers to timely data analysis. But in practice, OLAP is more of a solution for data analysis. OLAP's software preprocesses data into data cubes. Basically, all the possible totals are calculated in advance. When the user chooses to display some kind of summary, OLAP can quickly calculate the results based on the pre-calculated results. Thus, it can well support the timely analysis of a very large amount of data.

In other words, OLAP actually includes two aspects. First, OLAP provides an interactive data analysis model. OLAP divides the data into dimension and measure. By selecting dimension and measure, users can timely calculate the summary of data under different combinations of dimension. There may be a misunderstanding here. In fact, OLAP itself does not define a client-side interaction model. For example, most traditional OLAP is through a crosstab interface, but the new interface is more in the form of visual charts.

The second point, perhaps more essential, is the way data is handled as defined by OLAP. This includes the ideas and methods of data pre-calculation. In domestic manufacturers, there are few people who develop their own OLAP back-end data processing. Among foreign manufacturers, IBM,Microsoft,Oracle has its own OLAP back-end.

As a technology with more than ten years of practical application history, OLAP is a well-deserved veteran of data analysis. But in the application of OLAP, users also encounter a lot of problems. The most important of these is the weakness of OLAP in flexibility. The use of OLAP requires users to have an analysis of the data in advance and list all dimension and measure. In this way, the OLAP processor can calculate the relevant values in advance.

And because the data cube needs a lot of space. Its size grows exponentially. Therefore, when designing OLAP, users must limit the number of dimension to a relatively small range. In practical applications, users can only analyze within this pre-selected dimension. If the user needs to look at the data from a different perspective, the OLAP must be redesigned and generated.

In recent years, there have been several major technological innovations in the field of BI, which have made up for the shortcomings of OLAP to a great extent. In the front-end display, more visual mode is used in data analysis. This does not simply refer to showing the results of OLAP through a chart. The new visual data analysis tool realizes a complete interactive mode on the basis of displaying the data through charts, so that users can arbitrarily test the data through different angles and methods.

In the back-end processing, as the dominant database structure, and the concept of cloud computing, so that timely analysis can no longer rely on pre-calculation to deal with a huge amount of data. This not only solves big data's calculation, but also avoids the inflexible problems caused by OLAP.

StyleScope is a data processing structure that integrates visualization theory and column-based cloud computing, and achieves a perfect combination. Users no longer need to face the complex OLAP system, and can quickly create a powerful data analysis system.

At this point, the study of "what are the knowledge points of OLAP and data analysis" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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