Does anyone know the main industries where OLAP is used and the trends, thanks!

OLAP (Online Analytical Processing).

What is Online Analytical Processing (OLAP)

The concept of Online Analytical Processing (OLAP) was first introduced by the father of relational databases, E.F. Codd, in 1993, and he also proposed 12 guidelines for OLAP. (OLTP).

Today's data processing can be roughly divided into two categories: online transaction processing OLTP (on-line transaction processing), on-line analytical processing OLAP (On-Line Analytical Processing). OLTP is the main application of traditional relational databases, mainly for basic, day-to-day transaction processing, such as banking transactions.OLAP is the main application of data warehousing systems to support complex analytical operations, focusing on decision support, and provide intuitive and easy to understand query results. The following table shows a comparison between OLTP and OLAP.

OLTPOLAP UsersOperators, Lower ManagementDecision Makers, Senior ManagementFunctionsDaily OperationsProcessingAnalyticsDecision MakingDB DesignApplication-OrientedTheme-OrientedDataCurrent, Up-To-DateDetailed, 2DDiscreteHistorical, Aggregated, Multi-DimensionalIntegrated, UnifiedAccessReads/writesDozens of RecordsReads Millions of RecordsWorking UnitsSimple TransactionsComplicated query users thousands hundreds of DB size 100MB-GB100GB-TB

OLAP is a class of software technologies that enable analysts, managers, or executives to access information quickly, consistently, and interactively from multiple perspectives to gain deeper insights into the data. the goal of OLAP is to satisfy the decision-support or specific querying and reporting needs of a multidimensional environment, and it does this by providing the ability to create a single, integrated, unified access to the data. Query and reporting needs, the core of its technology is the concept of "dimensional".

"Dimension" is the perspective from which people observe the objective world, and is a high-level classification of types. "Dimension" generally includes hierarchical relationships, which are sometimes quite complex. By defining a number of important attributes of an entity as multiple dimensions, users are able to compare data on different dimensions. Thus OLAP can also be described as a collection of multidimensional data analysis tools.

The basic multidimensional analysis operations of OLAP are drill up (roll up and drill down), slice and dice, and rotate (pivot), drill across, and drill through.

-Drilling is changing the level of dimensionality and transforming the granularity of the analysis. It includes roll up and drill down. roll up is to generalize from low-level detail data to high-level summary data in a dimension, or to reduce the number of dimensions, while drill down, on the contrary, looks deeper into the detail data from the summary data or adds new dimensions.

-Slicing and dicing are concerned with the distribution of metric data over the remaining dimensions after selecting values on a portion of the dimensions. If there are only two remaining dimensions, it is slicing; if there are three, it is chunking.

-Rotation is transforming the orientation of dimensions, i.e., rearranging the placement of dimensions in a table (e.g., rows and columns are interchanged).

OLAP has a variety of implementation methods, according to the different ways of storing data can be divided into ROLAP, MOLAP, HOLAP.

ROLAP represents the implementation of OLAP based on relational databases (Relational OLAP). Relational database as the core, to the relational structure for multi-dimensional data representation and storage. ROLAP will be multi-dimensional database multi-dimensional structure is divided into two types of tables: one is the fact table, used to store the data and dimensional keywords; the other is the dimensional table, i.e., at least one table is used for each dimension to store the dimension of the dimension of the hierarchy, the membership of the category of dimensional descriptive information. The dimension table and the fact table are linked together by the main keyword and the foreign keyword, forming a "star pattern". For complex dimensions, in order to avoid redundant data taking up too much storage space, multiple tables can be used to describe the extension of this star schema is called "snowflake schema".

MOLAP represents an OLAP implementation based on multidimensional data organization (Multidimensional OLAP). Multidimensional data organization as the core, that is, MOLAP uses multidimensional arrays to store data. Multidimensional data in the storage will form a "cube (Cube)" structure, in the MOLAP "cube" of the "rotation", "cut", "slice" is the main technology to produce multi-dimensional data reports.

HOLAP indicates that the OLAP implementation based on hybrid data organization (Hybrid OLAP). If the lower level is relational and the higher level is multidimensional matrix. This approach provides better flexibility.

There are other ways to implement OLAP, such as providing a dedicated SQL Server that provides special support for SQL queries for certain storage schemas (e.g., star, snowflake).

OLAP tools are problem-specific online data access and analysis. It analyzes, queries, and reports on data through a multi-dimensional approach. Dimensions are the specific perspectives from which people look at data. For example, when an organization considers the sales of a product, it usually looks at the sales of the product in depth from different perspectives of time, region and product. Here, time, region, and product are dimensions. And the different combinations of these dimensions and the metrics examined constitute a multidimensional array that is the basis of OLAP analysis, which can be formalized as (dimension 1, dimension 2, ......, dimension n, metrics), such as (region, time, product, sales). Multi-dimensional analysis refers to the data organized in a multi-dimensional form to take a slice (Slice), cut (Dice), drill down (Drill-down and Roll-up), rotate (Pivot) and other various analytical actions, in order to analyze the data, so that the user can be from multiple perspectives, multi-side of the data in the database, so as to in-depth understanding of the information contained in the data.

Based on the different ways of comprehensive data organization, the current common OLAP mainly based on multi-dimensional database MOLAP and relational database based on ROLAP two kinds of MOLAP is a multi-dimensional way of organizing and storing data, ROLAP is to use the existing relational database technology to simulate multi-dimensional data. In data warehouse applications, OLAP applications are generally the front-end tools for data warehouse applications, while OLAP tools can also be used in conjunction with data mining tools, statistical analysis tools, to enhance decision-making and analysis functions.