First, see the difference between OLAP and OLTP:
OLTP (Online transaction processing ): Online/online transaction processing. Typical OLTP operations are relatively simple, mainly to add, delete, modify and query data in the database, and the main body of the operation is generally the user or operator of the product.
OLAP (Online analytical processing): refers to online analytical processing. Draw something conclusive by analyzing the data in the database. For example, reports for bosses, user behavior statistics for market development, summary analysis results of different dimensions , etc. The main body of operation is generally team members such as operations, sales and marketing.
The amount of data processed by a single OLTP is relatively small, and the tables involved are very limited, generally only one or two tables. OLAP is to find out some regularity from a large amount of data. Aggregation methods such as count(), sum() and avg() are often used to understand the current situation and provide data support for future plans/decisions , so it is very common to join and summarize the data of multiple tables.
In order to express the difference in data volume and complexity from the OLTP database (database), the operation object of OLAP is generally called a data warehouse (data warehouse), referred to as a data warehouse. The data in the database warehouse often comes from multiple databases and corresponding business logs.
My understanding is: OPTP pays more attention to the consistency of transaction processing, such as adding, deleting, modifying and checking each batch of operations is performed within one transaction to avoid confusion. OLAP pays more attention to data integration and integrates data from different sources for analysis.
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Atomic indicators
Atomic indicators refer to measurement values based on business processes. As the name implies, they cannot be split.
Core functions: define the aggregation logic of indicators
Three elements: business process measurement value aggregation logic
Derived indicators
Derived indicators are based on the relationship of atomic indicators: derived indicators = atomic indicators + statistical cycle + business + statistical granularity
Example specific explanation:
derived indicators: the total amount of orders for mobile phone categories in the province in the last week
Atomic indicators: total orders (define the business process , metric value aggregation logic)
Statistical cycle: the last week (limiting the scope of statistics is a special business limitation)
business limitation: the category is mobile phone (limiting the statistical scope, equivalent to the where condition in SQL)
statistical granularity: province (defining the statistical granularity , equivalent to group by in SQL)
Derived indicators
Derived indicators are formed on the basis of one or more derived indicators through various logical operations.
Indicators such as ratios, proportions, etc.
