Real-time data warehouse and data warehouse

Real-time data warehouse and data warehouse
Share an example of a real-time data warehouse.
The customer is a municipal Tobacco Company and needs to analyze the cigarette sales data in real time. About 0.1 million pieces of data are collected every day, which occurs within four hours.
Our solution is:
1. The dimension table information is processed every night (the customer will not maintain the basic File Content During the smoke setting process on the Day );
2. historical fact table data is processed every night;
3. The smoke data of the current day is used as a view to query the data of the business system. Only the data of the current day is queried (the query time is about 2-3 seconds );
4. Combine the historical data and the current-day data into another view to display the data;
5. Some content to be calculated (such as sales gross profit) is implemented in front-end tools.
Differences between real-time data warehouses and traditional Data Warehouses

Traditional data warehouses often have data usage delays. The most common is T + 1.

That is to say, the data must pass through one day to run the batch from generation to use. The data summary result can only be a daily report.

Real-time Data Warehouse solves the technical latency of data usage, and can obtain the most accurate analysis results at any time point.

Taking airlines as an example, we can create value positioning based on real-time information about customer preferences and needs, and use this information to provide targeted services.

What is real-time data mining?

With the development of information technology, data warehouse technology has been widely used and has produced huge economic benefits. In traditional data warehouse systems, the aggregation and analysis of historical data can provide strategic decision-making support for enterprises such as what marketing strategy will be adopted next year. However, with the continuous development of customer requirements, enterprises are increasingly hoping that data warehouses can provide real-time tactical decision-making services for frontline market personnel while supporting strategic decisions, such as real-time marketing and personalized services. This kind of data warehouse serving both strategic and tactical decisions is called real-time active data warehouse (RTADW ).