redshift data warehouse example

modeling. Here's a few details about designing a data warehouse: (This can be boring) Normalization/anti-normalization The purpose of this operation is to reduce the system's I/O operation time. The specific approach can be summed up in two sentences: To reduce the time spent on I/O operations, to merge some tables (normalize), or to introduce redundant data (re

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are the definitions of these things, such as time, department, and dimension tables. The fact table contains the data to be queried and the dimension ID. In terms of concept, it is a bit obscure. Any technology serves applications, which can be easily understood in combination with applications. Take banking as an example. The database is the data platform of th

process has been a great controversy, that is, the data warehouse need not to store the details of data, one side of the view is the Data Warehouse oriented analysis, so long as the storage of specific needs of multidimensional analysis model; the other side of the view is

of measurement is the whole basis of judging the cube. If the cube is about the number of items sold, the unit of measure is the count of the number of items sold. To repeat the grid example, the unit of measurement is the number you find in the grid cell. Figure 1: The above illustration shows the structure of the two-dimensional cube. In this example, the product and region are dimensions, and broccol

resources at this stage. On the contrary, if you simplify it, you will regret it later. So even if the system is slow, do not simplify the process of clearing old data. 6. Avoid granularity and partition issuesThere are two major data storage problems in the data warehouse design process. The first is how to locate a

simplify it, you will regret it later. So even if the system is slow, do not simplify the process of clearing old data. 　　6. Avoid granularity and partition issues There are two major data storage problems in the data warehouse design process. The first is how to locate an appropriate level of granularity for the conv

. Subject fields can have more than one theme, and themes can be divided into more sub-themes, while entities are the smallest units that cannot be divided. The relationship of subject fields, themes, and entities is as follows:V. Controversy over the subject areaYou've seen another way to define a topic field: "The Subject field is the boundary of the subject that is determined after the analysis of a topic." The relevant content is as follows:A subject field is the boundary of a topic that is

. Subject fields can have more than one theme, and themes can be divided into more sub-themes, while entities are the smallest units that cannot be divided. The relationship of subject fields, themes, and entities is as follows:V. Controversy over the subject areaYou've seen another way to define a topic field: "The Subject field is the boundary of the subject that is determined after the analysis of a topic." The relevant content is as follows:A subject field is the boundary of a topic that is

in multiple legacy locations, and in some cases even relatively small data sources, such as Microsoft Excel spreadsheets, can be stored on file shares. Clean up data When you merge OLTP data into a data warehouse, you provide an opportunity to clean up your

regularly archive stale data. For example, a click Stream Data Warehouse might keep detailed data online for only 3-4 months. Other common rules may be to keep online for 13 months, 37 months, or 10, and archive and delete old data