SSAS is transferred to another person's diary 2

1. create a data source (required) and set the connection to the Data Warehouse that may be used in the project. Generally, the Data Warehouse is configured with identity simulation. If the warehouse and SSAS are a server, you can directly use the service account. Otherwise, you can specify a specific user name and password. 2. create a data source view (required) which includes some dimension tables and

1. create a data source (required) and set the connection to the Data Warehouse that may be used in the project. Generally, the Data Warehouse is configured with identity simulation. If the warehouse and SSAS are a server, you can directly use the service account. Otherwise, you can specify a specific user name and password. 2. create a data source view (required) which includes some dimension tables and

1. Create a data source (required)
Set the connection of data warehouses that may be used in the project.
Usually a data warehouse
Note: identity simulation settings
If the repository and SSAS are a server, you can directly use the service account
Otherwise, you can specify a specific user name and password.

2. Create a data source view (required)
Map some dimension tables and fact tables that may be used in the project into a view.
Practice: select a fact table and then "add related table"

You can also perform some additional operations.
Modify friendly name
Add name calculation (based on the original database fields, and then generate some fields)
Modify the Link (if there is no link in the repository, you can establish a link here)
If there are still many tables, you can create different views to simplify the process.
3. Create a multi-dimensional dataset
Two main models:
Automatic Generation Based on Repository (most convenient)
Select data source view
Select fact tables and dimension tables

Key points to be modified
Multi-dimensional dataset Structure
Modify the aggregation mode of metric values
Modify the measurement value format

Dimension usage (relationship between dimensions and measurements)
Conventional: the relationship between dimensions and facts is a standard one-to-many relationship.
Fact relationship: the relationship between a degraded dimension (in some aspects to be analyzed, its data growth is basically close to the measurement value, which is not suitable for dimension tables) and a measurement value.
Multi-to-Multi-Relationship: an implementation of multi-to-Multi-relationship in a repository. Intermediate fact table required
Referenced relationship: the product of the snowflake architecture. Intermediate dimension required

Deployment: Send the definition to the server and create a multi-dimensional database
Processing: Read the data from the warehouse and pre-calculate the data according to the definition and dimension definition of the multi-dimensional dataset.

First modeling, and then reverse engineering to generate a warehouse

4. Understand the design of dimensions
Attribute of a dimension is a field in a dimension table.
Attribute values must be uniquely identified; otherwise, an aggregation error may occur.
Add a KeyColumn to make it a composite key.
Hierarchy
Convenient User Analysis
Provides better efficiency

Hide some low-level attributes

Attribute relationship
By default, all attributes are directly related to the dimension key column.
If a hierarchy is created, we recommend that you create a relationship between the hierarchy attributes so that there will be indirect relationships between some attributes and key columns.

5. advanced knowledge of multidimensional database design
5.1 computing
You can do three things.
Computing member: the member is calculated based on the aggregated value. Pay attention to the differences with name calculation. The fundamental difference is that named computation is for the row set (syntactically T-SQL), each row is calculated. Computing members are calculated based on the aggregated results. (The syntax is MDX). equivalent to a scalar function, a value is always returned.

Computing members are usually calculated based on metric values, which are finally classified into metric values.

Name set: You can also use the MDX script to define an expression. The returned value may be multiple. For example, we can define ten products that often need to be analyzed as a so-called "core product"

A command set is usually used to filter dimensions.

Script command: it can change the calculation result of MDX and even generate fictitious data.

5.3 KPI
Key performance indicators
Provides four expressions (MDX) to define four information.
Objective: To obtain the target indicator value through MDX Query
Current: MDX query returns the actual completion value
Status: usually compare the target and current values. The result is generally three values: 1, 0,-1.

Direction: usually compare the current value with the value of the previous period. The result is also 1, 0,-1.

5.4 action)
You want to provide other links, reports, and drilling in the analysis interface.
However, how to display the Action in the client program depends on our code for implementation (which has been implemented in Excel)

5.5 partition)
Plan data storage design. For large analytic databases, multiple partitions must be planned to improve processing speed and query efficiency.
After partitioning, You can simplify the processing. You can process a single partition, which greatly improves the processing speed.
If multiple partitions exist, you can perform operations in parallel during query.

First, change the binding method of the default partition from Table binding to query binding. In general, we use the time column as the condition to distinguish between Query conditions.
Create a new partition.

5.6 perspective
You can perform multiple pivoting on a cube. Each perspective is similar to a view.
5.7 Translation
Supports multiple language versions for one cube