Power BI can connect to SSAS data in two modes: Import mode and DirectQuery mode. Import mode pulls all the data from SSAS into Power BI for local caching, but changes in SSAS won't be reflected automatically. DirectQuery mode sends queries directly to SSAS in real-time, allowing users to see changes immediately without refreshes, but it can impact performance for large or complex queries. The choice of mode depends on factors like data size, refresh needs, and query complexity.

3. Dimensional data Modeling
Star Schema
https://www.youtube.com/watch?v=patBYUGwsHE

5. • s

8. Dimension

42. FACT Table

69. SSAS

70. Import Mode
By default, when connecting Power BI to SSAS, the default mode is often "Import" mode. This mode imports data
from SSAS into Power BI, where the data resides in the Power BI file (PBIX).
If there are changes or updates in the SSAS data source (such as data updates, model modifications, cube
processing, etc.), these changes won't automatically propagate to the Power BI model in Import mode.
To reflect changes made in SSAS, you need to process the SSAS model (for example, processing cubes or tabular
models) and redeploy the updated model to the SSAS server or Scheduled Refresh or Manual Refresh
DirectQuery Mode
When you switch to "DirectQuery" mode, Power BI doesn't import data from SSAS; instead, it sends queries
directly to the SSAS data source in real-time.
In DirectQuery mode, Power BI sends queries to the SSAS data source each time a user interacts with a report or
visualization. This means Power BI does not cache the data locally; it retrieves the data directly from SSAS on-
demand.
With DirectQuery, users get real-time or near-real-time data as it's stored in the SSAS database. Any updates or
changes made in the SSAS data source are reflected immediately in the Power BI report without requiring manual
refreshing.

71. •DirectQuery mode can have performance implications, especially for complex queries or large datasets, as it
may result in increased query response times due to the direct connection overhead.
•Certain Power BI features and DAX functions might not be fully supported in DirectQuery mode compared to
Import mode due to differences in query capabilities

72. If you use just BI without Analysis service centralized repository this happened

79. https://www.youtube.com/watch?v=eNxbMwUGl1g

80. https://www.youtube.com/watch?v=nxUlImAYWBc

81. The 7 Stages to Power BI Dashboards

83. Vertipaq

84. VertiPaq is a data storage technology developed by Microsoft that is used in their business intelligence
and analytics products, particularly within the SQL Server Analysis Services (SSAS) and Power BI tools.
It's part of the xVelocity in-memory analytics engine

91. OLAP

92. CUBE an DAX will be used
No data CUBE will be used

94. SSAS

95. SQL Server Analysis Services, a Microsoft tool used for online analytical processing (OLAP) and data mining
functionalities. It's part of the Microsoft SQL Server suite, designed to help users analyze and make sense of
large volumes of data for business intelligence purposes. Here are some key functionalities and features of
SSAS:
OLAP Cubes: SSAS allows the creation of multidimensional data models known as cubes. These cubes
organize data into dimensions (such as time, geography, product) and measures (such as sales, profit) to
facilitate faster querying and analysis.
Data Mining: It includes data mining algorithms to discover patterns and trends within the data, aiding
in predictive analysis, customer segmentation, and decision-making processes.
Multidimensional and Tabular Models: SSAS supports both multidimensional models (cubes) and tabular
models. Multidimensional models organize data in multiple dimensions, while tabular models use relational
models for easier querying.
Querying and Reporting: Users can query SSAS cubes using languages like MDX (Multidimensional
Expressions) for multidimensional models or DAX (Data Analysis Expressions) for tabular models. These
cubes can be used as a data source for various reporting tools like Microsoft Excel, Power BI, etc.
Scalability and Performance: It's designed to handle large volumes of data efficiently, with features for
optimization and performance tuning.