SQL Server Analysis Services Architecture

This document provides a comprehensive overview of the architecture of SQL Server Analysis Services (SSAS), covering its core components, data flow, and deployment models.

Core Components

SQL Server Analysis Services is a business intelligence platform that provides online analytical processing (OLAP) and data mining functionality. Its architecture is designed to efficiently process and analyze large volumes of data from various sources.

1. Server Instances

SSAS can be installed as a standalone instance or as part of a SQL Server instance. Each SSAS instance hosts databases and provides services for querying and processing data.

2. Analysis Services Engine

The core of SSAS is its multidimensional OLAP engine. This engine is responsible for:

Managing cubes, dimensions, and measures.
Processing and storing data in a highly optimized format.
Executing complex analytical queries using MDX (Multidimensional Expressions) or DAX (Data Analysis Expressions).

3. Processing Engine

This component handles the extraction, transformation, and loading (ETL) of data from various data sources into SSAS. It supports incremental and full processing of data to keep the analytical models up-to-date.

4. XMLA (XML for Analysis) Interface

XMLA is the standard protocol used for communicating with SSAS. Client applications, management tools, and programming languages use XMLA to send commands and receive data from the SSAS server.

5. Client Tools

Various client tools interact with SSAS:

SQL Server Management Studio (SSMS): For managing SSAS instances, databases, and objects.
SQL Server Data Tools (SSDT): For designing and developing SSAS models (both multidimensional and tabular).
Reporting Services, Power BI, Excel, and other BI tools: For connecting to SSAS and visualizing data.

Data Flow and Processing

The typical data flow in SSAS involves the following steps:

Data Source Connection: SSAS connects to relational databases, data warehouses, or other supported data sources.
Data Extraction: Data is extracted from the source system.
Data Transformation: Data is cleaned, aggregated, and transformed as needed.
Data Loading: Transformed data is loaded into SSAS storage.
Cube/Tabular Model Processing: The SSAS engine processes the data to build the multidimensional cubes or tabular models. This involves creating aggregations and optimizing data structures for fast querying.
Query Execution: Client applications send queries (MDX/DAX) to the SSAS server.
Result Return: SSAS processes the query against the optimized data structures and returns the results to the client.

Deployment Models

SSAS can be deployed in two primary modes:

1. Multidimensional Mode

This is the traditional SSAS mode, focused on building cubes with dimensions, measures, hierarchies, and defined relationships. It's ideal for complex analytical scenarios requiring rich OLAP functionality.

Multidimensional SSAS Architecture Diagram

Figure 1: Conceptual diagram of SQL Server Analysis Services Multidimensional Mode.

2. Tabular Mode

Introduced in SQL Server 2012, Tabular mode uses an in-memory columnar database engine (VertiPaq) that offers high performance and a simpler data modeling approach, often leveraging DAX for queries. It's well-suited for self-service BI and scenarios where rapid query performance is paramount.

Figure 2: Conceptual diagram of SQL Server Analysis Services Tabular Mode.

Key Architectural Considerations

Scalability: SSAS is designed to scale to handle large datasets and high concurrency.
Performance: Aggregations, partitioning, and the in-memory engine (in tabular mode) are key to achieving high query performance.
Security: Role-based security can be implemented at the server, database, cube, dimension, and cell levels.
High Availability: SSAS supports mirroring and clustering for failover scenarios.

Important Note

The specific components and their interactions may vary slightly between different versions of SQL Server Analysis Services. Always refer to the documentation for your specific version for the most accurate information.

Pro Tip

Understanding the underlying architecture of SSAS is crucial for optimizing performance, troubleshooting issues, and designing effective BI solutions.

Example: MDX Query Structure

Here's a simplified example of an MDX query structure:


SELECT
    {[Measures].[Internet Sales Amount]} ON COLUMNS,
    {[Date].[Calendar Year].MEMBERS} ON ROWS
FROM
    [Adventure Works]
WHERE
    ([Product].[Category].[Bikes])

Example: DAX Query Structure

And a similar query in DAX:


EVALUATE
SUMMARIZECOLUMNS (
    'Product'[Category],
    'Date'[Calendar Year],
    "Total Sales", SUM ( 'InternetSales'[SalesAmount] )
)
WHERE
    'Product'[Category] = "Bikes"