This SSAS blog may be a detailed introduction to SQL Server Analysis Services and is highly useful for beginners. SSAS may be a tool offered by Microsoft and an analytical processing engine in Business Intelligence. It allows the creation of a database that acts as an OLAP database or SSAS cube tutorial. It allows data analysis using different dimensions, the creation of cubes from data marts, data exploration in data warehouses, and data modelling, etc. SSAS may be a multi-dimensional OLAP server as well as an analytics engine that allows you to play with voluminous data easily.

These days demand for SSAS MDX professionals is increasing almost every passing day. Companies are hiring expert OLAP cube developers or SSAS cube tutorial at attractive salaries. If you furthermore may to get into the SSIS marketplace, then jowithin the SSAS online Training and improve your overall chances of getting hired by leading companies.

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History and Evolution of SSAS

OLAP features were primary included in SQL Server 7, but later it was purchased by an Israel Company named Panorama.
In a short time, SSAS became the most employed OLAP engines because it was introduced as the part of SQL Server.
After some time, SSAS was completely renovated with the release of SQL Server 2005 within the market.
The latest version of SSAS offers a feature of cubes with the Scope statement. You will learn about important SSAS terminologies in future sections.
SSAS 2008 and SSAS 2012 are mainly concerned with scalability and query performance.
In Microsoft excel 2012, one new feature was added Power Pivot that employed a local instance of SSAS to increase the overall query performance.

Key Features of SSAS

The important features of SSAS are highlighted below:

Features	Description
Data Analysis	Facts aggregation results in shorter query response time.
Speed	The multi-dimensional analysis may be allowed through cubes
Automatic displays	It can link and display reports automatically.
Supreme Data Models	Capable of generating good data models for better analysmay be and business reporting.

The other important features of SSAS can be given as:

It ensures data integrity through automatic data backups.
There is no additional requirement of software; a normal internet connection and a web browser are sufficient.
The organizational data is kept secure through powerful SSAS solutions.
The tool can be accessed anytime, anywhere with the help of an internet connection.

SSAS Architecture

SSAS ETL tool has a three-tier architecture that consists of RDBMS, SSAS, and the client. Let us discuss each of them in brief below.

RDBMS: Relational Database Management System: RDBMS allows the collection of data from multiple sources like excel sheet, database, files using an ETL tool like SSAS.
SSAS: SQL Server Analysis Services: Aggregated data from RDBMS is pushed into SSAS cubes using analysis service projects. The cube will generate an analysis database further; once the database is ready, it can be employed for multiple purposes.
Client: Clients may access data using dashboards, portals, scorecards, etc.

Important SSAS Terminologies

Data Source: It is similar to a connection string that can be employed to establish a connection between the analysis database and the RDBMS.
Data Source View: it may be a logical model of the data source.
Cube: It is the basic unit of storage. It may be a collection of data that has been aggregated to allow queries to return the data quickly.
OLAP: it is made up of data cubes that contain measures and dimensions. It includes almost all members in a hierarchical relationship. In simple terms, it a specific set of rules that helps you in determining how specific cells are computed in a specific sparse cube, and its measure values are rolled up inside those hierarchies.
Dimensions: it offers the context surrounding a business process event. In easy words, it offers who, what, and where of a fact statement. For example, within the case of sales fact tables, dimensions could be:
- Who: Customer?
- Where: Location?
- What: Product name?

In brief, you can say that dimension may be a window to view the information in facts.

Level: It may be a type of summary that can be retrieved from the single dimension is called the level.
Fact Table: It is the most important part of a dimensional table. It contains measurements, facts, foreign keys for the dimensional table. The best example of the Fact table is Payroll Operations.
Dimensional Table: A dimensional table contains dimensions of a fact. They can be joined to a fact table using a foreign key. Dimension tables are denormalized tables offer characteristics of facts with the help of some attributes. It defines limits for dimensions that contain one or more hierarchical relationships.
Measure: There are one or more tables for each fact table that should be analyzed properly.
Schema: A database schema supports the database management system and its structure can be given in a formal language. The term schema means the organization of the data as a blueprint in such a way how the database is constructed.
MDX: It may be a query language to retrieve the data from multidimensional tables. We will discuss MDX in detail in future sections.

Types of Models in SSAS

There are two popular models in SSAS: Multi-dimensional and Tabular Data Model.

Multi-dimensional Data Model: The multidimensional data model is made up of a data cube. It may be a group of dimensions that allows you to query the value of cells using cubes and dimensions. It defines a set of rules that measures values rolled up within hierarchies and how specific values are computed in a sparse cube.
Tabular Data Model: It organizes data into related tables that are not designated as tables and facts and development time is very less with tabular data models because related tables can serve both roles.

Tabular vs. Multidimensional Data Models

Parameters	Tabular Data Model	Multidimensional Data Model
Memory	In-memory storage	File-based Storage
Structure	Loose structure	Rigid Structure
Best Features	There is no need to move data from the source.	It works just the best when data is stored into a star schema.
Type of Models	Relational and DAX models	Dimensional and MDX models
Complexity	Simple	Complex
Size	Small in Size	Larger in Size

SSAS vs. PowerPivot

Features	SSAS	PowerPivot
About	SSAS is corporate BI	PowerPivot is Self-service BI.
Deployment	Deploy to SSAS	Deploy to SharePoint
USES	Visual Studio Projects	Excel Projects
Size	Limited Memory Size	Capacity limited to 2GB only
Partition Support	SSAS supports partitioning	PowerPivot does not support partitioning
Query Types	DirectQuery and Vertipaq	Only Vertipaq queries are allowed
Admin tools	Server admin tools	Excel and Share admin tools
Security	Dynamic Security Levels	Workbook File Security

Pros and Cons of Using SSAS:

Benefits:

The resource contention can be avoided with the source system.
It may be an ideal tool for numerical
The tool enables the discovery of data patterns that isn’t apparent immediately using data mining features.
It offers an integrated and unified view of business data reporting, analysis of KPIs (Key Performance Indicators), and scorecards.
It offers online analytical process feature (OLAP) from different data sources.
It allows users to analyze data using multiple tools like SSRS or Excel.

Drawbacks:

Once you’ve decided on a data model either Tabular or Multidimensional, you cannot change it to other versions.
It isn’t possible to merge data between Tabular or multidimensional cubes.
The tabular data model may be risky if project requirements change mid-way through the project.

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Best Practices of Using SSAS:

Aggregations should be defined well in a proper way.
Optimize cubes and measure group design too.
Use partition methods
MDX should be written effectively.
The query engine cache should be employed efficiently.
Try to avoid the tabular data model until it isn’t required.

So, this brings us to the end of blog. This Tecklearn ‘Overview of SSAS and its Architecture’ blog helps you with commonly asked questions if you are looking out for a job in Microsoft BI. If you wish to learn Microsoft BI and build a career in Business Intelligence domain, then check out our interactive, Microsoft SSAS Course Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

https://www.tecklearn.com/course/microsoft-ssas-course-content/

Microsoft SSAS Course Training

About the Course

Tecklearn’s Microsoft SQL Server Analysis Services (SSAS) training SQL Server Analysis Services (SSAS) is a part of Microsoft Business Intelligence (MSBI), used for developing Online Analytical Processing (OLAP) solutions. This course gives a brief knowledge of developing Analysis, services, databases from the dimension modelling process to the development of cubes and dimensions. You’ll also learn how to deploy and process the Analysis Services database.

Why Should you take Microsoft SSAS Training?

The Average salary for a SQL SSAS Developer is $103,987 per annum – ZipRecruiter.com
Wells Fargo, JPMorgan Chase, Northrop Grumman, DaVita and many other MNC’s are using Microsoft SSAS.
Microsoft BI is a Leader in 2018 Gartner Magic Quadrant for Business Intelligence & Analytics Platforms (9th Consecutive Year).

What you will Learn in this Course?

Introduction to MSBI and Data Warehousing

Fundamentals of Data Warehousing
Concepts of Dimensions, Measures, Metadata and Schemas
Data Marts and Design approaches
Normalization and Denormalization and Schema types
Concepts of Online Analytical Processing and Transactional Processing
OLAP Cube
Explain ETL process and various tasks involved in it
Slowly Changing Dimensions Types
Business Intelligence Concepts
Working of BI with data-warehouse

Introduction to SSIS

Understanding of the MSBI Architecture
Import and Export wizard
Understand SSIS Architecture
Control Flow and its Components (Tasks, Containers and Precedence Constraints)
Data Flow and its Components (Source and Destination Connections and types of Transformations)
System Variables and User-defined variables
Scenarios by combining Control Flow and Data Flow components
Hands On

Transformations and Use-Cases

Data Conversion transformation
Multicast transformation
Union all transformation
Conditional Split Transformation
Merge and Merge Join Transformation
Lookup transformation
Cached Lookup transformation
Foreach loop and use-cases
Bulk-insert task
Archival process using dynamic variables and FST
Advancing Execute SQL Task with Object return type
Types of Outputs usage
Hands On

Slowly Changing Dimensions

Understanding data that slowly changes over time
Learning the process of how new data is written over old data
Detail explanation of three types of SCDs –Type1, Type2 and Type3, and their differences
Hands On

Overview of Fuzzy Look-up Transformation and Lookup and Term Extraction

Concept of Fuzzy matching
How Fuzzy Lookup Transformation varies from Lookup Transformation
Hands On

Concepts of Logging & Configuration

Learning about error rows configuration
Package logging
Defining package configuration
Understanding constraints and event handlers
Hands On

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