Azure‌ ‌Analysis‌ Services

What is Azure Analysis Service?

Before going into Azure analysis services, let's get an idea about analysis services. Analysis services are referred to as an analytical data engine that is used by the business organizations to ensure that the decisions are taken precisely and also help in performing business analytics. Azure analysis services deal with semantic data modelling capabilities in different areas like Business intelligence, data analysis, reporting services, and other visualization tools.

Azure analysis service is a platform as a service that is fully managed and provides the data models related to the enterprise within the cloud. Azure analysis service utilizes the advanced mashup and modelling features that will help in combining the data from multiple resources,define the metrics, and secure the data in a single tabular, semantic data model that is trustworthy. This data model is feasible, faster, and provides an easier way for the users to perform the analysis, ad hoc data analysis using different tools like excel and power BI. 

Azure analysis services serve resources, support the tabular models at higher compatibility levels. It helps organizations in making precise decisions and meets the deliverables on time. It also helps in running the analysis of the business that makes a vital role in the business aspects. 

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Features of Azure Analysis Services:

Every tool or platform is developed with its own set of features. In the same way, Azure analysis services are also designed and implemented with a particular set of features that is helpful for business organizations. Let us have a full view of the features of analysis services.

Cost control:

Azure analysis services have come up with the feature wherein you are allowed to pause the services whenever required. It provides you with the flexibility to pay only for the time that you are really in need and using it. In short, it means that you can pay only when you need the database to be online. It also helps or allows us to change the service tier as and when required. This means that Azure analysis services allow the changes dynamically to increase the RAM and processing power to speed up the refresh, reducing the performance. But whenever you want to save some money with low workloads it is the best option that provides cost control.

Memory:

The memory feature in Azure analysis services that is included in the service tier has both the online database and also the temporary structures that are required to process the database. These parameters can be compared to the RAM of the server. You also need to know that there should be enough RAM to store the compressed data and even any other memory structure of analysis services which include the query cache and processing buffers. Azure analysis services have a memory that has a large number of users to be connected at the same time and also includes increasing memory demand.

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Scale-out control:

Azure analysis services have come up with a feature called scale-out control which helps in providing control over several query replicas to manage the different number of users who are connected simultaneously. The implementation of the scale-out control in SQL server analysis is not that easy and will need administrative efforts.

Segment size control:

Segment size control consists of a property called DefaultSegmentRowSize property which is used in the massive data models. It is essential, and it is the same as the property that is available in SQL server analysis services. Azure analysis services offer 8 million rows and maintain the right balance. It will help in reducing the segment size, which will reduce the memory pressure at the processing time, especially for the tables which include a large number of columns. However, you also need to know that a small number can also affect the performance during the query time for the table that has many rows, at least a million rows. We see that many of the companies are reducing this setting to avoid the out of memory errors during processing. This is a good idea for the tables that include 15 to 20 million rows.

Add a sample model from the portal:

The sample model is a completed version of the adventure works internet sales 1200 sample data model. The sample model is generally used to test the model management, connect with the tools and client applications, and to query the model data. Let us have a quick review of the steps to be followed to add a sample model from the portal.

Sign in to Azure portal:

Step 1: Add a sample model
1. You will need to go to the Server overview and click on the new model.
2. In the new model, choose a data source, verify the sample data if selected, and then click on the add option.
3. In the audio, you will need to verify whether the AdventureWorks sample model is added or not.

Step 2: Clean up resources
1. In the SQL server management studio (SSMS), navigate to the Object Explorer, click on connect followed by analysis services.
2. Connect to server based on the server name, and then in the authentication part, choose active directory - universal with MFA support, and then click on connect.
3. In the Object Explorer tab, right-click on the adventure work sample database and then click on delete.

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Configure the server administrator and user roles:

Below are the steps to be followed to configure the server administrator and user roles.

Step-1: Get the server name
1. In the Azure portal > server > Overview > Server name, copy the server name.
Step-2: Connect in SSMS
1. In SSMS, navigate to object Explorer and then click on connect followed by analysis services.
2. In the link to server dialogue box in a server name in the server name that you have copied from the portal. In the authentication, tab chooses the active directory universal with MFA support, enter your user account, and then press connect.
3. In the Object Explorer expand to see the server objects. Right-click to review the server properties.

Step-3: Add the user account to the server
1. In the Object Explorer, right-click your server name and then click on the properties administrator role.
2. In the analysis server properties window, click on the server followed by add.
3. In the Select, a user or group window, enter a group or the user account in your Azure AD and then click on add.
4. Click on Ok close the analysis server properties.

Step-4: Add user to the model database administrator role
1. In the Object Explorer expand databases > adventure works > Roles.
2. Right-click the internet sales administrator and then click on the script role as > CREATE OR REPLACE To > New Query Editor Window.
3. In the XMLA query, change the value for member name to a user or a group account in your Azure AD.
4. Press on the function key F5 to execute this script.

Step 5: Add a new model database role and add the user or a group
1. In the Object Explorer, right-click on AdventureWorks and then click new query > XMLA.
2. Copy and paste the TMSL script into the query editor.
3. Change the member name to a different one as: "memberName": "globalsales@adventureworks.com" object value you a user or group account in your Azure AD.
4. Press the function key f5 to execute the script.

Step-6 verify your changes.
1. In the Object Explorer, click on the server name and then click on refresh or F5.
2. Expand the databases> AdventureWorks > Roles. Verify the user account and the role that you have added in the previous task if they are appearing or not.

Connect with Power BI desktop:

Below are the steps to be followed to connect with Power BI desktop.

Step-1: Get the server name.
1. In the Azure portal, navigate to the server emotional > Overview > Server name, copy the server name.
Step 2 : Connect with Power BI desktop.
1. In the Power BI desktop, click on Get Data > Azure > Azure Analysis Services database.
2. In the server, paste the server name and then in the database enter Adventure works and then click ok.
3. When prompted, enter the credentials. The account you enter must have at least read permissions for the AdventureWorks sample model database.
4. In visualizations select clustered bar charts and then click on format followed by a turn on data labels.
5. In FIELDS > Internet Sales table, select Internet Sales Total and Margin measures. In the Product Category table, select Product Category Name.

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Advantages of Azure analysis services:

Azure analysis services have come up with different advantages that are utilized by most of the organizations. Let us have a quick review of the Azure analysis services advantages.

Server creation:

Azure Analysis services have come up with the flexibility for the developers to create a server within seconds. This is done by choosing the service tiers and the developer. Every tier comes up with the fixed capacity in terms of model cache and query processing unit. The developer tier provides its ability to support up to 3GB model cache and is the largest tier that supports up to 100 GB.

Pause and resume the server at any time:

Azure Analysis services have come up with the feature of pausing and resuming the server at any point in time. When the server is paused, there is no charge incurred for that. It also allows the administrators the ability to scale up and down a server between the standard tiers. Using Azure analysis services, the administrators are utilizing the opportunity and saving the operating costs by pausing and resuming the server as and when required.

Performance:

I think we have got an idea about how our Azure analysis services have put up its performance in the current times. Business organizations are utilizing the opportunity and striving hard to ensure that the business needs and deliverables are met at the right point of time. Performance is one of the essential aspects that every organization would consider and analysis services are away about the Expectations. Standard tiers offer dedicated capacity for predictable performance and are recommended for the production workloads the developer tier is highly recommended for development proof of concept and testing workloads.

Azure active directory:

In Azure analysis services, the administrators and Developers can use the active directory to manage user identity and role-based security for their models.

Compatibility with SQL server analysis services:

Flexibility with tools: Azure analysis services allows the developers to make use of the SQL server data tools in Visual Studio for deploying and creating the models to the service. It also allows the administrators to manage the models using SQL server management studio and investigate issues using the SQL server profiler. This flexibility has brought up an excellent view of the business and helps in running the analysis of the company in the right way.

Tabular modelling:

Azure Analysis services provide support to the business organizations by utilizing the tabular models. It offers extensive support for multidimensional models which would be a future release based on customer demand.

Conclusion:
Azure analysis services are one of the topmost platforms as a service which is delivering the business requirements and leading to the growth of the organization in a short period. I would recommend using Azure analysis services to perform the analytics related to business and utilize it in all aspects in the coming future. I think these feature releases were more significant and got down the limitations as well. I hope you have gained an understanding of Azure Analysis Services. I would recommend getting trained and certified in Azure and achieving a prosperous career.

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