Azure Analysis Services
Proven analytics engine
- Get started quickly without managing an infrastructure
- Scale resources to match your business needs
- Visualize your data using your favorite data visualization tool
- Govern, deploy, test, and deliver your BI solution with confidence
Get started quickly and scale with efficiency
Use Azure Resource Manager to create and deploy an Azure Analysis Services instance within seconds, and use backup restore to quickly move your existing models to Azure Analysis Services and take advantage of the scale, flexibility and management benefits of the cloud. Scale up, scale down, or pause the service and pay only for what you use.
Transform complex data into one version of the truth
Combine data from multiple sources into a single, trusted BI semantic model that’s easy to understand and use. Enable self-service and data discovery for business users by simplifying the view of data and its underlying structure.
Match performance to the speed of business
Reduce time-to-insights on large and complex datasets. Fast response times mean your BI solution can meet the needs of your business users and keep pace with your business. Connect to real-time operational data using DirectQuery and closely watch the pulse of your business.
Provide secured access anytime, from anywhere
Make sure only authorized users can access your data models, no matter where they are, with role-based security and Azure Active Directory support. With 99.9% availability, your users can access critical information when they need it.
Accelerate time to delivery
Release your BI solutions in a predictable and highly-secured way. Use the robust application lifecycle management capabilities to govern, deploy, test, and deliver your BI solution quickly and with confidence.
Develop in a familiar environment
Focus on solving business problems, not learning new skills, when you use the familiar, integrated development environment of Visual Studio. Easily deploy your existing SQL Server 2016 tabular models to the cloud.
Data model in Azure Analysis Services16-October-2019
Azure Analysis Services is a new preview service in Microsoft Azure where you can host semantic data models. Users in your organization can then connect to your data models using tools like Excel, Power BI and many others to create reports and perform ad-hoc data analysis.
To understand the value of Azure Analysis Services, imagine a scenario where you have data stored in a large database. You want to make that data available to your business users or customers so they can do their own analysis and build their own reports. To do this, one option would be to give those users access to that database. Of course, this option has several drawbacks. The design of that database, including the names of tables and columns may not be easy for a user to understand. They would need to know which tables to query, how those tables should be joined, and other business logic that needs to be applied to get the correct results. They would also need to know a query language like SQL to even get started. Most often this will lead to multiple users reporting the same metrics but with different results.
With Azure Analysis Services, you can encapsulate all the information needed into a semantic model which can be more easily queried by those users in an easy drag-and-drop experience. And you can ensure that all users will see a single version of the truth. Some of the metadata included in the semantic model includes; relationships between tables, friendly table/column names, descriptions, display folders, calculations and row level security.
Once your data is properly modeled for your users to consume, Azure Analysis Services offers additional features to enhance their querying experience. The biggest of which is the option to put the data in an in memory columnar cache which can accelerate queries to sub second performance. This not only improves the query experience but by hitting the cache also reduces the query load on your underlying database.
Ready to give it a try? Follow the steps in the rest of this blog post and you’ll see how easy it is.
- Server name: Type a unique name.
- Subscription: Select your subscription.
- Resource group: Select Create new, and then type a name for your new resource group.
- Location: This is the Azure datacenter location that hosts the server. Choose a location nearest you.
- Pricing tier: For our simple model, select D1. This is the smallest tier and great for getting started. The larger tiers are differentiated by how much cache and query processing units they have. Cache indicates how much data can be loaded into the cache after it has been compressed. Query processing units, or QPUs, are a sign of how many queries can be supported concurrently. Higher QPUs may mean better performance and allow for a higher concurrency of users.
Now that you’ve created a server, you can build your first model. In the next steps, you’ll use SQL Server Data Tools (SSDT) to create a data model and deploy it to your new server in Azure.
Sam Analytiks motivates, educates and proliferates data for any organisation as a non-profit partner. If you or any of your team member needs a help, surely our consultants will be glad to help you in any case.
Call us at +48-729473572 or email us at firstname.lastname@example.org
#data #datavisualisation #datamodelling #datamart #powerbi #sql #excel #powerquery #azure #azureanalysis