By: Amira Bedhiafi | Updated: 2023-07-11 | Comments | Related: > Azure
Problem
Analyzing data is an essential part of decision-making for many companies. Extracting meaningful insights from large amounts of data is vital to stay competitive in today's market. However, data analysis can be a complex and time-consuming task, often requiring specialized tools to be effective.
In this article, we'll explore the features and benefits of Azure Analysis Services, its architecture, key components, and languages used for development.
Solution
Suppose that a financial services firm wants to analyze its portfolio of investments to gain insights into investment performance, risk exposure, and market trends. To create a comprehensive view of their investment portfolio, they want to consolidate data from various sources, including market data providers, trading platforms, and internal databases.
The solution must provide the scalability and performance needed to handle large volumes of data and complex calculations. It must offer advanced security features to protect sensitive financial data and ensure compliance with regulatory requirements.
The firm can use Azure Analysis Services to create a data model that integrates data from multiple sources and provides a unified view of its portfolio. The data model can include calculations and measures that allow the firm to analyze the portfolio's performance, risk, and exposure to various market factors.
What is Azure Analysis Services?
Azure Analysis Services is a PaaS from Microsoft that enables organizations to analyze large amounts of data using data analysis models. It provides an end-to-end solution for creating, deploying, and managing data analysis models and reports to share with users.
By using Azure Analysis Services, organizations can benefit from a highly scalable, cost-effective, and easy-to-use data processing solution without managing their data analytics infrastructure.
The team behind Azure Analysis Services profoundly understands the challenges organizations face when managing and analyzing data at a critical scale. They used this knowledge to create a powerful, flexible, and user-friendly platform that allows companies to gain insights and make data-driven decisions quickly.
Thanks to all these efforts, Azure Analysis Services has become one of Microsoft's leading products in the global data analytics market. As a cloud-based data analytics service, it competes with other popular products such as AWS Athena, Google BigQuery, and Snowflake.
According to ratings from Gartner, a technology research and consulting firm, Azure Analysis Services is considered a leader in cloud analytics and business intelligence platforms. In its 2022 Magic Quadrant for Cloud Analytics and Business Intelligence Platforms, Gartner positioned Microsoft as a leader for the fourth consecutive year, highlighting its strong integration with other Microsoft data services.
Azure Analysis Services is also recognized for its compatibility with data visualization tools such as Power BI, Excel, and Tableau, widely used in enterprises worldwide to create reports and dashboards. This compatibility makes it easier to use Azure Analysis Services in existing environments where these tools are already in place.
Components of Azure Analysis Services
The main components of Azure Analysis Services help businesses efficiently analyze large amounts of data and make informed decisions. These components include:
- Data models: Data models are the foundation of Azure Analysis Services. They are used to represent data that is stored in data sources. Azure Analysis Services data models can be multidimensional or tabular. Multidimensional models are based on data cubes, while tabular models are based on tables.
- Analysis server: The analysis server is a virtual machine instance that runs the Azure Analysis Services service. It is used to process data requests and provide results to clients. The Analysis Server can be configured to meet business performance, capacity, and cost needs.
- Model building tools: Azure Analysis Services offers several tools to help users build data models. Users can use Visual Studio, Power BI Desktop, or SQL Server Data Tools to create multidimensional or tabular data models.
- Data connectors: Azure Analysis Services is compatible with various data sources, including Azure SQL Database, SQL Server, Oracle, Teradata, Excel, SharePoint, and many more. Data connectors allow users to connect Azure Analysis Services to their data sources to extract and analyze data.
- User interface: Azure Analysis Services can be used through multiple user interfaces, including Power BI, Excel, and custom applications. Users can use these interfaces to access data, create reports and dashboards, and perform ad hoc analysis.
Languages Used in Azure Analysis Services
Azure Analysis Services mainly uses two languages for the development of data models:
- MDX (Multidimensional Expressions): MDX is a query language for multidimensional models. It is used to query data cubes and extract analysis results. MDX is similar to SQL but is specifically designed for multidimensional data.
- DAX (Data Analysis Expressions): DAX is a formula language for tabular models. It is used to create calculations and measurements for data tables. DAX is similar to Excel because it uses formula functions to perform calculations.
Additionally, Azure Analysis Services supports other languages for development tasks, such as:
- T-SQL (Transact-SQL): T-SQL is used to create queries to extract data from SQL Server data sources.
- XMLA (XML for Analysis): XMLA is a query language for multidimensional and tabular data. It sends queries and commands to an analysis server like Azure Analysis Services.
- C#: C# is a programming language for developing .NET applications and services to develop Azure Analysis Services extensions.
Most Common Use Cases of Azure Analysis Services
Azure Analysis Services can be used for a variety of use cases, ranging from operational data analysis to business reporting:
- Financial analysis: Azure Analysis Services can be used to create advanced financial data models for functional data analysis. Financial data can be extracted from different sources such as Excel spreadsheets, SQL Server databases, or ERP systems. Financial data models can be created using DAX or MDX, providing multidimensional and tabular data analysis.
- Sales analytics: Businesses can use Azure Analysis Services to build data models for analyzing sales and business performance. Data models can include customer, product, sales, and profit data collected from different sources such as CRM systems, SQL Server databases, or flat files. Data models can be created using DAX or MDX, enabling multidimensional and tabular sales analysis.
- Market data analysis: Azure Analysis Services can create data models for market data analysis. Data models may include competitor data, market trends, sales forecasts, and market statistics collected from different sources, such as online data sources, APIs, and SQL Server databases.
- Supply chain analysis: Companies can use Azure Analysis Services to create data models for supply chain analysis. Data models can include vendor, cost, run time, delivery time, and vendor performance data collected from different sources such as ERP systems, SQL Server databases, or flat files.
Next Steps
Related topics:
About the author
Amira Bedhiafi utilizes Azure Analysis Services to harness the power of scalable, cloud-based OLAP cubes for advanced data analysis and multidimensional modeling.This author pledges the content of this article is based on professional experience and not AI generated.
View all my tips
Article Last Updated: 2023-07-11
