Developing Applications with Azure Analysis Services

This document provides comprehensive guidance on developing applications that leverage Azure Analysis Services, enabling powerful business intelligence and data analysis capabilities.

Introduction to Azure Analysis Services

Azure Analysis Services is a fully managed Platform as a Service (PaaS) that provides enterprise-grade data modeling capabilities. It enables developers to create semantic models that can be queried by reporting tools like Power BI, Excel, and other business intelligence applications.

Key Development Areas

Developing applications with Azure Analysis Services typically involves several key areas:

Data Modeling: Designing and building the tabular or multidimensional models that represent your business data.
Connecting to Data Sources: Establishing connections to various on-premises and cloud data sources.
Client Application Development: Integrating with Analysis Services from client applications using various SDKs and APIs.
Performance Tuning: Optimizing models and queries for speed and efficiency.
Security Implementation: Securing access to models and data.

Data Modeling with Tabular Editor

While SQL Server Data Tools (SSDT) has been a primary tool, the Tabular Editor has emerged as a popular and efficient option for developing tabular models. It provides a rich interface for creating, editing, and deploying your data models.

Note: Tabular Editor is a third-party tool. Ensure you understand its licensing and support before use.

Connecting to Azure Analysis Services

You can connect to Azure Analysis Services from various client applications and tools. Common connection methods include:

Power BI: Connect directly to your Azure Analysis Services models for interactive dashboards and reports.
Excel: Use Excel's Power Pivot or Get & Transform Data features to connect and analyze data.
SSAS Client Libraries: Utilize .NET libraries (AMO, ADOMD.NET) for programmatic access and management.
Other BI Tools: Many other BI platforms support direct connections to Azure Analysis Services.

Example: Connecting with C# using ADOMD.NET

Here's a simplified example of how to connect to an Azure Analysis Services model using ADOMD.NET in C#:


using Microsoft.AnalysisServices.AdomdClient;
using System;

public class ConnectionExample
{
    public static void Main(string[] args)
    {
        string connectionString = "Provider=MSOLAP;Data Source=asazure://westus.asazure.windows.net/your_server_name;Initial Catalog=your_database_name;Integrated Security=True;";

        try
        {
            using (var connection = new AdomdConnection(connectionString))
            {
                connection.Open();
                Console.WriteLine("Successfully connected to Azure Analysis Services.");

                // Example: Execute a simple DAX query
                string daxQuery = "EVALUATE 'DimCustomer'";
                using (var command = new AdomdCommand(daxQuery, connection))
                {
                    using (var reader = command.ExecuteReader())
                    {
                        while (reader.Read())
                        {
                            for (int i = 0; i < reader.FieldCount; i++)
                            {
                                Console.Write(reader.GetValue(i) + "\t");
                            }
                            Console.WriteLine();
                        }
                    }
                }
            }
        }
        catch (Exception ex)
        {
            Console.WriteLine($"Error connecting to Azure Analysis Services: {ex.Message}");
        }
    }
}

Best Practices and Performance

To ensure your applications perform well, consider these best practices:

Model Design: Design your tabular models efficiently, minimizing redundant calculations and optimizing data types.
Query Optimization: Write efficient DAX and MDX queries. Avoid SELECT * and minimize the use of complex iterators where possible.
Partitioning: Utilize partitioning to manage large datasets and improve query performance.
Resource Scaling: Scale your Azure Analysis Services instance appropriately based on your workload demands.

Next Steps

Explore the following resources to deepen your understanding and start developing your applications: