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Migrating to Modern APIs from Microsoft XML for Analysis SDK

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Migrating to Modern APIs from Microsoft XML for Analysis SDK### Introduction

Microsoft XML for Analysis (XMLA) SDK has long provided developers with a stable way to communicate with Microsoft Analysis Services and other OLAP (Online Analytical Processing) engines. Over time, however, modern APIs and protocols have emerged that offer better performance, improved security, richer functionality, and more idiomatic developer experiences. This article covers strategies, considerations, and step-by-step guidance for migrating from the Microsoft XML for Analysis SDK to modern APIs such as REST endpoints, the Analysis Services Tabular Model Scripting Language (TMSL), AMO for Tabular, or cloud-native services (Azure Analysis Services, Power BI REST APIs, and Microsoft Fabric endpoints).

Why migrate?

  • Performance: Modern APIs often use more efficient payloads (JSON vs. verbose XML), HTTP/2, and optimized endpoints that reduce latency.
  • Security: Newer services and APIs support modern auth flows (OAuth 2.0, Azure AD tokens), better encryption practices, and more granular IAM controls.
  • Ecosystem: Cloud-native services integrate with DevOps, monitoring, and management tools; they also receive active updates.
  • Developer experience: Modern SDKs and RESTful interfaces fit more naturally into contemporary application stacks (JavaScript, Python, .NET Core).
  • Future-proofing: Microsoft and cloud providers are investing in modern APIs; legacy XMLA-based workflows may receive limited future enhancements.

Assessing your current use of XMLA SDK

Before migrating, inventory how your application uses the XMLA SDK:

  • Querying data (MDX, DAX)
  • Executing administrative commands (CREATE, PROCESS)
  • Metadata discovery (catalogs, cubes, perspectives)
  • Security and role management
  • Eventing and logging
  • Programming languages and runtime environments in your stack

Document precise workflows, performance characteristics, and dependencies (scheduled jobs, ETL processes, client apps).

Choose the right modern target

Your migration target depends on your deployment and goals.

  • Azure Analysis Services / Azure Synapse / Microsoft Fabric:
    • Use REST APIs, TMSL, and modern management endpoints for serverless and PaaS scenarios.
  • Power BI:
    • Use Power BI REST APIs and XMLA endpoints (Power BI supports XMLA read/write but also benefits from REST APIs for workspace, dataset, and refresh management).
  • On-premises Analysis Services (latest versions):
    • Use Tabular Object Model (TOM/AMO) updates, TOM for .NET Core, and TMSL scripts.
  • Direct query or live connection layers:
    • Consider using Analysis Services JSON endpoints or the Tabular Model REST interfaces where available.

Authentication and authorization changes

XMLA often used Windows authentication or basic methods. Modern APIs require:

  • Azure AD OAuth 2.0 for cloud services (service principals, managed identities).
  • App registrations and client secrets or certificates.
  • Role-based access control (RBAC) and Azure AD groups.
  • For hybrid/on-prem scenarios, consider using Azure AD hybrid auth or service accounts with constrained permissions.

Migration steps:

  1. Register an application in Azure AD.
  2. Grant appropriate API permissions (Analysis Services, Power BI, Azure Management).
  3. Implement token acquisition: MSAL (Microsoft Authentication Library) is recommended.
  4. Replace legacy auth calls with bearer tokens in HTTP Authorization headers.

Translating operations: XMLA → Modern APIs

  • Queries (MDX / DAX)

    • Many modern endpoints still accept MDX/DAX but may expose them via different endpoints (REST payloads, TMSL Execute commands).
    • Example: Use the REST Execute endpoint to run DAX queries and receive JSON results.

  • Administrative tasks (CREATE / PROCESS)

    • Use TMSL scripts via REST or SDKs (TMSL is JSON-based and more concise than XMLA command XML).
    • Programmatic processing can be done with TOM/AMO (for .NET) or REST process endpoints.

  • Metadata discovery

    • Use the metadata endpoints or REST catalog calls to list models, tables, partitions, and measures.
    • TMSL/REST returns metadata in JSON, simplifying parsing.

  • Dataset refresh and management

    • Power BI has explicit refresh APIs; Azure Analysis Services supports processing via TMSL or management REST APIs.
    • Move scheduled refresh orchestration into Azure Data Factory, Logic Apps, or Power Platform where appropriate.

Example migration patterns

  1. Running a DAX query

    • Old (XMLA): Send an Execute command with DAX in an XMLA SOAP envelope, parse XML.
    • New (REST/TMSL): POST JSON to the query endpoint with DAX text, parse JSON results.

  2. Processing a model

    • Old: XMLA Process command.
    • New: POST a TMSL Process command (JSON) or use TOM’s Model.RequestRefresh in .NET.

  3. Creating/measuring metadata

    • Old: XMLA Discover commands and schema rowsets.
    • New: Use REST model metadata endpoints or TMSL scripts to inspect and modify model structure.

Code examples (conceptual)

.NET (using MSAL + HttpClient to call a REST execution endpoint)

// Acquire token with MSAL (pseudo) var app = ConfidentialClientApplicationBuilder.Create(clientId)   .WithClientSecret(secret)   .WithAuthority(new Uri(authority))   .Build(); var result = await app.AcquireTokenForClient(scopes).ExecuteAsync(); var token = result.AccessToken; var http = new HttpClient(); http.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", token); var payload = new {   queries = new [] {     new { query = "EVALUATE TOPN(100, Sales)" }   } }; var content = new StringContent(JsonConvert.SerializeObject(payload), Encoding.UTF8, "application/json"); var resp = await http.PostAsync("https://<server>/models/<model>/executeQuery", content); var json = await resp.Content.ReadAsStringAsync();

Python (calling Power BI/Analysis REST endpoints with requests + MSAL)

from msal import ConfidentialClientApplication import requests, json app = ConfidentialClientApplication(client_id, client_credential=client_secret, authority=authority) token = app.acquire_token_for_client(scopes=['https://analysis.windows.net/powerbi/api/.default']) headers = {'Authorization': 'Bearer ' + token['access_token'], 'Content-Type': 'application/json'} payload = {"queries":[{"query":"EVALUATE TOPN(100, Sales)"}]} r = requests.post('https://api.powerbi.com/v1.0/myorg/.../execute', headers=headers, json=payload) print(r.json())

Testing and validation

  • Create a migration test plan covering:
    • Functional parity for queries and DAX/MDX results.
    • Performance benchmarks (latency, throughput).
    • Security testing (token expiration, permission scopes).
    • Error handling and retry logic for transient failures.
  • Use synthetic workloads that mimic production queries and ETL patterns.
  • Validate data consistency at row and aggregated levels.

Performance considerations

  • JSON payloads reduce parsing overhead compared to XML; however, network design, compression, and paging matter.
  • Use batching where supported (batch queries or multi-statement TMSL).
  • Implement pagination for large result sets.
  • Cache metadata and token responses appropriately.
  • Monitor and tune timeouts, connection pooling, and retry policies.

Operational changes

  • Logging and monitoring: route diagnostics to Azure Monitor, Log Analytics, or Application Insights.
  • CI/CD: store TMSL scripts and deployment pipelines in source control; deploy via Azure DevOps, GitHub Actions, or Power BI deployment pipelines.
  • Backup and restore: automate metadata backups and model exports using REST/TMSL.

Common pitfalls and how to avoid them

  • Assuming full feature parity — verify that specific XMLA capabilities you rely on exist in the target API.
  • Underestimating auth complexity — set up proper app registrations and least-privilege permissions early.
  • Inadequate testing of big queries — large MDX/DAX results may require different handling (paging, streaming).
  • Ignoring rate limits — adhere to API quotas and implement exponential backoff.

Rollout strategy

  • Start with read-only operations: migrate reporting queries first to validate query paths.
  • Move administrative operations after auth and permissions are settled.
  • Use a blue/green or canary deployment pattern: route a portion of traffic to the new API and compare results.
  • Keep rollback plans: retain XMLA workflows until the new system proves stable.

When not to migrate

  • If you have legacy on-prem environments with no path to modern auth or network connectivity.
  • If third-party tools explicitly require XMLA and have no modern alternatives.
  • If migration cost outweighs benefits for a low-change, stable system.

Conclusion

Migrating from Microsoft XML for Analysis SDK to modern APIs provides security, performance, and development advantages. The migration requires careful planning: inventory current usage, pick the appropriate target API, update authentication to Azure AD flows, translate XMLA commands into TMSL/REST/TOM equivalents, and validate thoroughly with performance and consistency tests. A staged rollout with monitoring and rollback plans will reduce risk and ensure continuity.

If you want, I can: audit a specific XMLA script and produce an equivalent TMSL/REST example, or generate a migration checklist tailored to your environment.

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