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Remediate assets prior to deploying Microsoft Fabric workloads

During the assessment process for Microsoft Fabric workload migration, it's critical to identify and remediate any technical, operational, or architectural blockers that might impact the readiness or success of a workload deployment.

Unlike traditional IaaS migrations, Fabric workloads are composed of platform-native artifacts such as Lakehouses, Warehouses, Pipelines, and Eventstreams. However, many data sources, identities, and operational patterns must still be prepared or adjusted before workloads can be activated.

Types of remediation in Microsoft Fabric​

Remediation based on assessment results​

These actions are required to ensure readiness for deployment and compatibility with Microsoft Fabric capabilities:

  • Data classification and tagging gaps that affect governance and DLP policies
  • Access control model misalignment, e.g. lack of Entra ID groups for least-privilege access
  • Schema or source compatibility issues, such as unsupported formats in Pipelines or Lakehouses
  • Connectivity blockers, like unconfigured firewall rules for source systems or FTP endpoints

These tasks usually stem from the Fabric workload assessment phase.

Remediation based on testing feedback​

These arise during dry runs, UAT, or early activations:

  • Pipelines fail due to runtime permissions (e.g. service principal missing access to a SQL DB)
  • Eventstreams receive malformed data, causing downstream failure
  • Timeouts or ingestion errors from staging environments (e.g. unsupported JSON structures)
  • Power BI reports not rendering correctly due to schema drift or unsupported visuals

Expect these remediations to require several iterations.

Track and prioritize remediation​

Use Azure DevOps or GitHub Projects to:

  • Track remediation tasks across workloads and artifacts
  • Link tasks to affected Fabric components (e.g. workspace-forecast, pipeline-dailyload)
  • Tag cross-cutting issues (e.g. data-quality, access-control, governance)
  • Assign responsibility and align with sprint planning

Prioritize:

  • Shared blockers (e.g. broken lineage in a shared Lakehouse)
  • Security-critical issues (e.g. exposed service principal secrets)
  • Release-critical failures that prevent scheduled workload promotion

Common Fabric remediation patterns​

Metadata and lineage issues​

  • Incomplete or outdated descriptions for datasets and tables
  • Broken column-level lineage after schema refactoring
  • Missing business glossary terms

➡️ Use Microsoft Purview or inline descriptions and tags to remediate.

Identity and access issues​

  • Service principals not added to the right workspace roles
  • Fabric Pipelines unable to authenticate to Azure SQL / REST APIs

➡️ Verify Entra ID group membership and Managed Identity scopes.

Pipeline configuration issues​

  • Invalid parameter defaults or broken dynamic expressions
  • Missing failure paths or retry logic

➡️ Use Fabric's built-in test pipeline feature and validate in test environments.

Ingestion or staging issues​

  • Data not available in the expected staging location (Blob, Event Hub, FTP)
  • Encoding mismatches or missing delimiters

➡️ Add data validation steps and alerts on zero-row loads.

When to separate remediation from migration​

If remediating issues consumes a large portion of your iteration, separate these into:

  • A dedicated remediation sprint, or
  • A parallel modernization stream

Examples that justify this separation:

  • Legacy FTP endpoints that must be replaced with Event Hub or REST-based ingestion
  • Schemas requiring full redesign to align with a Medallion architecture
  • Replacing data silos with Lakehouse tables for better performance and cost

Modernization as remediation​

When the effort of remediation approaches the effort of rebuilding, consider rearchitecting your workload. Learn more at Modernize in Fabric.

Summary checklist​

ActivityDescription
Access validationVerify Entra ID group assignments and service principal scopes
Pipeline logicValidate parameters, expressions, retry logic
Source availabilityConfirm external data is accessible and well-formed
MetadataEnsure datasets, tables, and columns are properly tagged and described
GovernanceAlign classification and tags with Fabric governance policies
Staging healthValidate availability of data in Eventstreams, Blob, FTP

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