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Tool guide
Azure Data Factory
Defines source-backed Azure Data Factory pipeline, data movement, integration, monitoring, and deployment review, evidence handling, and action boundaries.
Read the fileOverview
Azure Data Factory overview
Use this bundle to prepare source-backed Azure Data Factory pipeline, data movement, integration, monitoring, and deployment review and a review-ready Azure Data Factory change brief.
Read the fileWorkflow
Azure Data Factory source-backed triage
1. State the requested decision or artifact. 2. Inventory evidence: tenant, subscription, resource group, factory, region, environment, and API version; pipeline, activity, dataset, linked.
Read the fileTemplate
Azure Data Factory change brief
Review-ready artifact for Azure Data Factory pipeline, data movement, integration, monitoring, and deployment review, evidence quality, verification, and controlled actions.
Read the fileIs this bundle right for your task?
Who it is for
- People who configure, operate, integrate, govern, or review work performed in Azure Data Factory
- Teams working in Technology, Business operations
When to use it
- A proposed Azure Data Factory configuration or workflow change needs current IDs, permissions, dependencies, tests, and rollback evidence.
- A report, export, integration, or automation result needs to be reconciled against actual workspace state and current product documentation.
What you need to provide
- The product version or workspace scope, relevant configuration or export, desired outcome, permissions, and accountable owner.
- Current IDs, settings, records, logs, screenshots, integration details, and test evidence needed to verify the requested change.
Tasks and expected outputs
Questions it helps answer
- Prepare an Azure Data Factory change brief without fabricating local facts.
- Separate verified, provided, assumed, and missing evidence.
- Produce a review-ready decision with explicit verification and approval boundaries.
What it helps produce
- Azure Data Factory change brief
Practical example
Use it with an agent
Load the bundle as context, provide the evidence named above, then adapt this example to your situation.
Provide the product version or workspace scope, relevant configuration or export, desired outcome, permissions, and accountable owner. Ask the agent to review Azure Data Factory and produce Azure Data Factory change brief that maps configuration evidence, dependencies, permissions, tests, rollback, and actions that still require approval. Begin with azure.microsoft.com — Products / Data Factory, then confirm that the reference is current and applicable. Inspect Azure Data Factory before drafting.
Context path: bundles/tools/azure-data-factory
What the bundle includes
Tools
- Azure Data Factory
Frameworks
- source-evidence matrix
- Azure Data Factory pipeline, data movement, integration, monitoring, and deployment review matrix
- qualified-review gate
Evaluations
- Azure Data Factory source verification check
Sources used to build this bundle
These are the public references behind the role definition and operating guidance. The bundle does not replace current documentation or evidence from your site.
Limitations and safe use
Do not use this for
- Changing live configuration, records, permissions, automations, integrations, or shared data without verified scope, testing, rollback, and approval.
Known limitations
- Use the listed authoritative sources for general role or tool behavior; local configuration, records, values, states, permissions, and results require inspected evidence.
- Task-specific work requires current evidence for tenant, subscription, resource group, factory, region, environment, and API version; pipeline, activity, dataset, linked service, integration runtime, trigger, parameter, expression, dependency, source and sink contracts; identity, RBAC, networking, credentials, Key Vault, data classification, Git and deployment state, tests, run history, monitoring, alerts, costs, rollback, and approvals.
- Do not infer connection validity, trigger behavior, pipeline outcome, data completeness, monitoring state, cost, production configuration, or root cause.
Safety notes
- Minimize personal, customer, employee, financial, credential, security, privileged, health, student, and other sensitive data.
- Require explicit confirmation before actions that publish a factory change, create linked services, expose credentials, start or stop triggers, run pipelines, move or overwrite data, or deploy across environments.
- Route legal, privacy, security, compliance, financial, employment, clinical, safety, and other qualified judgments to an evidenced accountable reviewer.