Start with one meaningful update
Identify the record that changes in Databricks or Microsoft 365, where it needs to appear, and which team depends on it.
Data warehouse / Business productivity
Databricks and Microsoft 365 integration
Plan how Databricks and Microsoft 365 should share data across your business. Work with Stacksync engineers on record mapping, system access, and the requirements for running the integration.
- Scope your workflow with an integration engineer
- Review the systems, records, and updates you need
Teams building with Stacksync
Plan the connection your business needs
Explore a Databricks and Microsoft 365 integration with a Stacksync engineer. Stacksync support for Databricks and Microsoft 365 is not established by the connector documentation reviewed for this page. Start with one record and the update your business needs to identify an implementation path.
Establish the available connection
Bring the objects, account editions, and required directions. An engineer can review the connector path, permissions, and field access.
Define a result you can verify
Agree on record matching, acceptable delay, expected volume, and how your team will resolve failed updates.
Technical referenceAvailable documentation, candidate record relationships, and questions for your technical review.
What records can you sync?
Explore the record types and read/write requirements for each system.
DatabricksConnection and object support require review
Record types to review with Stacksync
| Record type | Coverage and requirements |
|---|---|
| Catalogs | Confirm support for this record type and the direction you need. |
| Schemas | Confirm support for this record type and the direction you need. |
| Delta Tables | Confirm support for this record type and the direction you need. |
| Views | Confirm support for this record type and the direction you need. |
| Materialized Views | Confirm support for this record type and the direction you need. |
| Volumes | Confirm support for this record type and the direction you need. |
Microsoft 365Connection and object support require review
Record types to review with Stacksync
| Record type | Coverage and requirements |
|---|---|
| Users | Confirm support for this record type and the direction you need. |
| Groups | Confirm support for this record type and the direction you need. |
| Mail Messages | Confirm support for this record type and the direction you need. |
| Calendar Events | Confirm support for this record type and the direction you need. |
| Contacts | Confirm support for this record type and the direction you need. |
| OneDrive Files (driveItems) | Confirm support for this record type and the direction you need. |
Connection requirements and limits
Databricks
- Integration interface
- SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution
- Authentication
- Confirm the credentials, API plan, and permissions required for Databricks.
- Change detection
- Confirm how Stacksync detects changes for this connector and the objects you need.
- Read access
- Confirm with Stacksync
- Write access
- Confirm with Stacksync
Limitations to check
- Confirm Stacksync support for Databricks and the record types your workflow needs.
- Review write-back, deletion handling, update timing, and account limits with the integration team.
Microsoft 365
- Integration interface
- REST API (Microsoft Graph)
- Authentication
- Confirm the credentials, API plan, and permissions required for Microsoft 365.
- Change detection
- Confirm how Stacksync detects changes for this connector and the objects you need.
- Read access
- Confirm with Stacksync
- Write access
- Confirm with Stacksync
Limitations to check
- Confirm Stacksync support for Microsoft 365 and the record types your workflow needs.
- Review write-back, deletion handling, update timing, and account limits with the integration team.
Prepare your technical review
Use the worksheets and reference checks to capture record identity, ownership, and the result your business expects.
Implementation reference
Record identity and field ownership
Use these data-model references to describe the records your connection needs. They are planning examples; connector availability and supported operations must be established before implementation.
Download the mapping worksheetCSV · No email required · Record matching, ownership, and test cases
Reporting datasetProposed contact table in Databricks (choose its name) Contacts
Plan a contact dataset while preserving its source meaning.
- Databricks
- Your database schema
- Microsoft 365
- Object support to establish
Record identity
Use a stable person/contact ID and an explicit cross-system lookup. Email can change and can be shared, so treat it as a matching clue rather than a universal key.
Field ownership
Keep consent and communication preferences under an agreed authority; a general contact update must not silently resubscribe someone.
Fields to include
- Source person ID
- Display name
- Email address
- Organization reference
- Consent state
Reporting datasetProposed application user or identity table in Databricks (choose its name) Users
Plan a application user or identity dataset while preserving its source meaning.
- Databricks
- Your database schema
- Microsoft 365
- Object support to establish
Record identity
Use the immutable user ID within the tenant or directory. Do not equate an application user with a CRM customer contact.
Field ownership
Identity and application owners approve account lifecycle and access changes; synchronize only approved attributes.
Fields to include
- Source user ID
- Tenant reference
- Account status
- Group references
Reporting datasetProposed task or work item table in Databricks (choose its name) Planner & To Do Tasks
Plan a task or work item dataset while preserving its source meaning.
- Databricks
- Your database schema
- Microsoft 365
- Object support to establish
Record identity
Use a stable task/work-item ID and preserve its project or parent-ticket reference.
Field ownership
Choose the owner of task status and assignment; destination workflow states may require an explicit transition.
Fields to include
- Source task ID
- Parent reference
- Assignee
- Status
- Due date
Reporting datasetProposed message or conversation table in Databricks (choose its name) Mail Messages
Plan a message or conversation dataset while preserving its source meaning.
- Databricks
- Your database schema
- Microsoft 365
- Object support to establish
Record identity
Retain message ID, conversation/thread ID, channel, and sender identity.
Field ownership
Separate message history from actions that send new messages; preserve private/public visibility and channel consent.
Fields to include
- Source message ID
- Thread reference
- Delivery state
- Timestamp
- Related record ID
Reporting datasetProposed file or document metadata table in Databricks (choose its name) OneDrive Files (driveItems)
Plan a file or document metadata dataset while preserving its source meaning.
- Databricks
- Your database schema
- Microsoft 365
- Object support to establish
Record identity
Keep file/object ID, container, and version. A path can change and a filename can repeat.
Field ownership
Separate document content, metadata, and sharing permissions; a metadata sync does not imply file transfer or ACL replication.
Fields to include
- Source file ID
- Container reference
- Version
- Metadata
- Access classification
Reporting datasetProposed group or membership table in Databricks (choose its name) Groups
Plan a group or membership dataset while preserving its source meaning.
- Databricks
- Your database schema
- Microsoft 365
- Object support to establish
Record identity
Keep group IDs and membership relationships separately from group names.
Field ownership
The access owner controls membership; reporting a group is different from granting its permissions.
Fields to include
- Source group ID
- Member references
- Tenant reference
- Group type
Architecture decision
Choose how to connect your systems
Choose a method around one example record and the update your business needs. Use Proposed contact table in Databricks (choose its name) / Contacts to review record matching and confirm Stacksync support for the required operations. Compare ongoing sync, a custom workflow, and a scheduled export against that requirement.
Stacksync managed sync
- Best fit
- Review compatibility with a Stacksync engineer using an example of the records and updates you need.
- Operating responsibility
- Fits ongoing record synchronization when the required operations are supported. Add workflow steps for approvals or business actions that go beyond copying fields.
- Before you choose
- Check record matching: Use a stable person/contact ID and an explicit cross-system lookup. Email can change and can be shared, so treat it as a matching clue rather than a universal key. Verify field coverage, deletion handling, and how changes are detected.
Native vendor integration
- Best fit
- A vendor-built integration may fit if it supports your Databricks and Microsoft 365 record types.
- Operating responsibility
- Can reduce setup for a supported workflow. You may need another method for records or business steps it does not cover.
- Before you choose
- First check whether either vendor offers this integration. If available, verify Proposed contact table in Databricks (choose its name) / Contacts, update direction, account tier, and related-record handling.
Custom API or workflow
- Best fit
- Consider when Databricks and Microsoft 365 need a transformation, approval, or action outside a direct record sync.
- Operating responsibility
- Provides control over business steps; the team owns credentials, version changes, error queues, and reconciliation.
- Before you choose
- Verify endpoint permissions, pagination, quotas, duplicate detection, and failure recovery.
File or scheduled snapshot
- Best fit
- Consider for a one-time Databricks / Microsoft 365 migration or a reporting need with an explicit freshness window.
- Operating responsibility
- Can simplify a bounded transfer; later changes and deletion history require another extraction or a separately designed incremental process.
- Before you choose
- Record the extraction cutoff, source IDs, encoding, date/number formats, and reconciliation totals.
Workflow reference
From a business event to the right update
Open a workflow to see its trigger, record relationships, and expected result.
Contact reporting workflow
Starting event: A change to the selected Proposed contact table in Databricks (choose its name) or Contacts record needs a defined result in the other system.
- Start with Databricks Proposed contact table in Databricks (choose its name) and Microsoft 365 Contacts. Use the record-matching and field-ownership rules from your mapping worksheet.
- Resolve the organization relationship and any owner or consent references required by the destination.
- Test a normal update and one failed or repeated update in the supported direction. Keep both record IDs with the test results.
Expected result: Test an email change, two records sharing an email, and a person associated with multiple organizations.
If it fails: Hold ambiguous matches for review and reconcile the person ID before retrying; preserve the consent decision already recorded by its owner.
Application user or identity reporting workflow
Starting event: A change to the selected Proposed application user or identity table in Databricks (choose its name) or Users record needs a defined result in the other system.
- Start with Databricks Proposed application user or identity table in Databricks (choose its name) and Microsoft 365 Users. Use the record-matching and field-ownership rules from your mapping worksheet.
- Resolve tenant and group references and establish a protected administrative-account policy.
- Test a normal update and one failed or repeated update in the supported direction. Keep both record IDs with the test results.
Expected result: Test a renamed login, disabled account, missing group, and a user existing in two tenants.
If it fails: Review access impact before retrying a lifecycle change; reconcile current identity state and retain an approval trail.
Task or work item reporting workflow
Starting event: A change to the selected Proposed task or work item table in Databricks (choose its name) or Planner & To Do Tasks record needs a defined result in the other system.
- Start with Databricks Proposed task or work item table in Databricks (choose its name) and Microsoft 365 Planner & To Do Tasks. Use the record-matching and field-ownership rules from your mapping worksheet.
- Resolve project, user, and parent-item references before the task.
- Test a normal update and one failed or repeated update in the supported direction. Keep both record IDs with the test results.
Expected result: Test a reassignment, an unsupported status transition, a deleted parent, and a due date across time zones.
If it fails: Reconcile the destination state before retrying a transition to avoid reopening completed work.
Define an explicit handoff between Databricks and Microsoft 365
Starting event: A business event involving a selected business dataset needs a defined response involving Users or Groups.
- Document what the source event means and which destination record or action should respond. A similar name does not establish a shared entity.
- Retain separate IDs and choose whether the destination is a report, a new work item, or a change to an existing record.
- Assign an approval owner and a duplicate-detection rule before running an action. Use a custom workflow only after its endpoint support is confirmed.
Expected result: An example input has an unambiguous destination and expected result; repeated delivery produces only the intended change.
If it fails: Resolve missing identity or ambiguous business meaning before retrying; route unsupported operations to the implementation owner.
Production readiness
Test the behavior your business depends on
Keep both record IDs with the expected and actual result. Reconcile the same filters and time window in each system.
Proposed contact table in Databricks (choose its name) / Contacts
Test case
Test an email change, two records sharing an email, and a person associated with multiple organizations.
Expected result
The expected contact relationship is preserved with no duplicate action or unintended write.
Proposed application user or identity table in Databricks (choose its name) / Users
Test case
Test a renamed login, disabled account, missing group, and a user existing in two tenants.
Expected result
The expected application user or identity relationship is preserved with no duplicate action or unintended write.
Proposed task or work item table in Databricks (choose its name) / Planner & To Do Tasks
Test case
Test a reassignment, an unsupported status transition, a deleted parent, and a due date across time zones.
Expected result
The expected task or work item relationship is preserved with no duplicate action or unintended write.
Proposed message or conversation table in Databricks (choose its name) / Mail Messages
Test case
Test a delivery-state change, repeated message, private conversation, and reply linked to the correct thread.
Expected result
The expected message or conversation relationship is preserved with no duplicate action or unintended write.
Direction and permissions
Test case
Bring an example source record and the intended destination operation to the compatibility review. Confirm the supported route before granting write access.
Expected result
Only an approved, supported direction and permitted fields are written.
Freshness and reconciliation
Test case
Measure source and destination times for the selected records under normal load and a burst. Reconcile IDs and values using the same filters and cutoff.
Expected result
The process meets its agreed freshness target and reconciliation has no unexplained differences.
Failure recovery
Find the cause. Restore the data flow.
Start with the failed record and the destination error, then inspect the source value, field requirements, and access.
Rejected or repeated contact change
Investigate
Inspect Databricks Proposed contact table in Databricks (choose its name) and Microsoft 365 Contacts, their IDs, and the destination error.
Next action
Hold ambiguous matches for review and reconcile the person ID before retrying; preserve the consent decision already recorded by its owner.
Rejected or repeated application user or identity change
Investigate
Inspect Databricks Proposed application user or identity table in Databricks (choose its name) and Microsoft 365 Users, their IDs, and the destination error.
Next action
Review access impact before retrying a lifecycle change; reconcile current identity state and retain an approval trail.
Rejected or repeated task or work item change
Investigate
Inspect Databricks Proposed task or work item table in Databricks (choose its name) and Microsoft 365 Planner & To Do Tasks, their IDs, and the destination error.
Next action
Reconcile the destination state before retrying a transition to avoid reopening completed work.
A record type or update is unavailable
Investigate
Check the Databricks and Microsoft 365 connector guides, account permissions, and any operations marked On Request.
Next action
Ask the integration team to confirm a supported way to handle that record. Verify whether it needs connector configuration or a separate workflow step.
Source and destination disagree after a retry
Investigate
Compare current source values, destination validation, identity mappings, and any side effects already completed.
Next action
Stacksync issue retry reads the current source state. Decide the intended state before retrying or reverting; reconcile downstream effects separately.
Read the Stacksync issues dashboard guide for retry and revert behavior.
How updates move between Databricks and Microsoft 365
See how each system detects changes and which updates the other system can receive. Each direction has its own permissions and record requirements.
Databricks Microsoft 365 Direction requires confirmation
Detect changesConfirm how Stacksync detects changes for this connector and the objects you need.
Apply updatesConfirm that Stacksync can create or update the records you need in Microsoft 365.
Microsoft 365 Databricks Direction requires confirmation
Detect changesConfirm how Stacksync detects changes for this connector and the objects you need.
Apply updatesConfirm that Stacksync can create or update the records you need in Databricks.
Update timing and record limits
- Measure initial-load and ongoing-change latency separately. Source detection, selected objects, account limits, and destination validation determine the observed delay.
- Review write-back, deletion handling, update timing, and account limits with the integration team.
- Review write-back, deletion handling, update timing, and account limits with the integration team.
Prepare Databricks and Microsoft 365 access
Set up both accounts before testing the mapping. Use test records where available, and identify the account administrator who can approve access and help resolve setup errors.
Databricks setup checklist
- Identify the Databricks account, edition, environment, and business objects the integration must access.
- Confirm a Stacksync connector or implementation path for Databricks, including read/write support, authentication, and initial-load limits.
Microsoft 365 setup checklist
- Identify the Microsoft 365 account, edition, environment, and business objects the integration must access.
- Confirm a Stacksync connector or implementation path for Microsoft 365, including read/write support, authentication, and initial-load limits.
Prepare to go live
Record the fields each system can update, the first-load cutoff, both record IDs, the expected update delay, and who handles errors. Complete the tests before production before expanding to more records.
Use the Databricks and Microsoft 365 planning worksheet to capture these decisions. Record the access owner in the worksheet and enter credentials only in the connection setup.
Security and control for your integrations
Use SSO and SCIM to manage access, secure connection options to reach your systems, and record-level retry and revert controls to resolve sync errors.
FAQ
Databricks and Microsoft 365 integration FAQ
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Explore a Databricks and Microsoft 365 integration with a Stacksync engineer. Stacksync support for Databricks and Microsoft 365 is not established by the connector documentation reviewed for this page. Start with one record and the update your business needs to identify an implementation path.
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Confirm two-way support with Stacksync for the records and fields you need in both systems. Access to a vendor API does not confirm that its Stacksync connector supports write-back.
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Measure initial-load and ongoing-change latency separately. Source detection, selected objects, account limits, and destination validation determine the observed delay.
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No. Stacksync documents that pre-existing duplicates are not merged automatically when two-way sync begins. Review the initial dataset and matching plan before enabling it; an empty destination can simplify the first load.
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Start with one business entity and a stable record ID. Map a small set of editable fields with compatible types, test required values and relationships, then expand after the pilot passes.
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Check the destination error, field constraints, permissions, and current source value. The Stacksync issues dashboard supports retry and revert; retry reads current source values, so verify the intended record state before acting.
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Use the current Stacksync pricing page and confirm the supported implementation with the team. Scope the required objects, record volume, update frequency, initial load, and support needs when comparing a managed connector with native or custom development.
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Start by reviewing Proposed contact table in Databricks (choose its name) in Databricks and Contacts in Microsoft 365. Check how these records relate in your workflow, then confirm the actual fields and supported operations. Test record matching and one failed or repeated update before adding more records.
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Choose a method around one example record and the update your business needs. Use Proposed contact table in Databricks (choose its name) / Contacts to review record matching and confirm Stacksync support for the required operations. Compare ongoing sync, a custom workflow, and a scheduled export against that requirement.
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Prepare both accounts, the selected object schemas, stable source and destination IDs, and the expected outcome. Identify the Databricks account, edition, environment, and business objects the integration must access. Identify the Microsoft 365 account, edition, environment, and business objects the integration must access. Use the pair worksheet to record ownership and acceptance criteria.
Find the right integration path
Walk through your Databricks and Microsoft 365 records, field mappings, and requirements with an integration engineer.
Explore related integrationsFind other connections for Databricks and Microsoft 365.
Related integrations
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