Databricks
Connection and object support require review
Record types to review with Stacksync
| Record types | Coverage and requirements |
|---|---|
| Confirm support for this record type and the direction you need. |
Data warehouse / Business productivity · Two-way sync platform
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
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Proposed workflow
Contact reporting workflow
Planning example. Stacksync support for the required connection and record operations needs a technical review.
Starting eventA change involving the proposed contact table in Databricks or Microsoft 365 Contacts needs a defined result in the other system.
Start with the proposed contact table in Databricks 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.
What to verifyTest an email change, two records sharing an email, and a person associated with multiple organizations.
Review records and field ownershipProposed record relationships
Records to connect
Use these examples to define record matching and field ownership for your technical review.
Download the mapping worksheetCSV · No email required
| Databricks record | Microsoft 365 record | Record matching | Field ownership |
|---|---|---|---|
| Proposed contact tableProposed table; choose its name and schema.Reporting dataset | ContactsProposed record; confirm Stacksync object support. | 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. | Keep consent and communication preferences under an agreed authority; a general contact update must not silently resubscribe someone. |
| Proposed application user or identity tableProposed table; choose its name and schema.Reporting dataset | UsersProposed record; confirm Stacksync object support. | Use the immutable user ID within the tenant or directory. Do not equate an application user with a CRM customer contact. | Identity and application owners approve account lifecycle and access changes; synchronize only approved attributes. |
| Proposed task or work item tableProposed table; choose its name and schema.Reporting dataset | Planner & To Do TasksProposed record; confirm Stacksync object support. | Use a stable task/work-item ID and preserve its project or parent-ticket reference. | Choose the owner of task status and assignment; destination workflow states may require an explicit transition. |
These relationships do not establish connector availability. Review the required connection and record operations with Stacksync.
Record coverage to review
Use documented coverage where available. Catalog record types are starting points for review and do not confirm Stacksync support.
Microsoft 365
Connection and object support require review
Record types to review with Stacksync
| Record types | Coverage and requirements |
|---|---|
| Confirm support for this record type and the direction you need. |
Connection essentials
Confirm Stacksync support and account requirements for undocumented connections. Interface information alone does not establish connector availability.
View setup requirements and limits| Connection requirement | Databricks | Microsoft 365 |
|---|---|---|
| Integration interface | SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution | REST API (Microsoft Graph) |
| Authentication | Confirm the credentials, API plan, and permissions required for Databricks. | 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. | Confirm how Stacksync detects changes for this connector and the objects you need. |
| Read access | Confirm with Stacksync | Confirm with Stacksync |
| Write access | Confirm with Stacksync | Confirm with Stacksync |
Enterprise controls
Security and control for your integrations
Explore security controlsCompliance and data transfers
As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.
- SOC 2 Type II
- ISO 27001
- HIPAA BAA
- GDPR
- CCPA
- DPF US-EU-UK-CH
SSO & SCIM
Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.
Alerts
Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.
Secure connection options
Securely connects to your systems with:
Record-level recovery
Inspect sync errors and use retry and revert controls to resolve failed updates.
Read the recovery guideImplementation
Technical reference
Review setup, record relationships, testing, and recovery for your implementation.
Authentication, permissions and API limits
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
Setup requirements
- 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.
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
Setup requirements
- 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.
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 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.
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 worksheet · CSV, no email required
Reporting dataset
Proposed contact table in Databricks (choose its name) / Contacts
Plan a contact dataset while preserving its source meaning.
Planning example. Stacksync support for the required connection and record operations needs a technical review.
- 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
- Record dependencies
- Resolve the organization relationship and any owner or consent references required by the destination.
- Validation
- Test an email change, two records sharing an email, and a person associated with multiple organizations.
- Recovery
- Hold ambiguous matches for review and reconcile the person ID before retrying; preserve the consent decision already recorded by its owner.
Reporting dataset
Proposed application user or identity table in Databricks (choose its name) / Users
Plan a application user or identity dataset while preserving its source meaning.
Planning example. Stacksync support for the required connection and record operations needs a technical review.
- 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
- Record dependencies
- Resolve tenant and group references and establish a protected administrative-account policy.
- Validation
- Test a renamed login, disabled account, missing group, and a user existing in two tenants.
- Recovery
- Review access impact before retrying a lifecycle change; reconcile current identity state and retain an approval trail.
Reporting dataset
Proposed 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.
Planning example. Stacksync support for the required connection and record operations needs a technical review.
- 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
- Record dependencies
- Resolve project, user, and parent-item references before the task.
- Validation
- Test a reassignment, an unsupported status transition, a deleted parent, and a due date across time zones.
- Recovery
- Reconcile the destination state before retrying a transition to avoid reopening completed work.
Reporting dataset
Proposed message or conversation table in Databricks (choose its name) / Mail Messages
Plan a message or conversation dataset while preserving its source meaning.
Planning example. Stacksync support for the required connection and record operations needs a technical review.
- 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
- Record dependencies
- Resolve conversation, participant, and customer context before associating messages.
- Validation
- Test a delivery-state change, repeated message, private conversation, and reply linked to the correct thread.
- Recovery
- Check provider delivery state before retrying a send; replaying history must not send the message again.
Reporting dataset
Proposed 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.
Planning example. Stacksync support for the required connection and record operations needs a technical review.
- 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
- Record dependencies
- Resolve folder/container and parent-record references before attaching metadata.
- Validation
- Test a renamed file, a new version, a moved folder, and an access-restricted document.
- Recovery
- Check the current file version and destination access before retrying transfer or metadata updates.
Reporting dataset
Proposed group or membership table in Databricks (choose its name) / Groups
Plan a group or membership dataset while preserving its source meaning.
Planning example. Stacksync support for the required connection and record operations needs a technical review.
- 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
- Record dependencies
- Resolve user and tenant identities before membership changes.
- Validation
- Test removed membership, nested groups, and equal group names in different tenants.
- Recovery
- Recompute the approved membership delta before retrying; do not replay an outdated access grant.
Compare integration approaches
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 scenarios and expected results
Contact reporting workflow
Planning example. Stacksync support for the required connection and record operations needs a technical review.
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
Planning example. Stacksync support for the required connection and record operations needs a technical review.
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
Planning example. Stacksync support for the required connection and record operations needs a technical review.
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
This is an evaluation scenario; connector and operation support require confirmation.
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.
Initial load and acceptance testing
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.
Failed updates, retries and recovery
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.
Change detection and update delivery
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.
FAQ
Databricks and Microsoft 365 integration FAQ
-
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.
-
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.
-
Measure initial-load and ongoing-change latency separately. Source detection, selected objects, account limits, and destination validation determine the observed delay.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
Integration directory
Related integrations
Review documented support, sync direction, and setup requirements on each pair page. Search all 499 integrations listed for Databricks and Microsoft 365.
Next step
Plan your integration with an engineer
Walk through your Databricks and Microsoft 365 records, field mappings, and requirements with an integration engineer.
