Can Databricks and Deposco sync in both directions?

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.

What should be ready before the Databricks and Deposco initial load?

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 Deposco account, edition, environment, and business objects the integration must access. Use the pair worksheet to record ownership and acceptance criteria.

How fast does the Databricks to Deposco sync run?

Measure initial-load and ongoing-change latency separately. Source detection, selected objects, account limits, and destination validation determine the observed delay.

Will existing duplicates merge automatically?

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.

How do I map Databricks fields to Deposco?

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.

What should I do if a record fails to sync?

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.

What does a Databricks and Deposco integration cost?

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.

Which records should the Databricks and Deposco pilot start with?

Start by reviewing Proposed customer table in Databricks (choose its name) in Databricks and Customers in Deposco. 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.

Data warehouse / ERP

Databricks and Deposco integration

Plan how Databricks and Deposco 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

Integration planning

stacksync.com

Databricks

Deposco

Review the integration scope

Teams building with Stacksync

Plan the connection your business needs

Explore a Databricks and Deposco integration with a Stacksync engineer. Stacksync support for Databricks and Deposco 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.

01 / Business process

Start with one meaningful update

Identify the record that changes in Databricks or Deposco, where it needs to appear, and which team depends on it.

02 / Data access

Establish the available connection

Bring the objects, account editions, and required directions. An engineer can review the connector path, permissions, and field access.

03 / Success criteria

Define a result you can verify

Agree on record matching, acceptable delay, expected volume, and how your team will resolve failed updates.

Explore the technical references

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.

Discuss Databricks requirements

DeposcoConnection and object support require review

Record types to review with Stacksync

Record type	Coverage and requirements
Items / SKUs	Confirm support for this record type and the direction you need.
Inventory	Confirm support for this record type and the direction you need.
Sales orders	Confirm support for this record type and the direction you need.
Shipments	Confirm support for this record type and the direction you need.
Purchase orders / ASNs	Confirm support for this record type and the direction you need.
Receipts	Confirm support for this record type and the direction you need.

Discuss Deposco requirements

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.

Deposco

Integration interface: REST API plus file-based interfaces (EDI and flat file) common to WMS integrations
Authentication: Confirm the credentials, API plan, and permissions required for Deposco.
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 Deposco 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 worksheet

CSV · No email required · Record matching, ownership, and test cases

Reporting datasetProposed customer table in Databricks (choose its name) Customers

Plan a customer dataset while preserving its source meaning.

Databricks: Your database schema
Deposco: Object support to establish

Record identity

Determine whether each customer represents an individual, a company, or a business-unit relationship before matching it to a contact or organization. Retain the source customer ID and destination ID; names and email alone are insufficient.

Field ownership

Separate the customer relationship from contact details, billing authority, and consent. Choose an owner for each field after identifying whether the customer is a person or company.

Fields to include

Source customer ID
Person/company classification
Legal entity or business unit
Destination identity reference

Reporting datasetProposed sales order table in Databricks (choose its name) Sales orders

Plan a sales order dataset while preserving its source meaning.

Databricks: Your database schema
Deposco: Object support to establish

Record identity

Preserve both the order ID and stable line IDs. Allow one order to relate to several shipments or invoices.

Field ownership

Decide which system approves the order and which can cancel or amend it after fulfillment begins.

Fields to include

Source order ID
Customer reference
Line references and quantities
Order status
Currency

Reporting datasetProposed inventory or stock position table in Databricks (choose its name) Inventory

Plan a inventory or stock position dataset while preserving its source meaning.

Databricks: Your database schema
Deposco: Object support to establish

Record identity

Identify stock by product/variant, location, and any lot or serial dimension; a product ID alone is insufficient.

Field ownership

Choose the stock authority and distinguish available, reserved, and physical quantities.

Fields to include

Product reference
Location reference
Quantity type
Quantity
As-of time

Reporting datasetProposed shipment or fulfillment table in Databricks (choose its name) Shipments

Plan a shipment or fulfillment dataset while preserving its source meaning.

Databricks: Your database schema
Deposco: Object support to establish

Record identity

Retain shipment/fulfillment and line IDs, order reference, and carrier context.

Field ownership

The fulfillment owner controls shipped quantities and cancellation eligibility.

Fields to include

Source shipment ID
Order/line references
Carrier
Tracking reference
Fulfillment status

Reporting datasetProposed purchase order table in Databricks (choose its name) Purchase orders / ASNs

Plan a purchase order dataset while preserving its source meaning.

Databricks: Your database schema
Deposco: Object support to establish

Record identity

Retain purchase-order and line IDs together with the supplier and legal-entity context.

Field ownership

Keep procurement approval separate from receipt and payment states.

Fields to include

Source purchase-order ID
Supplier reference
Line references
Quantity
Approval status

Architecture decision

Choose how to connect your systems

Choose a method around one example record and the update your business needs. Use Proposed customer table in Databricks (choose its name) / Customers 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: Determine whether each customer represents an individual, a company, or a business-unit relationship before matching it to a contact or organization. Retain the source customer ID and destination ID; names and email alone are insufficient. 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 Deposco 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 customer table in Databricks (choose its name) / Customers, update direction, account tier, and related-record handling.

Custom API or workflow

Best fit: Consider when Databricks and Deposco 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. Separate reading history from actions that send messages, grant access, or post transactions.

File or scheduled snapshot

Best fit: Consider for a one-time Databricks / Deposco 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.

Customer reporting workflow

Starting event: A change to the selected Proposed customer table in Databricks (choose its name) or Customers record needs a defined result in the other system.

Start with Databricks Proposed customer table in Databricks (choose its name) and Deposco Customers. Use the record-matching and field-ownership rules from your mapping worksheet.
Resolve person or company type, legal entity, business role, and any billing account before transactions.
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 individual buyer, a company buyer, and one company with multiple billing relationships. Do not merge these into a single generic contact.

If it fails: Hold ambiguous customer matches for review and resolve customer type before retrying dependent records.

Sales order reporting workflow

Starting event: A change to the selected Proposed sales order table in Databricks (choose its name) or Sales orders record needs a defined result in the other system.

Start with Databricks Proposed sales order table in Databricks (choose its name) and Deposco Sales orders. Use the record-matching and field-ownership rules from your mapping worksheet.
Create or locate the customer and products first; resolve taxes, currency, and fulfillment references.
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 multi-line order, a partial cancellation, and two partial shipments. Compare line totals as well as the header.

If it fails: Check for an existing destination order before retrying a timed-out create; reconcile line IDs to avoid duplicate fulfillment.

Inventory or stock position reporting workflow

Starting event: A change to the selected Proposed inventory or stock position table in Databricks (choose its name) or Inventory record needs a defined result in the other system.

Start with Databricks Proposed inventory or stock position table in Databricks (choose its name) and Deposco Inventory. Use the record-matching and field-ownership rules from your mapping worksheet.
Resolve product, unit, warehouse/location, and lot/serial references.
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 the same SKU in two locations, a reservation, and an older snapshot arriving late.

If it fails: Reconcile the latest stock position before retrying; do not apply an old absolute quantity after new movements.

Reconcile Deposco business records with Databricks

Starting event: A finance-owned record in Deposco needs operational visibility through a selected destination dataset.

Select Inventory or Sales orders with the correct legal entity, period, and currency.
Define a reporting relationship in Databricks; do not equate customer records, ledger accounts, and posted transactions.
Decide whether the process only reports a financial state or requests an approved accounting action, and maintain a separate transaction ID for each action.

Expected result: Totals reconcile within the same entity/currency/window; a repeated handoff creates no duplicate financial transaction.

If it fails: Verify posting and settlement state before retrying. Use the approved adjustment path for already-posted transactions.

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 customer table in Databricks (choose its name) / Customers

Test case

Test an individual buyer, a company buyer, and one company with multiple billing relationships. Do not merge these into a single generic contact.

Expected result

The expected customer relationship is preserved with no duplicate action or unintended write.

Proposed sales order table in Databricks (choose its name) / Sales orders

Test case

Test a multi-line order, a partial cancellation, and two partial shipments. Compare line totals as well as the header.

Expected result

The expected sales order relationship is preserved with no duplicate action or unintended write.

Proposed inventory or stock position table in Databricks (choose its name) / Inventory

Test case

Test the same SKU in two locations, a reservation, and an older snapshot arriving late.

Expected result

The expected inventory or stock position relationship is preserved with no duplicate action or unintended write.

Proposed shipment or fulfillment table in Databricks (choose its name) / Shipments

Test case

Test split shipments, multiple tracking references, partial returns, and a carrier status arriving out of order.

Expected result

The expected shipment or fulfillment 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 customer change

Investigate

Inspect Databricks Proposed customer table in Databricks (choose its name) and Deposco Customers, their IDs, and the destination error.

Next action

Hold ambiguous customer matches for review and resolve customer type before retrying dependent records.

Rejected or repeated sales order change

Investigate

Inspect Databricks Proposed sales order table in Databricks (choose its name) and Deposco Sales orders, their IDs, and the destination error.

Next action

Check for an existing destination order before retrying a timed-out create; reconcile line IDs to avoid duplicate fulfillment.

Rejected or repeated inventory or stock position change

Investigate

Inspect Databricks Proposed inventory or stock position table in Databricks (choose its name) and Deposco Inventory, their IDs, and the destination error.

Next action

Reconcile the latest stock position before retrying; do not apply an old absolute quantity after new movements.

A record type or update is unavailable

Investigate

Check the Databricks and Deposco 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 Deposco

See how each system detects changes and which updates the other system can receive. Each direction has its own permissions and record requirements.

Databricks Deposco 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 Deposco.

Deposco 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 Deposco 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.

Deposco setup checklist

Identify the Deposco account, edition, environment, and business objects the integration must access.
Confirm a Stacksync connector or implementation path for Deposco, 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 Deposco planning worksheet to capture these decisions. Record the access owner in the worksheet and enter credentials only in the connection setup.

Talk to an engineer · Review current pricing

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.

Explore security controls

FAQ