Dual-Write CRM Sync Extensions

Standard dual-write ships with a fixed set of table maps: customers, contacts, vendors, products, price lists, sales orders, invoices and the reference data they depend on. That set covers a clean out-of-the-box deployment and very little else. The moment your business puts real fields on a customer record, tracks a service agreement or an installed equipment base, prices from a custom structure, or wants the sales team to see credit and delivery status that lives in Dynamics 365 Finance and Supply Chain Management, you are outside what the standard maps carry.

The second problem shows up later. Dual-write is a synchronous, transactional integration: when a row fails validation on the far side, the write fails, the error lands in a log, and unless somebody is watching that log the two systems quietly drift apart. Teams usually discover the drift weeks afterwards, when a salesperson quotes against a credit limit that was changed in F&O and never arrived in Dataverse.

ECOSIRE builds the extension layer that fixes both. This is not a product you download; it is a scoped build against your entity model, your field list and your conflict rules, delivered as an X++ extension package plus a Dataverse solution. Typical lead time is one working week from signed quote.

Extending the map set

We start from the entities you actually need on both sides and work backwards to what F&O can expose. For each map we define the F&O data entity, the Dataverse table, the key that joins them, the direction of flow, and the row filters that keep the map narrow enough to run fast. Where a standard entity is close but incomplete, we extend it with X++ extension classes and Chain of Command rather than overlayering, so your environment stays upgradeable. Where nothing suitable exists, we build a new public data entity with a proper EntityKey, correct mandatory-field definitions and the field groups the map needs.

Reference data comes first, because it is where most dual-write projects fail. Currencies, units of measure, payment terms, terms and modes of delivery, sites and warehouses have to line up before a transactional map has any chance of succeeding. Number sequences and the company table are configured so records land in the correct DataAreaId and a legal-entity-scoped row cannot be written into the wrong company.

Transformation and a declared source of truth

Real integrations are never a straight field-for-field copy. We implement value maps for enumerations that do not match, concatenation and splitting where one side keeps a single field and the other keeps three, lookup resolution where a code has to become a reference, and default handling so a blank does not overwrite a good value.

Every mapped field gets a declared source of truth. That decision is written into the mapping specification and enforced in code, which is what stops the classic ping-pong loop where each system overwrites the other and both audit trails fill with meaningless updates. Where a field genuinely has to be writable on both sides, we implement a change-detection guard so an echo of your own write does not trigger a further sync.

Error handling and replay

The replay workspace is the part customers notice most. Instead of asking a support analyst to read the raw dual-write error log and reconstruct what happened, F&O gets a workspace listing failed rows with the payload that was attempted, the error text, the map and the correlation identifier, plus a re-queue action. Rows can be retried individually or in bulk once the underlying data problem is fixed, and retries are counted so a row that keeps failing is escalated rather than looping forever.

On the Dataverse side we register plugin logic on the dual-write pipeline only where it is genuinely needed — validation that must run before the write, or a transformation that cannot be expressed on the F&O side — and keep that logic thin and traceable through the plugin trace log.

Monitoring and drift detection

Business events are raised when a map stops, when the error count in a window crosses a threshold, or when a row exceeds its retry limit. Those events drive Power Automate alerts into the channel your support team actually reads. Application Insights instrumentation carries the same correlation identifier used in the replay workspace, so a single failure can be traced across both platforms.

Because a synchronous integration can still drift after a restore, a maintenance window or a bulk data operation, we build a reconciliation job on the batch framework that compares record counts and key field values between F&O and Dataverse on a schedule and reports the differences. Finding drift on a Monday morning report is a very different experience from finding it in a customer meeting.

Initial sync and cutover

Initial sync is where large environments get into trouble: an unfiltered backfill of a big table will throttle, time out and leave a partial state. We configure filters, page sizes and run order, sequence reference data ahead of transactional data, and rehearse the full backfill in your sandbox with production-shaped volumes before anything touches production. You receive a cutover runbook with the order of map enablement, the checks after each step, and the rollback position.

Who this is for

Organisations running Dynamics 365 Finance and Supply Chain Management alongside customer engagement apps on Dataverse, where the standard map set no longer covers the business. It fits best when custom or heavily extended entities are in scope, when sync failures are already causing visible data quality problems, or when a support team needs to resolve integration errors without a developer. If your requirement is analytics rather than operational sync, an export to a data lake is usually the better and cheaper answer, and we will say so on the call rather than after the quote.

How delivery works

1. Scoping call. We review the entities and fields in scope, direction of flow, volumes, your current dual-write state, environment topology and versions, and the failures you are living with today. 2. Fixed quote. You receive a written mapping specification — entity by entity, field by field, with transformations and source of truth — and a fixed price and delivery date against it. 3. Build. Development runs against your target application and platform version. The F&O side is an extension model with no overlayering; the Dataverse side is a solution you can inspect before it is imported. 4. Install in test. We deploy the package through your LCS release pipeline into a sandbox, import the solution, run initial sync at real volumes, and prove the maps under load and under deliberate failure conditions. 5. Production. The same artefacts move to production on your release cadence, following the cutover runbook, with your sign-off at each gate. 6. Support. A defect-fix warranty window follows go-live, with an optional ongoing arrangement so maps are re-tested against each service update you take.