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Reliable SaaS synchronization is built, not switched on. Choose a stable record identity, make every write safe to repeat, save progress as you go, separate temporary failures from invalid data, and monitor whether the two systems actually reconcile. Those controls prevent the most damaging failure mode: a request times out after the destination accepted it, the integration retries, and a second record is created.
This guide presents a vendor-neutral design for one-way and bidirectional syncs, including initial backfills, webhook or polling updates, retries, replay, and operational checks. The exact matching fields, ownership rules, and API behavior still depend on the applications in your pair.
Why SaaS syncs create duplicates or stop midway
A sync has at least three independent failure surfaces: the source application, the integration process, and the destination API. A network timeout can occur after a destination has committed a write but before the integration receives the response. If the integration treats that timeout as proof that nothing happened, it sends a second create request. Rate limits, expired credentials, schema changes, and malformed records can stop a run for different reasons and need different recovery actions.
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Duplicates also arise when two systems have no durable way to recognize the same business object. Names and email addresses can change, be shared by several people, or be reused after an account is closed. Matching on a display value without a uniqueness guarantee turns a routine update into a new create.
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Bidirectional flows add another class of error. If both systems can edit the same field, each update can trigger the other system and create a loop or a last-write-wins surprise. A sync therefore needs an identity decision, ownership rules, repeatable writes, progress tracking, and an observable recovery path before it is allowed to run unattended.
Start with an identity and ownership contract
Choose a durable matching key
For each object type—such as customer, ticket, subscription, or invoice—define the field that proves two records represent the same object. Prefer a vendor-issued ID that is stable in the source and store it as an external ID in the destination. If no shared business identifier exists, create a cross-system mapping table containing the source system, source record ID, destination record ID, and mapping version.
Use an email address, company name, phone number, or other human-readable value only when the applications and your business process guarantee that it is unique, normalized, and immutable. Document what happens when that guarantee fails. A matching field should be indexed or otherwise enforce uniqueness where the destination API permits it.
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After an initial match, retain both record IDs. HubSpot’s data-sync guidance describes this pattern: records are paired using a selected shared property, and subsequent synchronization uses the paired IDs. Its matching workflow also supports filters so that only records meeting defined criteria enter the sync. See HubSpot’s record-matching documentation for the product-specific behavior.
Assign field ownership
Create a field-level ownership matrix before enabling writes. For every field, state which system is authoritative, whether the other system is read-only, and what to do when both values change.
- Source-owned: copy changes from the source and reject or overwrite destination edits.
- Destination-owned: allow the destination to edit and send the resulting value back only if the source is designed to accept it.
- Transform-owned: calculate a value in the integration, with a documented formula and version.
- Shared: define a conflict rule such as source priority, destination priority, explicit review, or a timestamp rule that both APIs can support reliably.
Also decide what deletion means. A source deletion might archive a destination record, mark it inactive, or remove it; do not let an implicit API default make that business decision.
Make every mutating request safe to repeat
Use upsert or a durable idempotency key
An upsert finds a record by a stable external identifier and updates it, creating it only when no match exists. When an API offers idempotency keys instead, generate one key for one logical operation and send that same key on every retry. Generate a new key only for a genuinely new operation.
AWS recommends idempotent mutating operations and warns that inconsistent keys defeat duplicate recognition. It also cautions against timestamps as keys because clock skew and shared timestamp values can produce collisions or fail to identify a retry. Read the detailed guidance at AWS Well-Architected REL04-BP04.
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Shopify’s implementation guidance states that duplicate requests must reuse the same idempotency key for deduplication and concurrency protection. The page documents a 24-hour retention window for the mutations it covers, so an integration must not assume that a key remains recognized forever or that another API uses the same window. See Shopify’s idempotency documentation and verify the retention and scope for your own destination.
Persist the operation before sending it
Store an operation record before the network call, including a generated operation ID, object type, source ID, destination ID if known, payload hash, idempotency key, attempt count, and status. On a timeout, consult that record and retry with the unchanged key and payload. If the destination supports querying by external ID or idempotency key, reconcile the result before deciding to create anything else.
operation = load_or_create(source_id, payload_hash)
if operation.status == "succeeded":
return operation.destination_id
response = send_upsert(payload, idempotency_key=operation.key)
record_result(operation, response)
The exact API syntax varies, but the invariant is constant: a retry must identify the same logical write and must not be interpreted as a new create.
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Use a uniqueness constraint on the external ID where possible. Otherwise, acquire a lock keyed by object type and external ID, or use a compare-and-set operation. A lock should have an expiry so a crashed worker cannot block the record permanently. Keep the idempotency key stable even when a worker is replaced.
Separate the initial backfill from ongoing changes
Prepare and throttle the backfill
An initial backfill has different risks from an incremental sync: it touches every eligible record, consumes API quotas, and can run long enough for credentials or schemas to change. Define the population with explicit filters, estimate the number of pages, and set a controlled concurrency limit. Start with a small batch and inspect the resulting matches before increasing throughput.
- Freeze the matching and field-mapping configuration for the run; record its version.
- Read a page of source records in a deterministic order.
- Resolve each record through the mapping table or stable external ID.
- Send an idempotent upsert, recording the operation before the request.
- Persist the next cursor only after the page’s outcomes are recorded.
- Repeat until the source reports no further page.
Do not run two workers over the same page range unless the destination’s uniqueness and locking controls make that safe. If you need parallelism, partition by a disjoint key range and keep a separate checkpoint for each partition.
Use webhooks when available
Prismatic’s data-sync guidance prefers webhooks for ongoing change notifications when an application provides reliable events. A webhook handler should acknowledge quickly, place the event on a durable queue, and process it asynchronously. Treat event delivery as at-least-once: the same event may arrive more than once, so the handler still needs an event ID or record-level idempotency key.
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Use a cursor for polling
When webhooks are unavailable or incomplete, poll using the strongest cursor the source supports: a monotonically increasing change ID is usually safer than a timestamp. If only timestamps are available, store the last processed value plus an overlap window, then deduplicate by record ID and version. Persist the cursor after each successfully recorded page, not only at the end of the entire job.
Keep a high-water mark and a replay window. For example, a poll can read changes after the stored mark while deliberately re-reading a short interval to catch late-arriving updates; idempotent upserts make those re-reads safe. The exact overlap duration must account for the source’s ordering and clock behavior.
Retry transient failures, not invalid requests
Classify errors before choosing an action
| Error class | Typical examples | Action |
|---|---|---|
| Transient | Timeout, connection reset, temporary service-unavailable response, or rate-limit response | Retry with backoff and jitter, keeping the same idempotency key and payload. |
| Authentication or authorization | Expired token, revoked permission, or invalid signature | Pause the affected connector, refresh or repair credentials, then replay the queued operation. |
| Schema or mapping | Required field missing, invalid enum, wrong data type, or unmapped relationship | Do not retry unchanged. Correct the mapping or record, then replay deliberately. |
| Business-rule rejection | Destination refuses a state transition or duplicate that violates its rules | Route to review with the destination response and source context. |
| Unknown outcome | Client timeout after a mutating request | Query by idempotency key or external ID, then retry the same operation only if the destination confirms it was not applied. |
Use bounded exponential backoff
For a transient response, wait progressively longer between attempts and add random jitter so many workers do not retry simultaneously. Set a maximum delay and a maximum attempt count. Respect a server-provided retry-after value when present, and keep concurrency below the vendor’s documented rate limit. A retry policy without an attempt limit can turn an outage into an endless queue.
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Send persistent failures to a replayable dead-letter flow
After the retry budget is exhausted, place the operation in a dead-letter queue or equivalent review store. Include the source and destination IDs, operation key, mapping version, last response, attempt timestamps, and a redacted payload snapshot. Operators should be able to correct the cause and replay the same logical operation, not copy the record into a new ad-hoc job that can create another duplicate.
Separate poison records from a destination outage. If one malformed record fails repeatedly while neighboring records succeed, isolate it and continue the page. If authentication fails for every record, stop the connector and alert rather than filling the queue with identical errors.
Make checkpoints and concurrency explicit
Checkpoint after durable outcomes
A checkpoint represents work whose outcomes are recorded, not merely work that a worker attempted. For each page or partition, persist the source cursor, counts, and a completion marker in one durable transaction where possible. On restart, resume from the last incomplete unit and reprocess its records safely with the same keys.
Prevent overlapping runs
Use a distributed lease or scheduler lock so only one backfill or incremental worker owns a given stream at a time. The lease needs a heartbeat and expiry. If a worker loses its lease, it must stop issuing writes; a replacement can resume from the checkpoint. Partitioned designs should make ownership visible so two workers cannot claim the same range.
Handle updates that arrive during a backfill
A common pattern is to record a change-watermark before the backfill starts, load records up to that boundary, and then process queued changes after the backfill. This prevents an update made during the initial load from being silently lost. The source must provide a usable change cursor or event stream; if it does not, schedule a reconciliation pass after the backfill and document the residual risk.
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Monitor agreement, not just job status
Log per-run and per-record outcomes
At minimum, record the run ID, connector and object type, mapping version, start and end times, source cursor range, and counts for scanned, matched, created, updated, skipped, retried, and failed records. Include latency, rate-limit responses, and the oldest queued operation. Never place access tokens or unnecessary personal data in logs.
Alert on symptoms that predict data drift
- Cursor progress stops while source changes continue.
- Retry counts or dead-letter volume rises above its normal baseline.
- Creates spike relative to updates, suggesting matching failure.
- The same external ID appears in more than one destination record.
- Webhook queue age exceeds the business freshness target.
- Reconciliation finds missing, extra, or conflicting records.
Run reconciliation independently
A green job status means the worker completed its code path; it does not prove that both applications contain the same data. On a schedule appropriate to the business, compare counts and selected business keys in both systems. Sample important fields, check that every mapping points to an existing destination record, and investigate records changed during an outage. Any repair script must use the same upsert and idempotency rules as the primary sync.
For high-value objects, maintain an audit trail of field-level changes and the operation that caused them. That makes it possible to distinguish a legitimate user edit from an integration replay.
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Choose an implementation approach by capability
There is no universally correct sync configuration. Compare a managed connector, an integration platform, and custom code against the controls your data requires rather than against a feature-count list.
| Decision axis | Questions to answer | Why it matters |
|---|---|---|
| Application coverage | Are both source and destination supported, including the required object types and relationships? | An unsupported edge case often becomes an unsafe custom workaround. |
| Directionality | Is the flow one-way, bidirectional, or separate by object and field? | Bidirectional writes require explicit ownership and loop prevention. |
| Matching and mapping | Can you select a stable identifier, retain paired IDs, normalize values, and filter records? | Weak matching is the leading route to duplicate creates. |
| Load controls | Does it distinguish initial load from incremental changes and persist cursors or checkpoints? | Long runs must resume without restarting or skipping records. |
| Retry and replay | Can it preserve an idempotency key, classify errors, and replay dead-lettered operations? | Blind retries can duplicate writes; no replay path leaves failures stranded. |
| Rate and concurrency limits | Can you set worker limits, honor retry-after responses, and prevent overlapping runs? | Uncontrolled concurrency causes throttling and race conditions. |
| Observability | Are cursor progress, per-record outcomes, audit data, and reconciliation available? | Completion alone cannot demonstrate agreement. |
| Ownership effort | Who will maintain mappings, credentials, schema changes, alerts, and recovery procedures? | The least expensive build is not the least expensive system to operate. |
These are capability criteria, not a tested ranking of vendors. Validate the exact behavior against current documentation and a sandbox containing duplicate-like records, edits on both sides, rate limits, and forced timeouts.
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“A retry created a second record”
- Stop further creates for the affected object type.
- Search the destination by external ID, idempotency key, and any legacy mapping.
- Determine whether the first request committed and whether the second record is a true duplicate.
- Merge or archive according to the business owner’s rule, preserving an audit record.
- Enable upsert or stable idempotency keys before restarting; do not simply increase the retry delay.
“The job succeeds, but records are missing”
Check whether the source filter excluded them, whether the cursor advanced before outcomes were persisted, and whether a webhook was dropped during the run. Compare source and destination counts for the same time boundary, then replay the missing range using the existing mapping and operation keys.
“Every record is failing”
Look first for a shared cause: expired credentials, revoked scopes, an API-version change, a destination outage, or a mapping deployment that changed a required field. Pause retries, fix the connector-wide issue, and replay the queued operations. Retrying thousands of identical invalid requests only increases load and obscures the original error.
“One record fails forever”
Move it to the dead-letter flow, inspect the destination’s validation response, and correct the source data or mapping. Re-run that operation with the same logical key after the correction. If the record is intentionally out of scope, mark it skipped with a reason rather than deleting the error history.
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Implementation checklist
- Stable matching field or durable source-to-destination mapping exists for every object.
- Uniqueness is enforced where the destination supports it.
- Field ownership, conflict, and deletion behavior are documented.
- Creates use upsert or an idempotency key that remains unchanged across retries.
- Operation state is persisted before each mutating request.
- Backfill and incremental paths have separate controls and checkpoints.
- Webhook events or polling cursors are durable and replayable.
- Transient, authentication, schema, and business-rule errors have different actions.
- Concurrency, rate limits, lease expiry, and retry budgets are bounded.
- Dead-lettered operations include enough context for safe correction and replay.
- Logs report scanned, created, updated, skipped, retried, and failed records.
- Reconciliation checks counts, mappings, and critical fields independently of job status.
Frequently asked questions
Should I use a timestamp as an idempotency key?
No. Timestamps can collide when requests share a clock value and can differ because of clock skew. Use a generated key that is stored with the logical operation and reused for every retry. AWS discusses this risk in its idempotent-operation guidance: https://docs.aws.amazon.com/wellarchitected/latest/framework/rel_prevent_interaction_failure_idempotent.html.
How long should I retain idempotency keys?
Retain them for at least the maximum period in which a retry or replay of that operation can occur, subject to the destination API’s rules. Retention is vendor-specific; for the mutations covered by its documentation, Shopify describes 24 hours. Do not apply that period to another API without confirming its contract.
Are webhooks enough to guarantee delivery?
No. Webhooks reduce polling delay but can be duplicated, delayed, rejected, or unavailable during an outage. A durable queue, event deduplication, retry handling, and a periodic reconciliation process are still required.
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Do not leave the result to arrival order. Assign an owner or define a deterministic conflict rule, record the losing value and operation, and send ambiguous cases to review. The correct rule depends on the business meaning of that field and the guarantees exposed by both APIs.
Frequently Asked Questions
Should I use a timestamp as an idempotency key?
No. Use a stored generated key for the logical operation; timestamps can collide or differ because of clock skew.
How long should I retain idempotency keys?
Follow the destination API’s contract and retain keys for the full possible retry or replay period. Retention is vendor-specific; one documented Shopify mutation set retains them for 24 hours.
Are webhooks enough to guarantee delivery?
No. Use durable queuing, event deduplication, retries, and periodic reconciliation in addition to webhooks.
What should happen when both applications edit the same field?
Define field ownership or a deterministic conflict rule before syncing, and route ambiguous conflicts to review.
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