The Tool Desk
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What a parallel migration actually does
In a parallel migration, the old (source) and new (target) systems operate during an overlap period. Data is copied or replicated, users and integrations are tested against the target, and the organization gathers evidence before changing traffic. The source may remain the only writer, or a carefully designed dual-write process may be used. The choice determines consistency, monitoring effort, and rollback complexity.
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CDC’s NBS 7.13 readiness guidance describes running both systems during a transition; the duration depends on deployment pace and local decisions. There is no platform-neutral “normal” number of days. Set an end date, purpose, and exit criteria so parallel operation does not become an expensive permanent state.
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Decide scope, ownership, and success criteria first
Inventory what will move
Create a migration register for every entity, interface, workflow, and report in scope. Record the source table or object, target location, owner, retention requirement, legal or geographic constraint, migration method, and validation method. Include data that is easy to overlook:
- Reference and code tables used by applications or reports.
- Attachments, audit trails, history, and soft-deleted records.
- Queues, scheduled jobs, webhooks, exports, and downstream integrations.
- Users, roles, service accounts, secrets, and environment-specific settings.
- Data partitions, date ranges, tenants, and records excluded by policy.
CDC planning material frames modernization as governance work as well as technical work. Assign a business steward for each important data domain and an accountable owner for each interface. The owner must be able to approve a mapping, accept a discrepancy, or stop the migration.
Document mappings and transformations
For each field, specify its source, target, data type, null behavior, default, transformation, and validation rule. Explicitly document:
- Time zones, daylight-saving handling, and timestamp precision.
- Null, blank, zero, and “unknown” semantics.
- Code-set conversions, enumerations, currencies, and units.
- Identifier generation, cross-system identity maps, and key stability.
- Duplicate detection, merge rules, and deleted-record treatment.
- Rounding, truncation, character encoding, and locale rules.
Have a domain owner sign off mappings before loading production data. A technically successful transform can still be wrong for the business if, for example, a closed account is interpreted as active or a local date is shifted into the next reporting period.
Assign a writer for every phase
Write down which system is authoritative for each entity during initial load, parallel operation, cutover, and rollback. A single authoritative writer with replication is generally easier to reason about than uncontrolled writes in both systems. If different entities have different owners, document that explicitly and define ordering for cross-entity transactions.
Choose the operating model
| Model | How it works | Strengths | Main risks and requirements |
|---|---|---|---|
| Source-only writes with replication | Users and integrations continue writing to the old system; changes flow to the target through change data capture, exports, APIs, or another replication mechanism. | One authority for business transactions; simpler reconciliation and rollback before target writes. | Replication lag, failed records, schema incompatibilities, and a target that may not exercise every new-system workflow. |
| Target shadow/read-only use | The target receives current data but is used for reports, simulations, or selected read paths without accepting authoritative writes. | Allows realistic validation while protecting write consistency. | Read results may differ because of semantic or calculation changes; users may mistake shadow access for production readiness. |
| Application dual-write | Application logic submits each transaction to both systems, with status tracking and retry or compensation behavior. | Can keep both stores current when a long overlap is required. | Partial success, ordering, idempotency, latency, and conflict handling become application responsibilities. Every write path, batch job, and integration must be covered. |
| Target becomes writer before final cutover | The target accepts production transactions while the old system is retained for recovery or reference. | Exercises the target under real load. | Rollback must preserve or replay target transactions; redirecting traffic to a stale source can lose data or split the record of truth. |
Do not allow independent writes to both systems without conflict rules. If a dual-write request succeeds in one system and fails in the other, record the outcome, retry safely with an idempotency key, and expose the exception to operations. “Both calls returned 200” is not a reconciliation strategy.
Define reconciliation before moving data
Agree on the reconciliation unit and acceptance rules before the first load. A unit might be a tenant, account, order, partition, date range, or complete database. Use several layers because no single test proves equivalence.
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| Layer | Checks | What a failure suggests |
|---|---|---|
| Completeness | Entity and record counts; partition and date-range coverage; missing, extra, and deleted records. | Missed extract window, filter error, replication lag, or incorrect delete handling. |
| Integrity | Checksums or hashes where the serialized representation is comparable; field-level comparison for critical values. | Transformation, encoding, precision, or truncation defect. |
| Relationships | Foreign-key and reference validity, duplicate keys, orphan records, and identity-map coverage. | Load ordering issue, key collision, missing reference data, or bad merge rule. |
| Business meaning | Totals, balances, statuses, entitlements, inventory, and other domain invariants; equivalent outcomes for representative workflows. | Schema values match syntactically but business rules differ. |
| Operational behavior | Transaction success and error rates, latency, queue depth, interface delivery, and retry behavior. | Target capacity, configuration, integration, or performance problem. |
Microsoft Azure migration guidance describes row counts as a quick check and checksums or hash functions as a deeper comparison, alongside functional workload checks. Choose tolerances by business and risk requirement: a one-record difference may be unacceptable for a ledger, while a small delay in an analytics feed may be expected. Do not apply one percentage to every table or transaction class.
Make discrepancies actionable
Every reconciliation report should identify the comparison window, source and target snapshot times, keys used, transform version, and status. Classify each mismatch:
- Expected: documented behavior such as a generated target key or intentionally excluded history.
- Repairable: a failed record, mapping error, or retryable transformation that has a defined owner and deadline.
- Blocking: an unexplained financial difference, broken relationship, missing critical customer, security mismatch, or failed business workflow.
Store samples of mismatches, not only aggregate counts. A report showing “12 records differ” is not sufficient for a go/no-go decision unless the records, cause, owner, and disposition are traceable.
Rehearse with representative data
Run the migration in a lower environment that resembles production in schema, volume, integrations, permissions, and batch schedules. Measure extract time, load time, replication lag, reconciliation runtime, backup duration, and the time required to restore or reverse traffic.
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- Take source and target snapshots and record versions, configuration, and time zones.
- Execute the initial load and every incremental or change-capture step.
- Run completeness, integrity, relationship, business, and operational checks.
- Exercise interfaces, reports, scheduled jobs, and representative user workflows.
- Capture failures, mismatches, lag, throughput, and operator actions in the runbook.
Fix mapping, capacity, permissions, and sequencing defects rather than simply marking them as known issues.
Second dry run: prove the corrected procedure
Microsoft Business Central guidance recommends at least two dry runs: the first to discover technical issues and measure timing, and the second to confirm fixes and perform business validation. Repeat the procedure after corrections, involve business users, and require them to sign off on the scenarios they own. If the second run cannot meet the planned window or reconciliation gates, production is not ready.
Retain evidence for each rehearsal: runbook revision, source and target snapshots, counts, mismatch samples, defect decisions, elapsed times, approvals, and the person who made each decision.
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Operate the parallel period under control
Publish a daily operating procedure for the overlap. It should state who can change schemas, mappings, jobs, or replication settings; how failed records are retried; and when reconciliation runs.
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- Replication state, last successful checkpoint, and end-to-end lag.
- Extract, transform, and load failures by entity and error type.
- Rejected, quarantined, duplicated, and retried records.
- Queue depth, throughput, CPU, storage, connection limits, and rate limits.
- Drift in counts, hashes, critical fields, relationships, and business totals.
Alert on a condition that requires action, not merely on a process that is still running. Keep an auditable exception queue with an owner, severity, retry history, and resolution.
Keep users and integrations aligned
Tell users which system is authoritative and which functions are available in the target. Disable or redirect integrations that would otherwise write to the wrong system. Test scheduled jobs and external consumers during the overlap; a migration can appear correct in the database while invoices, notifications, exports, or analytics continue using the old endpoint.
Define the end of parallel operation in advance. Exit criteria commonly include stable lag, no unresolved blocking discrepancies, successful critical workflows, verified access controls and monitoring, staffed support, and business-owner approval. These are planning gates to tailor to the system, not universal thresholds.
Use a gated cutover runbook
AWS Prescriptive Guidance outlines a broadly applicable sequence: freeze ingestion or writes as needed, take a final backup, complete the final data sync, and route users to the target. Adapt the order to your architecture and write an exact command, owner, expected result, and abort condition for every step.
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- Announce the window. Confirm stakeholders, support coverage, user communications, maintenance banners, and the decision-maker with authority to proceed or stop.
- Enter the freeze. Stop new writes, pause ingestion, or lock the source if the consistency requirement demands it. Record the start time and verify that queues and in-flight transactions are drained or explicitly handled.
- Back up and verify. Create the final source backup and any target recovery point. Confirm that each backup is restorable, not merely present.
- Run the final sync. Apply the last change set, verify checkpoint completion, and investigate every failed or late record.
- Run cutover reconciliation. Compare counts, hashes or critical fields, relationships, business totals, and required workflows against the agreed gates.
- Validate the target runtime. Check permissions, secrets, certificates, connection strings, monitoring, alerts, scheduled jobs, and integration endpoints.
- Route traffic. Change the load balancer, DNS, service-discovery record, application configuration, or equivalent routing control. Record the exact old and new values.
- Perform smoke tests. Execute login, read, create, update, search, report, and integration scenarios for the highest-risk paths.
- Release the freeze deliberately. Re-enable writes only after the target passes the smoke tests and the business owner gives the recorded go decision.
If source locking is required, include the resulting downtime in user and recovery communications. Do not start a final sync while untracked writes are still entering the source.
Define decision gates before the window
At each checkpoint, record evidence and a named approver. A go decision normally requires replication lag within the agreed limit, no unresolved critical reconciliation discrepancy, passing critical transactions and interfaces, working access controls and monitoring, available support, and business-owner sign-off. If any gate fails, the runbook must say whether to pause, repair and retry, continue with a documented exception, or abort.
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Design rollback for the state after cutover
Rollback is not one action; it depends on whether the target has accepted new transactions. AWS notes that, when no data has changed, rollback can be as simple as resuming the on-premises instances and redirecting traffic by changing load-balancer or DNS configuration.
Before target writes
If the target is read-only and no authoritative writes occurred there, preserve the evidence, restore routing to the source, and resolve the failed gate. Confirm that source queues, locks, and integrations are healthy before reopening writes.
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After target writes
Do not point users back to a stale source. First account for every target transaction. Viable patterns include:
- Fail-forward database: maintain replication or another mechanism that keeps a recovery environment current, then repair the target while users remain on the target or move forward to a corrected environment.
- Application dual-write during recovery: write new transactions to both systems with idempotency, ordering, conflict handling, and a monitored exception queue.
- Backup and restore: restore a tested recovery point and replay target transactions from durable logs or an export, accepting the recovery-point and recovery-time objectives this entails.
AWS identifies these categories, including a fail-forward database maintained through replication, application-level dual writes, and native backup and restore. Select one that fits transaction volume, regulatory retention, recovery objectives, and platform support, then rehearse it in a lower environment.
Validate and monitor after traffic moves
Routing traffic is a milestone, not completion. Run the same reconciliation layers after cutover, using a clearly stated snapshot window. Confirm:
- Critical reads and writes, calculations, statuses, and permissions.
- Reports, exports, notifications, queues, and downstream integrations.
- Backups, restore points, audit trails, retention, and security monitoring.
- Error rates, latency, capacity, user activity, and support tickets.
- That old-system writes are disabled or intentionally controlled.
Microsoft Azure guidance recommends comprehensive post-cutover validation and health monitoring. Its migration examples discuss an initial 24–48-hour observation period; treat that as context-specific guidance, not a universal requirement. Set a longer or shorter watch period according to transaction volume, business cycles, reporting deadlines, and the time needed to detect delayed jobs.
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Troubleshoot common failure patterns
Counts match, but totals do not
Compare field-level values for critical records, decimal precision, currency and time-zone conversions, rounding, and excluded or soft-deleted rows. Then run the business calculation in both systems and trace one discrepant record end to end.
Replication lag keeps growing
Check source change volume, capture checkpoints, network throughput, target write capacity, throttling, locks, and failed-record retries. Reduce nonessential load only under an approved change; never hide lag by widening the reconciliation window without recording the decision.
Duplicate or orphan records appear
Verify key generation and identity-map uniqueness, load reference data before dependent entities, and make retries idempotent. Quarantine duplicates for business disposition instead of deleting them blindly.
Dual writes diverge
Find transactions that succeeded in only one system, preserve request and idempotency identifiers, replay safe operations, and route irreconcilable cases to an owner. Do not “fix” a mismatch with an ad hoc manual edit that bypasses the audit trail.
Users report that the target is correct but integrations fail
Trace endpoint configuration, credentials, certificates, firewall rules, payload versions, queue consumers, and scheduled jobs. A database comparison cannot validate an interface that was never exercised.
People, evidence, and tools you need
A controlled migration needs more than a database administrator. Assign at least these responsibilities, even if one person holds several:
- Migration lead: owns the plan, dependencies, runbook, and schedule.
- Data owners: approve mappings, tolerances, and discrepancy dispositions.
- Application and integration owners: test workflows, interfaces, and dual-write behavior.
- Operations and security: monitor capacity, access, backups, alerts, and incident response.
- Business decision-maker: gives the recorded go/no-go and rollback decision.
Keep a versioned evidence set containing mappings, transformation code, runbooks, snapshots, reconciliation outputs, mismatch samples, approvals, timing, incident records, and final sign-off. For teams lacking internal expertise in mapping, rehearsals, reconciliation, or cutover planning, specialized migration consulting can fill those gaps after the source and target stack, geography, and regulatory obligations are understood.
A database migration service or continuous database replication product may help maintain a fail-forward copy in an appropriate platform and database combination. AWS specifically names Database Migration Service as one possible way to maintain such a database; verify supported engines, features, regional availability, costs, and current limits before selecting it. A tool does not decide write ownership, business tolerances, or rollback policy.
A practical go/no-go checklist
- Every in-scope entity, interface, owner, retention rule, and mapping is documented.
- Write authority is explicit for initial load, parallel operation, cutover, and rollback.
- Reconciliation units, comparison windows, tolerances, and blocking conditions are approved.
- At least two representative rehearsals produced timing and discrepancy evidence.
- Replication lag, failed records, retries, and drift have active monitoring and owners.
- Final backup and restore procedures were tested, not merely scheduled.
- Cutover steps, commands, checkpoints, communications, and decision authority are in the runbook.
- Rollback accounts for transactions accepted by the target after cutover.
- Critical workflows, integrations, access controls, reports, and alerts passed validation.
- Support staffing and a defined post-cutover observation period are in place.
The safest parallel migration is a governed experiment with an explicit end: one accountable write model, evidence-based reconciliation, rehearsed cutover gates, and a rollback path that remains data-aware after the target begins accepting transactions.
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