Cutover Weekend

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Module 4 of 5 in the chapter Migration and Synchronization, chapter 10 of the Data Architect course. Each module stands on its own. Previous: Automation During the Load · Next: Getting Data Out: Keeping Another System in Step

The question this module answers: How do you switch over, and prove the migration worked?

It’s Friday night. Users are out, the migration team is online, and someone says, “We’ve tested this three times. Let’s go.” Okay. What exactly are we about to do? When does the old system stop changing? When is the final extract? Who disables the automation, who starts each load? What if we’re two hours behind at midnight, or 3 percent of accounts fail? Who decides whether we continue? How do we prove the data is right, and when can users come back? And if it goes badly: how do we get out? Cutover isn’t “run the migration.” It’s a controlled transition from one known state to another.

Start With the Picture

Here’s our landscape one more time. This is the weekend that amber arrow runs, and then disappears.

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The weekend this arrow runs. On Monday morning, it’s gone.

But more changes that weekend than one arrow. The partners’ manifests stop landing in RouteDesk and start arriving through the integration layer. RouteDesk’s own outbound feeds, Keystone’s nightly status file and Redrock’s webhook, have to come from Salesforce instead. Coordinators route Monday’s deliveries in Field Service, not RouteDesk. And on Monday morning, 330 trucks leave the distribution centers on routes from the new system. Everything in this module is about getting to that Monday safely, and knowing what we’d do if we couldn’t.

Production Should Be Boring

I want production cutover to feel almost disappointing. We’ve already run the process, timed it, seen the failures, tested the bypasses, reconciled the results and practiced restoring normal operation. Production is the same playbook with new data. If we’re inventing the migration on cutover weekend, we’re already in trouble.

That takes a dress rehearsal, not “we loaded accounts in the sandbox.” Beginning to end: a final-style extract, transformations, quality checks, the load sequence, automation and integration controls, reconciliation, deferred processing, the return to normal mode, all timed, at production or representative scale, in a Full sandbox. Record how long each step took, what failed, what needed manual help, what confused people, where we waited, what surprised us. Then improve the runbook. The first rehearsal teaches you how the migration works; the next one should prove you learned. And because module 2 brought the keys back, a rehearsal doesn’t have to start from nothing every time: fix one transformation, reload just the rows it touched, and run the reconciliation again. For RouteDesk, that means the whole weekend in a Full sandbox: the extract, the preparation data store, the waves of module 1, the bypass of module 3, the switch of the partners’ feeds, and the reconciliation, with a stopwatch running.

The Runbook Is Executable Architecture

Not “Saturday morning: migrate data.” Something closer to:

Step	Action	Owner	Expected duration	Evidence / exit criteria
1	Confirm RouteDesk is read-only after Saturday’s last truck	Operations lead	10 min	Written confirmation; time of the last change recorded
2	Final RouteDesk extract into the preparation data store	Data team	45 min	Counts and control totals captured
3	Confirm the bypass on the migration user	Salesforce team	10 min	A test record saves with no status events sent
4	Incremental load: Saturday’s changes, upsert by the partner key	Migration team	30 min	Results and new IDs back in the preparation data store
5	Reconcile against the expected numbers	Data team and operations	30 min	Counts, totals and aggregate reports approved
6	Switch the partners’ manifests and status feeds to the integration layer	Integration team	30 min	One test manifest upserted and one status sent, per partner
…	…	…	…	…
14	Remove the bypass from the migration user	Salesforce team	10 min	Permission set unassigned, and checked

Now we know what happens, who does it, how long we expect, and what proves each step is complete. That’s much stronger than a checklist that says “Load Work Orders.” Notice step 14. Module 3’s bypass is a permission to skip business rules, so taking it away is a runbook step with an owner, not something we hope someone remembers.

Day −7, Day −1, Day 0

I call go-live Day 0: usually a Sunday or another off-hours day. Steve Trucking delivers six days a week, so Day 0 is the Sunday with no trucks on the road. Day +1 is after it; Day −1 and Day −7 are before it. And on any large load, I ask: do I have to wait until Day 0 to load all the data? If the load takes hours and the source keeps changing, I’m going to need an incremental anyway. So:

• Day −7: the initial load. Load the bulk of the data a week early, kept invisible by sharing, so users and reports don’t see it. Smoke-test users (a system administrator) validate it in place. Ask the business what would be hurt by loading early; if the answer is “it would break my reports,” load it hidden. It’s much simpler to open up the sharing on Day 0 than to spend five hours loading a million records and all their children inside the window.
• Day −1: the refresh load. Bring across what changed since Day −7, and protect the starting line: are users still changing the source, are integrations writing, are scheduled jobs running? Freeze the source, make it read-only, or define exactly how the changes after the extract will be captured.
• Day 0: the incremental. Load what changed since Day −1 (sometimes one incremental at the start of the window and a final one at the end), reconcile, decide go or no-go, open the sharing, restore normal operation.

For RouteDesk, Day −7 is the Sunday before: the 190,000 delivered Work Orders go in, hidden by sharing, and smoke-test users check them all week. History barely changes, so most of the load is done before the weekend starts. Day −1 is Saturday night, once the last truck is back: RouteDesk goes read-only, and the refresh brings the week’s changes. Day 0 is Sunday: a small incremental, then the gates.

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Day −7: most of the data, a week early, where nobody can see it yet.

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Day −1: the week’s changes, and the source stops moving.

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Day 0: a small incremental, then the gates.

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Day +1: the first truck leaves, and the rollback clock runs out.

This isn’t an unusual way to cut over; it’s the common one, under other names. Salesforce’s own guidance for moving a B2C Commerce site says to migrate as much data as you can “as close as possible prior to the actual cut-over. Ideally, only the delta from the previous day should be required to copy during the cut-over to minimize the down time” (B2C Commerce Realm Split FAQ (opens in a new tab), Salesforce Help). Microsoft’s guidance for its own platform is a full migration “about a month before go-live, followed by weekly delta loads,” with the final delta “after freezing both source and target systems” (Data migration planning for go-live (opens in a new tab), Microsoft Learn). And AWS’s database migration service builds the same idea in: a full load while it captures changes, then “shut down your applications, let the remaining changes flow through to the target” (Components of AWS DMS (opens in a new tab), AWS; all three checked 2026-10-27). The timing changes with the volume: a month or a week ahead, weekly or daily deltas. The Salesforce twist is the one I add: load early hidden by sharing, so the data is in place and checked, and nobody sees it until Day 0.

Test knowledge. For a large migration with a short cutover window, load most of the data days early, hidden from users by sharing, then load only what changed (a refresh before the window, an incremental inside it). Freeze or capture changes in the source from the extract onward, and validate with test users before opening access.

For the cutover, the point is the gates around those loads. By Day −7 I don’t want to be designing the migration anymore: mappings, transformations, the load sequence, automation decisions, migration identities, reconciliation reports, the runbook, rollback criteria and known exceptions are stable, because the real loads have started. Things can still change, but every late change reduces rehearsal. At some point you stop improving the migration and start protecting the cutover. And know when the source stopped moving, or know exactly how you’ll capture what changed after your extract.

That second half is the watermark: the point through which each extract was current, so the next load knows what changed since. For RouteDesk, the watermark is the moment of each extract: everything RouteDesk changed after Sunday’s Day −7 extract is in Saturday’s refresh. That only works if RouteDesk records when a delivery last changed, so check that it does, and that every DC’s screens update it. If it doesn’t, compare snapshots: extract again and find the differences by key. Slower, but it’s a watermark you can trust. Without one, delta processing is guesswork.

Then Day 0 is execution. Follow the runbook, record actual times, job IDs, counts, errors and decisions. If step 17 fails, don’t jump to step 24 because someone thinks it saves time: understand the dependency, use the recovery procedure, update the state. The team should always be able to say where we are, what’s complete, what’s failed, what’s next, and whether we’re still inside the window.

Lock Users Out When You Need Control

Nobody likes downtime. But pretending the system is available while we’re changing its foundation can be worse, and “we want zero downtime” mustn’t quietly become “we have no idea which version of the data is authoritative.” Sometimes the cleanest strategy is to lock users out, perform the cutover, prove the result and bring them back. How?

• The hard ways: freeze the users, change their login hours, or restrict IP ranges. Don’t deactivate them: that breaks ownership and everything attached to it.
• The soft way: a login flow that lets people log in and drops them in a waiting room: “the system is under maintenance; come back later.” Friendlier than errors that make users think their account is broken. Never put that login flow on the system administrators; if it breaks, nobody gets in.
• Always keep a back door: a set of system administrators (and your smoke-test users) who can still get in.
• Sessions persist. Locking users out doesn’t end sessions already open, so plan how to move people out who are still working: a notice, a deadline, or, with custom work, a way to log them out.

At Steve Trucking it’s Sunday, so the people to plan for are the care agents answering phones, the coordinators getting ready for Monday, and the 300 Retail Partner users who might log in to the portal to check a claim. Login flows are assigned by profile (Custom Login Flows (opens in a new tab), Salesforce Help, checked 2026-10-27), so the partners’ profile can get the waiting room (“your deliveries are moving to a new system; back Monday at 6 a.m.”) while the smoke-test users and administrators come and go.

Don’t make this up the week before. Decide what users will experience, and tell them.

Test knowledge. To keep users out during a cutover, freeze their accounts, restrict login hours or IP ranges, or use a login flow (assigned by profile) as a waiting room. Don’t deactivate users: it breaks ownership and what’s attached to it. Keep administrators and test users able to log in.

Go/No-Go, and the Rollback Clock

At 2:00 a.m., twelve hours in, is not when to invent the go/no-go criteria. Define them before cutover: critical objects reconciled, failure rate under an approved threshold, no unresolved critical data defects, required relationships validated, critical integrations verified, automation restored and tested, performance acceptable, business validation done, enough time left for the downstream steps. Otherwise “go” becomes “we’ve come this far.” That’s not architecture. That’s sunk-cost bias.

“We have a rollback plan.” Good. Until when? We migrate, open Salesforce, and for six hours users create cases, update accounts, enter opportunities, change contact details, while integrations send data elsewhere. Can we restore Friday night? Not without losing legitimate work. Rollback gets harder the longer the new system runs, so define the rollback point: before it, we return to the previous state with the tested procedure; after it, recovery needs a different strategy. Rollback has a clock, and the runbook should show it. For Steve Trucking the clock is easy to read: until Monday’s first truck leaves on a Field Service route, we can still send the coordinators back to RouteDesk, which is why it stays read-only and untouched until then. After that, every delivery made, photo taken and status sent lives only in Salesforce, and going back would lose real work. From that moment, recovery means fixing forward.

Rollback also isn’t just deleting what you loaded. Automation may have created children, integrations may have sent records elsewhere, users may have changed migrated records, files may be attached, events published, existing records updated. Distributed effects are why module 3’s automation and integration controls are cutover architecture.

And what makes you roll back? In my experience it’s rarely the loader. It’s unknown data: you find out something key was missed, or a data feed was running that nobody accounted for. The best rollback plan is not needing it: know all of your key data, and every feed that touches it, before cutover. RouteDesk has two that are easy to forget, because they go out, not in: the nightly status file Keystone picks up, and the webhook that tells Redrock what happened. Retire RouteDesk without moving them, and on Monday night Keystone hears nothing. Nobody loaded the wrong data; a feed just stopped.

Four Ways I Prove the Data

After the load I don’t ask “did the API say success?” I want four kinds of proof, and I use all four: counts, totals, spot checks and aggregate reports.

Counts. How many records should exist, and how many do? Source accounts selected: 500,000. Duplicates consolidated: 20,000. Excluded by approved policy: 5,000. Expected: 475,000. Target: 475,000. Good, but counts alone aren’t proof; you can have the right number of records and the wrong data.

Totals. Where numbers matter, compare control totals: invoice amount, order value, open balance, quantity. Both systems have 100,000 invoices, but legacy totals $412,800,000 and Salesforce $398,100,000. Stop. Totals expose what counts miss.

Spot checks. Look at real records, not just easy ones: a simple record and a complex one, a large customer and a small one, one with many children, one that was transformed, one involved in dedupe, one with unusual history. Trace each from source to target: fields, relationships, ownership, history, External IDs. Averages hide serious individual failures.

Aggregate reports. Compare the shape: accounts by state, orders by year, cases by status, customers by segment, revenue by month. If the source says 82,000 customers in Arizona and Salesforce says 8,200, we have a clue: a state code that didn’t map, a year excluded, a status translated wrong, a file skipped. The row count can look fine while the distribution tells the story.

For RouteDesk, each proof has its check, and each catches something the others miss.

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Each proof catches what the others miss. Use all four.

The aggregate report is the one I’d watch first. Remember module 1’s status codes: nine DCs, nine spellings. If DC 7’s deliveries by week come out 40 percent short against RouteDesk’s, the count for the whole load might still look close, but one DC’s DONE/INST didn’t map.

Test knowledge. Prove a migration with more than “no errors”: record counts against expected numbers set before the load, control totals on amounts that matter, spot checks of real records traced end to end, and aggregate reports that compare the shape of the data. Business owners validate it too.

Two rules go with them. First, set the expected numbers before the load (module 1): source count, approved exclusions, consolidated duplicates, quarantined records, expected target. Then compare and explain the difference. An expected number invented after seeing the result isn’t reconciliation; it’s rationalization. Second, zero errors can still be wrong. Map Billing State into Shipping State and every row loads with no API errors: perfect technical execution, wrong architecture. That’s why business reconciliation matters, not just technical logs.

Business Owners Validate Too

The technical team can prove counts, keys, relationships and load success. Business users recognize what we won’t: “these aren’t our active customers,” “that status hasn’t been used since 2022,” “these accounts belong to the wrong region,” “this customer should have three active contracts.” So business validation is an explicit go-live step, with named people, named reports, named records and a named approval, not “take a look when you have time.”

Authority Moves on Day 0

Why not move everything on Day 0? The Number of Zeros. If the full migration takes 18 hours and the business tolerates 4 hours of downtime, we have a problem, and moving data earlier solves it. History goes in before cutover while the legacy system stays authoritative; the window only has to handle what changed, what couldn’t be preloaded, final relationships, validation and reconciliation. Instead of moving eight years of history during the outage, we’re closing a gap.

But preloaded data needs protection. If production data lands on Day −7, can users see it, edit it, can automation react, can integrations publish it, can agents act on it? The answer should be: not yet. So we keep it hidden by sharing, owned and controlled, with automation and integrations held back (module 3). A preload isn’t “put the records in early.” It’s put them in early without accidentally making the new system authoritative early.

Before cutover, the legacy system is authoritative and Salesforce holds a staged, increasingly current copy. During cutover we close the gap. At the agreed moment, authority moves, and afterward Salesforce is authoritative. For Steve Trucking that moment is Day 0: from then on, a delivery’s truth lives in Salesforce, and RouteDesk is a read-only record of the past. That’s where chapters 6, 9 and 10 meet: identity, authority, movement and cutover aren’t separate problems; they’re views of the same data lifecycle.

So don’t let two systems both think they’re master. If the legacy system stays writable after cutover, a coordinator changes a delivery window in Salesforce, another changes it in the legacy system, and both systems think they own it. Last write wins? Salesforce wins? Human review? If that’s unanswered, we haven’t finished a migration; we’ve created two writable copies. Chapter 9’s rule: technical permission isn’t architectural authority.

Not every migration needs ongoing synchronization. If we’re replacing the legacy system completely, Day 0 comes, the final deltas load, reconciliation passes, users move, the old system goes read-only and is eventually archived or retired. Don’t build a permanent integration because “migrations usually have integrations.” If the old system is dead, stop feeding it. That’s RouteDesk: on Monday it’s read-only, then it’s retired, and nothing syncs with it. The synchronization Steve Trucking does need is a different arrow, Salesforce to Snowflake, designed in chapter 9 and covered in module 5.

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The gap closes, authority moves, and the migration ends. The Snowflake feed goes on.

The opposite happens too. A transition that lasts months, users active in both systems, business units moving at different times: synchronization may have to start before the migration is finished. Now the migration is still moving the historical baseline while synchronization keeps live records consistent, and that’s a much harder architecture. You need to know which process owns which changes, or the load and the sync overwrite each other. So define the boundary visibly: before this point we’re building the target state; after it we’re maintaining consistency. It might be a timestamp, a cutover event, a business-unit transition, an object-by-object switch, a watermark. If nobody can tell me whether a record belongs to the migration process or the synchronization process, we have overlapping ownership, and that’s where records get missed or processed twice.

Back to Normal, Then Hypercare

Restore normal operation deliberately: automation, scheduled jobs, integrations, event consumers, agents, user access, in an order that matters. For Steve Trucking: take the bypass away from the migration user, switch the Change Data Capture subscriber back on from the cutover point (so Snowflake gets Monday’s changes, not the weekend’s load), run module 3’s one backfill, then let the coordinators in. Remember module 3’s catch-up storm: when an integration comes back, what does it think happened while it was off? When users come back, is every critical service actually ready? The return to normal is part of the cutover.

Then we’re live, but not done. For a stabilization period (call it hypercare), watch for migration errors, integration failures, unexpected automation, new duplicates, missing relationships, performance problems, user-reported data issues and reconciliation drift. Migration risk doesn’t disappear the moment users log in.

Then clean up: temporary migration users and permissions, bypass settings, staging data, temporary scripts, migration-only configuration, files holding sensitive source data, temporary integrations, runbook access. Don’t leave migration scaffolding attached to production because “we might need it someday.” Temporary architecture needs an exit plan too. Don’t forget the preparation data store. After RouteDesk, it holds eight years of Recipients’ names, addresses and phone numbers in its raw tables. Keep what the retention policy says to keep, as evidence, then delete the rest, on a date someone owns.

And keep the evidence: the final extract, the transformation version, load files, job results, error files, reconciliation reports, approvals, the runbook with actual times, the go/no-go decision and the exceptions. Three weeks later someone will ask whether a record was wrong before the migration or because of it. You should be able to answer. Trust but verify doesn’t end when the load completes.

AI Can Help Watch the Cutover. It Doesn’t Own Go/No-Go.

AI is useful under cutover pressure: summarizing failures, classifying errors, comparing reports, spotting anomalies, tracking the runbook, surfacing deviations from rehearsal. But go/no-go combines technical evidence, business impact, time remaining, rollback position and risk tolerance. AI can surface the evidence. The accountable humans make the decision.

Beyond the exam: what AI changes. AI can make cutover monitoring and reconciliation faster, especially by finding patterns across large error and validation data sets. Keep explicit human ownership of go/no-go, rollback and accepting unresolved migration risk.

What an Architect Decides Here

• The rehearsal depth, the Day −7 / Day −1 / Day 0 load plan and the rollback point (what loads early, hidden by sharing) and the runbook that makes production cutover repeatable.
• How users are locked out and let back in, with a back door and smoke-test users, and how automation, integrations and agents are controlled meanwhile.
• The go/no-go criteria, the rollback procedure and the point after which rollback stops being the safe recovery, and the unknown data and feeds that must be found first.
• How the migration is proven with counts, totals, spot checks and aggregate reports, against expected values set before the load.
• What business validation, evidence and post-go-live stabilization are required before the migration is complete.

Cutover Is Proof

The scripts ran: not enough. The API returned success: not enough. The row counts matched: still not enough. I want counts, totals, spot checks and aggregate reports, business validation, the system returned to normal, knowledge of whether we’re still inside the rollback window, and evidence of exactly what happened. Production cutover should be boring, because we did the hard thinking before Friday night. Trust but verify. Then let the users back in.

Explore

• Others who cut over the same way: Salesforce Help, B2C Commerce Realm Split FAQ (opens in a new tab); Microsoft Learn, Data migration planning for go-live (opens in a new tab); AWS, Components of AWS DMS (opens in a new tab); all checked 2026-11-01.
• Salesforce Help: Freeze or Unfreeze User Accounts (opens in a new tab) and Custom Login Flows (opens in a new tab) (login flows are assigned by profile), checked 2026-11-01.
• In this course: Master Data and Identity and Integration and Data Movement.