Migration and Synchronization

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Chapter 10 of Data Architect, Part 2: Architecting at Scale. Previous: Integration and Data Movement.

Migration is where data architecture meets reality. Every modeling, quality, master-data and volume decision we’ve made gets tested the day millions of records arrive at once. And a migration that “loaded successfully” can still be a failure: wrong owners, broken relationships, duplicated customers, a million welcome emails, locked records, or a cutover weekend that runs out of time.

Image generated with AI.

Every CTA scenario has a legacy system to bring across, and the candidate has to say how the data comes in, how it’s cleaned and how it’s de-duplicated. This chapter is that answer, from the source extract to the moment the old system stops mattering, and then the copies that keep going.

One example runs through all five modules: Steve Trucking retiring RouteDesk, its old dispatch tool, with eight years and about 5 million deliveries. What comes into Salesforce, how it’s prepared, how it loads, what the automation does about it, and the weekend it all happens. Then the other direction: Salesforce, now the system of record, keeping Snowflake’s copy in step.

The ideas in this chapter

• Migration is a quality project first, and a migration is an integration with an end.
• Migrate, archive or discard, and know why every class of data moves, or doesn’t.
• Prepare outside, then load the result: a preparation data store, the raw source kept, no step that loses fidelity, and three sets of keys (the source’s, the transformations, Salesforce’s).
• The model sets the load order, and every child finds its parent by a key, never by a name.
• The Bulk API is the loading dock behind the bank, and the dock still needs a plan: insert before upsert, sort by parent, the load user, and the IDs brought back.
• Any time you turn automation off, you own that business logic not happening. The bypass is part of the original design, tested on and off and deployed long before it’s needed.
• Production cutover should be boring: rehearsed, run from a runbook, loaded early and hidden by sharing, with go/no-go and a rollback clock decided in advance.
• Prove it four ways: counts, totals, spot checks and aggregate reports.
• Getting data out isn’t leaving Salesforce. Migration has a defined start and end; synchronization is ongoing, and the mirror is never the master.

The path through this chapter

Five modules, each standing on its own.

1. Migration Is a Quality Project First. RouteDesk on the landscape; migrate, archive or discard; the preparation data store; Steve’s five steps; loading by keys; merged records and their children; three sets of keys.
2. Loading at Volume. The teller window and the loading dock; Bulk API 2.0 and 1.0; insert, update or upsert; sorting by parent; the load user; bringing the keys back; rehearsing the whole run.
3. Automation During the Load. The arrows a load sets off; don’t need it, simulate it, defer it; what a bypass looks like in Apex, flows and validation rules; sharing; the automation inventory.
4. Cutover Weekend. The runbook; Day −7, Day −1, Day 0 and the rollback point; locking users out; the four proofs; authority moves on Day 0.
5. Getting Data Out: Keeping Another System in Step. Salesforce stays the system of record; Snowflake needs a mirror. The ways out, PK chunking, keeping it current, a copy once or a copy forever.

What an architect decides here

• What moves at all: migrate, archive or discard, and why.
• How the data is made fit before it arrives, and how every record stays traceable to its source.
• The load plan: sequence, keys, concurrency, identity and ownership.
• What the load is allowed to trigger, and how normal operation comes back.
• How the cutover is run and proven, and when rollback stops being an option.
• Where migration ends, what keeps another system’s copy in step afterward, and who owns each value.

Beyond the exam: AI speeds up the analysis half of a migration: profiling, mapping, anomaly detection, writing the scripts. Use it step by step, with every generated command tracked. Validation, reconciliation and go/no-go stay deterministic and human-owned. Each module ends with what AI changes.

Explore

• Salesforce, General Guidelines for Data Loads (opens in a new tab) and Bulk API and Bulk API 2.0 Limits and Allocations (opens in a new tab), checked 2026-11-01.
• Salesforce, Best Practices for Deployments with Large Data Volumes (opens in a new tab), Winter ‘27, checked 2026-11-01.
• The Integration Architect course: Bulk (coming soon).

Next: Transactions, Concurrency, and Reliability