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Keeping Another System in Step

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第 5 个模块，共 5 个，属于 Data Architect 课程第 10 章迁移与同步。每个模块都可以单独阅读。 上一篇：切换周末 · 下一篇：下一章事务、并发与可靠性

The question this module answers: How do you get a copy of Salesforce data into another system, at volume, and keep it current? ⟲

So far we’ve talked about getting data into Salesforce. Data architects also have to get data out. Not to leave Salesforce: because the data lives in Salesforce, and another system needs a copy of it. A warehouse such as Snowflake for reporting and analytics, an archive or a backup, another Salesforce org, or a sandbox with realistic data for a rehearsal. The Number of Zeros works in reverse too: 500 records out is easy; 50 million out is architecture. And the copy is rarely a one-time thing. Usually the other system wants it today, and again tomorrow, and every day after that. ⟲

Start With the Picture

Section titled “Start With the Picture”

Here’s Steve Trucking’s landscape after cutover. RouteDesk is retired. Salesforce is the system of record for every delivery. And one arrow carries the status history out of Salesforce, through the integration layer, into Snowflake, where leadership’s five-year reports and the analysts live. ⟲

The mirror that never ends Steve Trucking's landscape after cutover, in the same places: people on the left, Salesforce with Field Service in the middle, the integration layer to its right, and the systems around it, with RouteDesk retired and its history in Snowflake. Two arrows are picked out in amber, the mirror this module zooms in on: status events from Salesforce to the integration layer, and status events from the integration layer to Snowflake. Underneath: after cutover, Salesforce is the system of record; Snowflake keeps a mirror of its status history, every day, with no end date. {"author": "Steve Simpson", "blueprint": "1.0.1", "builder": "standard 1.54.0", "built": "2026-10-30", "id": "DGM-DA-MG-50", "rights": "\u00a9 2026 Twinshark LLC. All rights reserved.", "template": "system-landscape 0.13.0", "theme": "stevetecharc 1.4.0"} ABOVE: IDENTITY, APPEXCHANGE, WEB PEOPLE RIGHT: INTERNAL SYSTEMS BELOW: CUSTOMER CHANNELS BOTTOM RIGHT: PARTNERS AND PROVIDERS Dispatch coordinators 45, inside 9 partner DCs Delivery crews ~700 on 330 trucks Care and office staff 100: service, ops, finance Retail Partner users ~300 at 3 partners Recipients ~1 million a year Corporate IdP SSO for office staff only Retail Partner portal Tracking page INTEGRATION Snowflake status history, analytics FleetLink raw GPS stays here Finance ERP invoices partners, pays crews Photo storage 4–5 million a year, off core RouteDesk retired; its history in Snowflake Phone care agents Email email-to-case SMS and email provider day-before message, with a link Redrock REST API, webhooks Keystone CSV over SFTP Brightwave EDI through a third-party network Salesforce Field Service, one US org a link, no login office staff only calls cases in manifests in status out manifests in status out manifests in status out upserts, partner key status events status events latest position only delivery counts delivery counts photos, from the crews' app day-before, with a link Salesforce a community people identity, apps, web an internal system a customer channel the integration layer starts at the system that opens the connection; the label says what moves a shared certificate: trust, no direct call the mirror we zoom in on After cutover. Salesforce is the system of record; Snowflake keeps a mirror of its status history, every day, with no end date. © 2026 Twinshark LLC · stevetecharc.com
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Salesforce keeps the data. Snowflake keeps a mirror of it, every day. ⟲

Salesforce isn’t going anywhere. Snowflake doesn’t replace it; Snowflake needs a copy of part of it, kept in step. That’s what getting data out is for, and it’s two problems in a row: the first copy, which can be big, and every copy after it, which has to be current. ⟲

Why You’d Pull a Lot Out

Section titled “Why You’d Pull a Lot Out”

The first copy is the big one. When Snowflake starts mirroring status history, it needs everything so far, not just today’s changes. At Steve Trucking that starts right after cutover: module 3 deferred the status history of RouteDesk’s 196,400 migrated Work Orders to “one backfill after the load.” That backfill is the first big extract out of Salesforce. And as the business grows, every new mirror, every new report store, every rebuild after a problem starts with a copy of everything. ⟲

Other reasons bring you here too: an archive for records that leave the core (chapter 7), a backup you can restore from, another Salesforce org that needs a slice of the data, a sandbox with realistic data for a rehearsal. ⟲

也不要因为能导出就导出一切。八年的历史、大文件、过时的记录、临时数据、派生值、已归档的交易：要理解每一类数据为什么要被提取。每一条记录都有成本：时间、存储、转换、移动、验证、对账、安全，以及下游的保留义务。“导出整个 org”并不自动就是一种架构。

出去的路

Section titled “出去的路 ”

Salesforce 给了你好几个出口，它们适合不同的工作。

  • Data Export Service. Setup’s Data Export produces a zip of CSV files for the whole org, weekly (Enterprise, Performance and Unlimited Editions) or monthly (all editions except Database.com), on demand or on a schedule. You can run it manually once every 7 days (weekly) or 29 days (monthly). Professional and Developer Editions get the monthly export only. The files are deleted 48 hours after the email is sent, not counting weekends; formula and roll-up summary fields are always excluded; and whoever has the Weekly Data Export permission can see all exported data (Export Backup Data from Salesforce （在新标签页中打开）, Salesforce Help, checked 2026-10-27). It’s a backup-shaped snapshot, not a migration pipeline. ⟲
  • **Data Loader 和报表。**Data Loader 一次把一个对象导出为 CSV，用 Export All 可以包含已删除和已归档的记录；报表导出适合小规模、有特定形状的提取。两者处理几千行都没问题，处理几千万行就不行了。
  • Bulk query. For volume, the loading dock works in both directions: sometimes the truck is unloading, sometimes it’s being loaded. A bulk query runs asynchronously and returns its results in files. Bulk API 2.0 manages the batching for you, and on large tables it chunks the query itself: its query jobs “automatically determine the best way to divide your query job into smaller chunks, helping to avoid failures or timeouts” (Understanding Bulk API 2.0 Query （在新标签页中打开）, checked 2026-10-27). On Bulk API (1.0) you turn that on yourself, with PK chunking. Salesforce’s Large Data Volumes guide draws the line: “When using a query that can return more than one million results, consider using the query capability of the Bulk API 2.0” (Extracting Data from the API （在新标签页中打开）, Winter ‘27, checked 2026-10-27). ⟲
  • 用于恢复的备份产品，比如 Salesforce 自己的 Backup & Recover，它做的是与迁移不同的工作：恢复到某个时间点，而不是把数据搬到新家。

For a mirror, I want the extract repeatable, logged and reconcilable, so that usually means a bulk query driven by a script, not clicks. Steve Trucking’s backfill is exactly that: a bulk query of the migrated Work Orders and their status history, written to Snowflake once, and reconciled. ⟲

Test knowledge. Know the ways data comes out of Salesforce and what each is for. The Data Export Service is a scheduled, whole-org CSV backup (weekly or monthly by edition; files kept 48 hours; no formula or roll-up fields). Data Loader and reports suit small, object-by-object or shaped extracts. Bulk API query handles volume asynchronously. A backup product restores a point in time; it isn’t a migration tool. ⟲

Very Large Copies: PK Chunking

Section titled “Very Large Copies: PK Chunking”

Fifty million records as one giant query may never finish. PK chunking divides the table by its primary key, the record ID: Salesforce adds ID boundaries to your query and runs it as a series of smaller queries, each a separate batch with its own results. The default chunk is 100,000 records and the maximum 250,000. Salesforce recommends it for tables of more than about 10 million records, or when a bulk query keeps timing out. That’s where I use it too: it’s specialized, for very large exports. It’s a Bulk API 1.0 header, it works on a defined list of objects, and each chunk counts toward the daily batch allowance (PK Chunking （在新标签页中打开）, checked 2026-10-27). Bulk API 2.0 does the same chunking for you, but an ORDER BY or a LIMIT switches it off on either API, and the query may then time out. ⟲

Steve Trucking will get there. About a million Work Orders a year, growing 30 percent a year, adds up to about 12.8 million by the end of year 6, unless chapter 7’s archiving keeps the table smaller. Work Order is on Salesforce’s list of supported objects. ⟲

PK chunking: one big table, many small queries On the left, highlighted, one big table: Work Orders, about 12.8 million by year 6 if nothing is archived. On the right, chunks by record ID, each its own query: chunk 1, the first 250,000 record IDs; chunk 2, the next 250,000; chunks 3 to 51, range after range; and chunk 52, the last record IDs. Each chunk returns its own results. Underneath: on Bulk API 1.0 you switch PK chunking on, and each chunk is a batch you download; Bulk API 2.0 query chunks for you. {"author": "Steve Simpson", "blueprint": "1.0.0", "builder": "standard 1.48.0", "built": "2026-10-27", "id": "DGM-DA-MG-51", "rights": "\u00a9 2026 Twinshark LLC. All rights reserved.", "template": "cells 0.4.0", "theme": "stevetecharc 1.4.0"} ONE BIG TABLE CHUNKS BY RECORD ID, EACH ITS OWN QUERY Work Orders about 12.8 million by year 6, if nothing is archived Chunk 1 the first 250,000 record IDs Chunk 2 the next 250,000 Chunks 3 to 51 range after range Chunk 52 the last record IDs its own results its own results their own results its own results Bulk API 1.0: you switch PK chunking on, and each chunk is a batch you download. Bulk API 2.0 query: it chunks for you. HOW TO READ IT the table: past 10 million rows, Salesforce recommends chunking © 2026 Twinshark LLC · stevetecharc.com
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Twelve million rows become about 52 queries of 250,000. ⟲

语法属于文档。架构师应该认出来的是这个模式：当“零的个数”改变时，提取策略可能也必须改变。

Test knowledge. For very large extracts (Salesforce’s line is more than 10 million records), PK chunking splits a bulk query into ranges of record IDs, each run and downloaded separately. It’s a header you set on Bulk API 1.0; Bulk API 2.0 query jobs chunk automatically. ORDER BY and LIMIT disable it. ⟲

Then Keep It Current

Section titled “Then Keep It Current”

The first copy is a snapshot. By the time it finishes, the source has changed: crews closed deliveries, coordinators changed windows, partners revised manifests. That’s normal. The architecture has to answer: what changed after our baseline? That’s the job of a watermark: a timestamp, a sequence, a change-tracking mechanism, a source identifier. Conceptually it says: this is what we copied, this is the point through which it was current, these are the changes since. Without that boundary, delta processing is guesswork. On the Salesforce side, the watermark is usually SystemModstamp, which is indexed (chapter 8): each run asks for what changed since the last run’s watermark, and moves the watermark forward only when the run succeeds. ⟲

Don’t forget the deletes. A record deleted in Salesforce must disappear from the mirror too, or the reports keep counting it. A normal query doesn’t return deleted records; a bulk query with the queryAll operation “returns records that have been deleted because of a merge or delete” (Create a Query Job （在新标签页中打开）, Bulk API 2.0, checked 2026-10-27). But deleted records stay in the Recycle Bin for 15 days at most, less if someone empties it (Deleting Data （在新标签页中打开）, Best Practices for Deployments with Large Data Volumes, checked 2026-10-27), so a mirror that looks for deletes less often than that will miss some. And a hard delete skips the Recycle Bin altogether, so plan how the mirror learns about those too. ⟲

How often? That’s chapter 9’s question, and Salesforce’s own guidance gives the line: the SOAP API’s getUpdated() and getDeleted() calls for intervals over 5 minutes, and for anything sooner, let Salesforce send the change (Asking Every Minute). Steve Trucking does both: the backfill is a bulk query, and from the cutover watermark onward, Change Data Capture sends each status change through the integration layer as it happens. ⟲

Keeping the mirror current Salesforce on the left, highlighted, the system of record, with the Work Orders and their status. Snowflake on the right, the mirror: status history, never written back. In between, how the copy moves, added one at a time. First, once: the first copy, a bulk query of everything so far, which sets the watermark, sends everything so far to Snowflake. Then, as it happens: every change after, by Change Data Capture through the integration layer, sends each change. Last, every few days: the deletes, a queryAll check well inside the Recycle Bin's 15 days, tells Snowflake what to remove. Underneath: one big copy, then a stream of small ones; each knows where the last one stopped. THE SYSTEM OF RECORD HOW THE COPY MOVES THE MIRROR Salesforce Work Orders and their status The first copy a bulk query of everything so far; sets the watermark Snowflake status history; never written back once everything so far One big copy, then a stream of small ones. Each knows where the last one stopped. HOW TO READ IT the system of record: fixes happen here © 2026 Twinshark LLC · stevetecharc.com
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Once: the first copy, everything so far. It sets the watermark. ⟲

Keeping the mirror current Salesforce on the left, highlighted, the system of record, with the Work Orders and their status. Snowflake on the right, the mirror: status history, never written back. In between, how the copy moves, added one at a time. First, once: the first copy, a bulk query of everything so far, which sets the watermark, sends everything so far to Snowflake. Then, as it happens: every change after, by Change Data Capture through the integration layer, sends each change. Last, every few days: the deletes, a queryAll check well inside the Recycle Bin's 15 days, tells Snowflake what to remove. Underneath: one big copy, then a stream of small ones; each knows where the last one stopped. THE SYSTEM OF RECORD HOW THE COPY MOVES THE MIRROR Salesforce Work Orders and their status The first copy a bulk query of everything so far; sets the watermark Every change after Change Data Capture, through the integration layer Snowflake status history; never written back once as it happens everything so far each change One big copy, then a stream of small ones. Each knows where the last one stopped. HOW TO READ IT the system of record: fixes happen here © 2026 Twinshark LLC · stevetecharc.com
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Then every change after the watermark, as it happens. ⟲

Keeping the mirror current Salesforce on the left, highlighted, the system of record, with the Work Orders and their status. Snowflake on the right, the mirror: status history, never written back. In between, how the copy moves, added one at a time. First, once: the first copy, a bulk query of everything so far, which sets the watermark, sends everything so far to Snowflake. Then, as it happens: every change after, by Change Data Capture through the integration layer, sends each change. Last, every few days: the deletes, a queryAll check well inside the Recycle Bin's 15 days, tells Snowflake what to remove. Underneath: one big copy, then a stream of small ones; each knows where the last one stopped. {"author": "Steve Simpson", "blueprint": "1.0.0", "builder": "standard 1.48.0", "built": "2026-10-27", "id": "DGM-DA-MG-54", "rights": "\u00a9 2026 Twinshark LLC. All rights reserved.", "template": "cells 0.4.0", "theme": "stevetecharc 1.4.0"} THE SYSTEM OF RECORD HOW THE COPY MOVES THE MIRROR Salesforce Work Orders and their status The first copy a bulk query of everything so far; sets the watermark Every change after Change Data Capture, through the integration layer The deletes a queryAll check, well inside 15 days Snowflake status history; never written back once as it happens every few days everything so far each change what to remove One big copy, then a stream of small ones. Each knows where the last one stopped. HOW TO READ IT the system of record: fixes happen here © 2026 Twinshark LLC · stevetecharc.com
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And every few days, the deletes. One big copy, then a stream of small ones. ⟲

Test knowledge. To keep another system current, extract only what changed since a watermark (SystemModstamp is indexed), and use queryAll to catch records deleted or merged. Deleted records stay in the Recycle Bin for 15 days, so run the delete check more often than that. For changes needed within minutes, use events (Change Data Capture) instead of polling. ⟲

A Copy Once, or a Copy Forever

Section titled “A Copy Once, or a Copy Forever”

这是我使用的区分：**迁移有明确的开始和结束。**我们在从一个状态移动到另一个状态。它可能从第一次生产提取开始，包括几次预加载，持续一周，包括几次增量加载。它依然是迁移，因为有一个终点：迁移完成的那个时刻。

**同步是让两个系统保持一致的持续过程。**没有“迁移完成，关掉它”这回事。两个系统都依然重要，变化持续发生，而且它们必须流动。现在我们又回到了第 9 章：哪个系统拥有这个值，它往哪个方向流动，用什么键识别它，谁推送、谁保证完整性，失败如何重试和监控，两个系统修改同一个东西时会怎样？一旦需求变成“让这些系统保持一致”，我们设计的就不再是迁移了。我们在设计集成。

Steve Trucking had one of each, on the same weekend. RouteDesk into Salesforce was a migration: its delta loads closed the gap, then it ended. Salesforce to Snowflake is synchronization: it started that weekend, and it never ends. ⟲

A copy once, or a copy forever Two equal panels. A copy once, migration, RouteDesk into Salesforce: it ends Monday morning, when RouteDesk retires; built with a runbook, delta loads and a cutover; proven by the four proofs, once, before go; when it fails, fix and rerun from the preparation data store; owned by the migration team, until it's done. A copy forever, synchronization, Salesforce to Snowflake: it never ends, for as long as the reports need it; built with a contract, a watermark and events; proven by proof of delivery, every run; when it fails, catch up from the last good watermark; owned by an integration owner, for good. Underneath: migration has a defined start and end; synchronization is an ongoing process to keep two systems consistent. {"author": "Steve Simpson", "blueprint": "1.0.0", "builder": "standard 1.48.0", "built": "2026-10-27", "id": "DGM-DA-MG-53", "rights": "\u00a9 2026 Twinshark LLC. All rights reserved.", "template": "comparison 0.4.0", "theme": "stevetecharc 1.4.0"} A copy once migration: RouteDesk into Salesforce IT ENDS Monday morning, when RouteDesk retires BUILT WITH A runbook, delta loads and a cutover PROVEN BY The four proofs, once, before go WHEN IT FAILS Fix and rerun from the preparation data store OWNED BY The migration team, until it's done A copy forever synchronization: Salesforce to Snowflake IT ENDS Never: for as long as the reports need it BUILT WITH A contract, a watermark and events PROVEN BY Proof of delivery, every run WHEN IT FAILS Catch up from the last good watermark OWNED BY An integration owner, for good Migration has a defined start and end. Synchronization is an ongoing process to keep two systems consistent. © 2026 Twinshark LLC · stevetecharc.com
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Same weekend, two arrows. One has an end, so everything about it is different. ⟲

但增量并不自动意味着同步。“我们有增量加载，所以我们在同步。”不一定。问一问：这个过程有明确的结束吗？“有，我们一直运行这些增量直到切换，然后遗留流程就停止了”依然是迁移；增量帮助我们到达它的最终状态。“没有，两个系统都保持活动，而这个过程无限期地让它们保持一致”就是同步。操作看起来可能一样。架构上的责任却不同。

Test knowledge. Migration has a defined end; synchronization is ongoing. Delta loads before a cutover belong to the migration. Keeping another system in step with Salesforce afterward is synchronization, and it needs an integration design with a clear owner for each value. ⟲

The Mirror Isn’t the Master

Section titled “The Mirror Isn’t the Master”

Salesforce owns the delivery status. Snowflake holds a copy. That has to stay true. Suppose an analyst notices a wrong status in Snowflake and corrects it there. Now the two systems disagree, and the next sync either overwrites the correction or, worse, somebody builds a feed to send it back. Then both systems think they own Delivery Status. Last write wins? Salesforce wins? Human review? If that’s unanswered, we haven’t built a mirror; we’ve created two writable copies. Chapter 9’s rule: technical permission isn’t architectural authority. Fix it at the source, and let the sync carry the fix. ⟲

So write the arrow’s contract down, the way chapter 9 does: who owns each value, which way it moves, what key identifies it, how fresh it must be, what counts as delivered, how failures are retried, how it’s monitored, what the watermark is. Those aren’t extract-script questions; they’re operating-model questions. The first copy builds the starting state. Synchronization keeps it coherent. ⟲

Prove the First Copy, Then Keep Proving It

Section titled “Prove the First Copy, Then Keep Proving It”

Use module 4’s four proofs (counts, totals, spot checks, aggregate reports) on the first copy. Prove the baseline before synchronization becomes the normal operating model, or the sync will faithfully maintain a bad starting point. Integration doesn’t repair a bad copy just because it runs continuously; it can keep two systems perfectly in step around the wrong data. ⟲

Then keep proving it. A mirror that stops quietly is a data quality problem with a delay: the reports look fine and slowly go stale. Chapter 9’s proof of delivery applies to every run: the side that knows what should arrive checks that it did, with counts against the watermark window, and someone is told when it doesn’t. ⟲

AI Can Help Spot the Drift. Keep the Boundary Deterministic.

Section titled “AI Can Help Spot the Drift. Keep the Boundary Deterministic.”

AI can help find changed records, unexpected differences between Salesforce and the mirror, anomalies, likely conflicts and records that seem to have been missed. Useful. But “is this change in the mirror yet?” shouldn’t depend on an AI’s guess from context. Watermarks, keys, ownership rules and state decide it. ⟲

Beyond the exam: what AI changes. AI can make comparison and anomaly detection between a system and its mirror much faster. Keep the sync deterministic: explicit keys, authority and watermarks decide what moves and when. ⟲

架构师在这里决定什么

Section titled “架构师在这里决定什么 ”
  • Why each copy of Salesforce data exists, which classes of data it really needs, and which system owns each value. ⟲
  • 哪种导出机制适合这项工作和“零的个数”：数据导出服务、Data Loader 或报表、批量查询，或者针对超大表的 PK 分块。
  • What watermark establishes the baseline, how later changes and deletes are captured, and how often (polling or events). ⟲
  • Whether a data movement is a migration with an end or a synchronization without one, and what integration contract keeps the mirror consistent. ⟲
  • How the first copy is proven, and how every run after it is checked. ⟲

Synchronization Keeps You There

Section titled “Synchronization Keeps You There”

Getting data out isn’t leaving Salesforce. It’s giving another system what it needs: once at volume, then every day after, current, complete and never the master. The first copy is the big job; keeping it current is the long one. Remember the line from this chapter: migration has a defined start and end; synchronization is an ongoing process to keep two systems consistent. The first copy gets the mirror there. Synchronization keeps it there. ⟲

延伸探索

Section titled “延伸探索 ”

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