ETL and ELT Design Patterns Questions

In a pay-as-you-go warehouse, ELT's core promise (compute scales elastically, so push transformation down) is also exactly where its costs hide, because "elastic" means "you pay per query, and a bad query pattern compounds."

Where the costs actually show up

• Repeated full-table scans. A transform that re-scans a large source table on every run instead of filtering to what actually changed pays for the whole table's worth of compute, every single run.
• Wide, unnecessary joins inside transformation SQL. Joining more columns or tables than a given model actually needs (common when dbt models get copy-pasted and extended rather than redesigned) inflates the bytes scanned per query.
• Materializing too much, too often. Rebuilding a large table as a full table materialization every run when it could be incremental burns compute on rows that didn't change.
• Query concurrency at peak hours. Running heavy transformation jobs during the same window as interactive BI queries forces the warehouse to scale up compute (or queue queries), both of which cost money.

Concrete tactics to reduce spend without breaking service-level agreements (SLAs)

1. Materialized views and pre-aggregation for the heaviest, most-repeated aggregate queries, so you pay for the aggregation once instead of on every downstream query.
2. Clustering/partitioning keys aligned to how the data is actually filtered and joined, so the warehouse prunes what it scans instead of reading everything.
3. Right-sizing compute (warehouse size in Snowflake terms, slot reservations in BigQuery terms) to the actual workload rather than over-provisioning "to be safe," and scheduling the heaviest jobs off-peak when they don't compete with interactive queries.
4. Switching full-refresh models to incremental wherever the transformation logic allows it, since re-transforming only the delta is usually the single biggest lever.

A worked diagnostic: if a team notices costs rose after migrating pre-load ETL transforms into in-warehouse ELT SQL, the first thing to check isn't the warehouse bill line item, it's the query profile: how many bytes did the slow queries actually scan versus how many they needed to. A query that scans the full history of a table to compute yesterday's aggregate, because the model was written as a full refresh instead of incremental, is a much more common root cause than "the warehouse itself got more expensive."

Trade-off to be explicit about: every one of these tactics (materialized views, clustering, incremental models) adds engineering and maintenance complexity in exchange for lower compute spend. The right amount of optimization is proportional to how much that specific model actually costs, over-optimizing a rarely-run, cheap model is wasted effort that could go toward the genuinely expensive ones.

A metric-definition change is a fundamentally different kind of backfill from "fix a bug that corrupted data": the old numbers weren't wrong, they were computed under a different, equally valid definition, and the plan has to preserve BOTH versions rather than simply overwriting history.

The design

1. Version the metric definition itself, not just the data. Store a metric_version (or a definition id) alongside every computed value, so "revenue under definition v1" and "revenue under definition v2" are distinguishable rows, never the same cell silently overwritten.
2. Compute the new definition over the full historical range as a genuinely separate backfill job, writing to versioned output (a new column, a new table, or the same table tagged with the new version), not in place over the old values.
3. Support side-by-side comparison before cutover. Build a reconciliation view or dashboard that shows both versions over the same historical window, so stakeholders can see exactly where and by how much the numbers diverge before the new definition becomes the default anyone sees.
4. Cut the dashboard over deliberately, with an explicit transition point, rather than silently swapping which definition a dashboard reads once the backfill finishes. A visible annotation on the dashboard ("metric definition changed on this date, see docs for what changed and why") is what prevents someone from misreading a definitional shift as a real business change.
5. Document what changed and when, including the old and new definitions in plain language and the exact date/version boundary, in a place discoverable from the dashboard itself, not just in a commit message or a Slack thread that won't exist when someone asks about this in eight months.

Why this differs from correcting a bug: fixing a genuinely wrong number (a computation error, corrupted data) has one correct target state, and the backfill's job is to converge everyone on it. A definitional change has TWO correct states depending on which definition you're asking about, and collapsing history down to only the new definition, even though it's "more correct going forward," destroys the ability to explain historical decisions that were made using the old definition, which is a real cost, not a hypothetical one.

Reprocessing considerations shared with a bug-fix backfill: minimize dashboard disruption by backfilling in the background and only flipping the default view once fully validated; verify the backfill actually reproduces the old definition's numbers exactly over the overlap period where nothing should have changed (a self-check that your new pipeline logic didn't accidentally also alter something it wasn't supposed to); and keep the backfill resumable/idempotent per the same partition-level patterns used for any large reprocessing job, since "recompute five years of history" is exactly the kind of job that can and will fail partway through.

The late-arriving-data twin of this problem: the same reprocessing mechanics (recompute a bounded historical range, verify against a baseline, communicate the correction) apply when a daily aggregate needs correcting because data for that day arrived late, not because a definition changed. The difference is scale and cadence, a late-arriving correction is usually small and routine (a few hours to a couple of days of data, handled automatically via an allowed-lateness window), while a definitional backfill is a large, deliberate, one-time reprocessing of full history, but the underlying discipline (versioning, validation, controlled cutover, communication) is the same.

Change Data Capture is the practice of identifying exactly which rows changed (and how) in a source system, so a downstream consumer only has to process the delta instead of re-reading everything. There are three common ways to implement it, and they trade off latency, load on the source, and correctness very differently.

When each is the right call

• Log-based is the right default for a high-write OLTP database feeding near-real-time analytics: it doesn't add load to the source and it's the only one of the three that captures deletes and preserves transaction ordering. Debezium is the standard open-source tool here (into Kafka, then a sink connector or stream processor).
• Trigger-based shows up when you don't have log access (a managed database that doesn't expose its replication log, or a source you don't control at the infrastructure level) but do have DDL permission to add triggers.
• Timestamp polling is the pragmatic choice when the source has a reliable updated_at, deletes are rare or handled another way (a soft-delete flag), and near-real-time latency isn't required: it's the cheapest to build and operate, at the cost of missing hard deletes and adding poll-interval latency.

The trap: teams often reach for log-based CDC (Change Data Capture) by default because it's "the correct one," without checking whether they actually need deletes or sub-minute latency. Trigger-based and timestamp-based CDC are legitimate production patterns, not just fallbacks, when their specific correctness gaps don't matter for the use case.

Migrating nightly ETL jobs off an on-prem tool onto a cloud-native ELT stack (a modern warehouse plus dbt) is a data-correctness migration wearing an infrastructure-migration costume: the real risk isn't "can we stand up Snowflake and dbt," it's "will the new pipeline produce the exact same numbers the old one did, and if not, can we tell before a stakeholder does."

The plan

1. Inventory. List every existing job: what it reads, what business logic it applies, what it writes, who consumes the output, and how often it runs. Rank by consumer impact and complexity, not build order.
2. Map transformations. For each job, translate the transformation logic into dbt models (staging, then business logic). Do this job by job, not all at once, so each migrated piece can be validated independently.
3. Build parity tests. For every migrated job, write a reconciliation check that runs both the old and new pipeline over the same input window and diffs the outputs: row counts, key aggregates, and a sample of individual rows compared field by field. This is the single highest-leverage step; skipping it is how migrations ship silent regressions.
4. Staged validation. Run the new pipeline in shadow mode (writing to a parallel table, not the production one) for a real window of days, comparing continuously against the legacy output, before anyone downstream depends on it.
5. Dry-run backfills. Before cutover, backfill historical data through the new pipeline and reconcile it against the legacy historical output, since a pipeline that matches going forward can still disagree on how it handles old edge cases (a schema that changed shape a year ago, a historical data-quality issue the legacy job special-cased).
6. Cutover plan. Flip consumers over job by job once parity is proven, not all at once, so a problem is isolated to one job instead of the whole migration.
7. Rollback criteria. Define upfront what triggers a rollback (a reconciliation mismatch above some tolerance, a downstream consumer reporting wrong numbers) and keep the legacy job runnable in parallel until the new one has proven itself over a real operating window, not just the initial validation period.

The trap worth naming: teams treat "the new pipeline runs successfully" as the finish line. A pipeline that runs cleanly and silently produces slightly different numbers (a different NULL-handling default, a rounding difference, a JOIN that behaves differently because ELT does it in the warehouse instead of in application code) is worse than one that visibly fails, because nobody notices until a stakeholder does. Parity testing against the legacy pipeline, not just "does the new one look reasonable," is what catches that class of defect.

The MERGE, loading orders_stg(order_id, amount, last_modified, deleted) into orders(order_id PK, amount, last_modified, is_deleted):

MERGE INTO orders AS t
USING (
  SELECT order_id, amount, last_modified, deleted
  FROM (
    SELECT *, ROW_NUMBER() OVER (
      PARTITION BY order_id ORDER BY last_modified DESC
    ) AS rn
    FROM orders_stg
  )
  WHERE rn = 1
) AS s
ON t.order_id = s.order_id
WHEN MATCHED AND s.last_modified > t.last_modified THEN
  UPDATE SET amount = s.amount, last_modified = s.last_modified, is_deleted = s.deleted
WHEN NOT MATCHED THEN
  INSERT (order_id, amount, last_modified, is_deleted)
  VALUES (s.order_id, s.amount, s.last_modified, s.deleted);

Verified end to end with a target row for order 1, and a staging batch containing an update for order 1, a stale duplicate row for order 1 with an older last_modified, a new order 2, and a new order 3 that's inserted and soft-deleted in the same batch:

before: orders = [(1, 100.00, 09:00, false)]

staging batch:
  (1, 150.00, 11:00, false)   -- real update
  (1,  90.00, 08:00, false)   -- STALE duplicate for the same key, older timestamp
  (2,  40.00, 11:05, false)   -- new
  (3,  10.00, 11:10, true)    -- new, arrives already deleted

after MERGE:
  order_id | amount | last_modified | is_deleted
  1        | 150.0  | 11:00         | False
  2        | 40.0   | 11:05         | False
  3        | 10.0   | 11:10         | True

re-running the SAME merge again: identical output (confirmed idempotent)

The inner ROW_NUMBER() window is what makes this correct rather than merely lucky: without it, the MERGE spec would try to match target row 1 against BOTH staging rows for order 1, and most engines either error on a duplicate match in the USING clause or apply them in an undefined order, which can silently leave the stale (90.00, 08:00) value as the final state instead of the correct 150.00. Deduplicating the staging batch down to one row per key, keeping the latest by last_modified, before it ever reaches the MERGE's ON clause is what guarantees a deterministic, correct result regardless of what order duplicate source rows happen to arrive in.

What makes this safe under retries: the WHEN MATCHED AND s.last_modified > t.last_modified guard means re-applying an already-applied batch is a no-op, since the incoming last_modified will no longer be greater than what's already in the target. Combined with the per-batch dedup, the MERGE produces the same end state whether it runs once or is retried after a partial failure and re-run from scratch.

What makes this safe under concurrent runs: a single MERGE statement is atomic in every warehouse that supports it (Snowflake, BigQuery, Postgres), so two concurrent MERGEs against the same target either serialize (one waits for the other's lock) or one fails with a concurrent-modification error that the caller retries, rather than interleaving in a way that corrupts a row. The dangerous version of "concurrent" is two SEPARATE INSERT/UPDATE statements instead of one MERGE, where a race between them can produce a duplicate insert; using one atomic MERGE statement is what removes that class of bug entirely.

Indexes/constraints: a unique constraint (or primary key) on order_id in the target table is what turns "the MERGE has a bug that lets two source rows both count as NOT MATCHED" into a loud constraint violation instead of a silent duplicate row, so it's not optional, it's the backstop for exactly the kind of duplicate-match scenario this dedup step exists to prevent.