ETL vs Reverse ETL vs Two-Way Sync: Choose the Data Flow

Compare the contracts, not just the arrows

The timing depends on the implementation. AWS Glue documentation is one example of a service supporting several data integration approaches, including ETL, ELT, and streaming. Evaluate how a particular job captures, processes, and delivers changes rather than assigning speed from its category name.

Example: publish a customer health score

Suppose a warehouse combines product usage, support history, and billing records into a customer health score. A reverse ETL job writes the score and its calculation timestamp to the CRM. The warehouse owns those derived fields; sales continues to own account notes and the sales process.

The score’s freshness depends on more than the final delivery job. Source ingestion must run, the model must recompute, and the destination update must succeed. A one-minute export interval cannot make a score current if its input dataset is a day old.

This is an illustrative design, not a customer result. Define how unknown customers, missing scores, and changes to the model behave. An empty result set should not accidentally clear every CRM score without an agreed policy.

When you need a two-way operational path

Add a return path when a second system has a legitimate editing responsibility. For example, an internal application may let an operations team correct a contact value that should return to the CRM. That flow needs stable identity, permitted properties, validation, and conflict behavior.

Do not write a derived warehouse value to a CRM field that users expect to own unless the overwrite rule is explicit. If a person must override the calculated result, consider separate calculated and override fields with a documented effective-value rule. The design should make it clear which value is authoritative.

The field mapping workbook captures ownership and null behavior for each path. The HubSpot–PostgreSQL guide provides a concrete example of operational database write-back that should be evaluated separately from analytics ingestion.

Verify the actual connector and destination

Check database or warehouse permissions, key requirements, selected tables, and writable application fields. A connector appearing in a catalog establishes a starting point; it does not prove every object, transformation, or write direction needed by a workflow.

For Stacksync, begin with the warehouse integration overview and the specific Snowflake connector documentation or other relevant connector guide. Validate the live connection’s capabilities before relying on operational write-back from a warehouse.

A useful evaluation includes a normal update, a missing match, an invalid destination value, and a repeated delivery. Also test what happens when a warehouse model drops a row or changes its customer key. Reconciliation must distinguish an intentional exclusion from a lost record.

Choose a design and record the decision

• 01For reporting, first evaluate ingestion and transformation into the analytical destination.
• 02For publishing derived attributes, evaluate a one-way activation path with explicit field ownership.
• 03For jointly edited operational records, evaluate bidirectional behavior and conflict handling.
• 04For workflows requiring every transition, evaluate retained events and delivery semantics.
• 05Compare options with the same data volume, objects, freshness target, and recovery cases.

Use the integration evaluation worksheet and production checklist to preserve the evidence. If Stacksync is on the shortlist, bring the full data path and field ownership rules to an integration review so the discussion covers the business outcome as well as the connection.