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ETL vs. ELT in the AI Era

TL;DR

• ETL (extract-transform-load) vs. ELT (extract-load-transform): AI affects the transform step. It can suggest mappings, write transforms, and even generate SQL. Governance and correctness don't change.
• Use AI for transform logic and mapping. You own: schema design, lineage, and "is this right?"
• Data integration is a trust layer. Wrong transforms = wrong decisions. Verify.

ETL and ELT are about moving and shaping data. Transform is where the logic lives. AI can now draft transforms, suggest mappings, and generate SQL or Python. That speeds things up. It also introduces errors. Schema drift, wrong joins, misunderstood business rules. Your job: use AI for productivity, own the trust layer.

What AI Can Do

Mapping and transform logic:

• "Map this source schema to this target." AI can draft. You verify against business rules.
• Good for first pass. Real data has edge cases. Validate.

SQL generation:

• "Summarize this table by date and region." AI can write the query. You check correctness and performance.
• Useful for ad-hoc and reports. For pipelines, review carefully. Wrong SQL = wrong data.

Documentation:

• "What does this pipeline do?" AI can summarize from code. You fill gaps. You ensure accuracy.
• Pipelines are read by many people. Good docs matter. AI assists; you own.

Pattern suggestions:

• Incremental loads, CDC, slowly changing dimensions. AI knows the patterns. You pick what fits your context.
• Your sources, your latency needs, your constraints — AI doesn't know. You do.

What Stays Human

Governance and lineage:

• Where did this data come from? Who owns it? What transformations were applied? AI doesn't track this. You design the metadata. You enforce it.
• Data governance is organizational. AI is a tool. You're the steward.

Business logic:

• "Revenue = X minus Y, except when Z." AI can implement from a description. It can also misinterpret. You validate.
• Run spot checks. Compare to known good outputs. Especially for financial or regulatory data.

Schema evolution:

• Sources change. Columns added, deprecated, renamed. Who decides how to handle it? You. AI can suggest. You own the policy.
• Version your transforms. Document breakages. Have a rollback plan.

Quality and monitoring:

• What's "good" data? Null rates, distributions, anomalies. AI can flag. You define the rules. You act on the alerts.
• Data quality is a process. AI assists. You own it.

ETL vs. ELT With AI

ETL (transform before load):

• Transform happens in a separate engine. AI can help write the transform logic. You still design the flow. You still validate.
• AI doesn't change when you transform. It changes how fast you can draft the transform.

ELT (load first, transform in warehouse):

• Transform happens in Snowflake, BigQuery, etc. AI can generate SQL. You run it. You verify.
• Same deal. AI speeds drafting. You own correctness and performance.

AI as transform assistant:

• Use AI to generate. Review. Test. Deploy. The loop is faster. The responsibility is unchanged.

Do This Next

1. Generate one transform with AI — Mapping or SQL. Validate against business rules. List what you had to fix. That's your QA checklist.
2. Document lineage for one pipeline — Source, transform, target. Who owns what? Make it explicit. AI won't. You will.
3. Add one quality check — Null check, distribution check, or comparison to a known source. Automate it. Review when it fails.