ETL and data pipelines

What it is

A data pipeline moves data between systems on a schedule or in response to events. ETL is the classic pattern: extract from sources, transform into a consistent shape, load into a destination. IBM notes a close cousin, ELT (extract, load, transform), which loads raw data first and transforms it inside the destination.

How it works

1. Extract: pull data from tools such as your CRM, payments and analytics, through their APIs.
2. Transform: clean, join and calculate. AI steps can classify or summarize text using structured outputs.
3. Load: write the results to a sheet, database or dashboard.

For small teams, an automation hub or a scheduled script is often enough. IonBin doesn’t recommend a specific pipeline product yet, because none are in our catalog.

Benefits

• Consistent numbers every week without copy-paste.
• One place where metrics are defined.
• AI-assisted transforms make unstructured data countable.

Limits and risks

• A broken source connection can leave reports silently out of date, so add alerts for failed runs and empty results.
• AI classification needs spot checks, and a fixed list of allowed labels.
• Data access must follow data governance rules.
• Write down how each metric is calculated, so a changed formula doesn’t look like a business change.