# Data Pipelines & ETL

*AI Data & Analytics Team*

CSVs, APIs, and source systems → cleaned, joined, analysis-ready datasets

Your AI analytics team builds and runs the ETL no one wants to write — pulling from CSVs, APIs, databases, and SaaS sources, then cleaning, joining, and writing to a destination of your choice.,Pipelines are versioned. Re-runs are deterministic. Failures alert with the offending row and the proposed fix.,Outputs are analyst-ready: deduplicated, normalized, enriched, and joined to the entities you care about.

## Benefits

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## How It Works

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## At a Glance

- **Any** Source type supported
- **Versioned** Pipelines under change control
- **Deterministic** Re-runs produce same output
- **Alerting** On regression, not after

## Pipelines That Do Not Drift

## FAQ

### Is this competing with Fivetran / dbt?

It works with them. Your team can write to dbt models or skip the stack entirely for lighter use cases.

### How does it handle PII?

PII masking and field-level encryption supported. Configurable per source.

### What about real-time pipelines?

Event-driven runs supported via webhook triggers and message queues.

### Can it backfill historical data?

Yes — backfills are first-class with idempotency guarantees so you can re-run safely.