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Every source you connect to Auxia runs through the same three stages, whatever it is and wherever it lives. Knowing the stages makes setup choices and troubleshooting much easier: almost every setup choice belongs to exactly one stage, and when something goes wrong, the first question is always which stage failed.
🏦
Your data
BigQuery · GCS · S3 · SFTP
→
📥
1. Transfer
Raw copy, as-is
1–2 days
→
⚡
2. Transform
Cleaned and unified
4–24 hours
→
🚀
3. Process
Into serving systems
~1 hour
Powers →
🎯 Real-time decisioning
🧠 ML features
🤖 Agent Studio
📊 Reporting
The times shown are typical for a new customer’s first connection — from finishing setup to data being ready in Auxia. Once a connection is live, each source syncs on its own schedule — see When will my data show up?

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Stage 1: Transfer

Copies your data as-is into Auxia. Nothing is renamed or reshaped.
  • For BigQuery, Auxia runs a read query against your table.
  • For Google Cloud Storage and Amazon S3, a storage transfer copies your files into an Auxia-side bucket.
  • For SFTP, you push files to an Auxia-hosted SFTP server.
Choices that belong to this stage: access grants, file format, and folder layout. Typical time for a new customer: 1–2 days.

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Stage 2: Transform

Reads the raw copy and writes Auxia’s unified tables — the single schema that everything downstream reads. The column mapping you confirm in the setup wizard is exactly what this stage applies; Map your data explains how that mapping works. This is Auxia’s proprietary capability: Auxia cleans your data and transforms it into unified data attributes, so your team doesn’t have to. There’s no code to write and no pipeline to build — onboarding is a guided, no-code setup. The processed data is ready for day-one modeling as ML features. Choices that belong to this stage: column mapping, primary user ID, event names, and row filters. Typical time for a new customer: 4–24 hours.

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Stage 3: Process

Pushes the unified data into Auxia’s serving systems. From there it powers real-time decisioning, ML feature generation, reporting, and Auxia Agent Studio use cases. Typical time for a new customer: about 1 hour.

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When will my data show up?

Three different timings come up when you connect a source. They measure different things:

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Ongoing sync by connector

If your data lands late in the day, tell your Auxia contact when it usually arrives. A scheduled run that starts before your data lands finds nothing new — and reports success, not failure.

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How the stages show up in the console

The Data Health dashboard shows each stage as its own status indicator, so you can see at a glance whether a problem is in reaching your data (Transfer), interpreting it (Transform), or serving it (Process).

What data is needed

The four kinds of data Auxia uses, with examples.

Map your data

How the Transform stage interprets your columns.