Schema Naming

Overview

In any data system, naming is what turns raw data into something understandable. In Precog, schemas serve as logical containers inside your destination database that group related tables together. How those schemas are named determines how easily your teams can interpret, trace, and manage the data that flows through them.

Precog uses a consistent schema naming pattern to make that structure transparent. Every schema name tells the story of where the data came from and what process brought it there.

Why It Matters

As organizations connect more business applications, it's common to encounter overlapping table names — such as "orders," "customers," or "users." Without a predictable naming structure, data from different applications can blur together, making analysis harder and maintenance riskier.

A clear schema naming convention prevents this confusion. It gives your data environment order and meaning — helping you distinguish data sources, understand lineage, and align teams working across analytics, operations, and governance.

Consistent schema names also reflect data ownership and intent. They show which business process or schedule produced a dataset, allowing teams to reason about their data without needing to look behind the scenes.

How It Works Conceptually

Precog names schemas using a simple, descriptive pattern:

<schedule name>_<source>_<configured source name>

This convention balances automation with context.

  • The schedule name ties each schema to the process that produced it — reinforcing the idea that data movement in Precog is intentional and time-bound.

  • The source identifies the system of origin — the business application or API that generated the data.

  • The configured source name expresses the human context — how you or your organization labeled that source during setup (for example, a department, project, or account).

Together, these elements create schema names that are both machine-readable and meaningful to people. Anyone reviewing your database can immediately understand the lineage of a dataset without documentation or guesswork.

Example in Context

Imagine your organization runs a daily schedule that brings in Shopify order data for multiple online stores. Each store uses the same data structure, but each represents a separate business unit.

The schema name daily_report_shopify_citymerch communicates that:

  • The data is part of the daily reporting workflow.
  • It originated from Shopify.
  • It came from the CityMerch account.

Even without knowing the technical configuration, the schema name itself tells the story of the data's purpose, origin, and timing.

The Broader Idea

Schema naming isn't just a formatting rule — it's a communication tool. A well-designed naming convention brings structure and predictability to the way data moves through your environment.

By encoding context into schema names, Precog helps teams reason about data without depending on documentation or metadata systems. The name itself carries meaning — bridging the gap between the technical operation of data loading and the business understanding of where that data belongs.