AutoWarehouse
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Use Cases

Three Paths to Your
Data Warehouse

Every DWH project starts differently. AutoWarehouse meets you where you are — whether you need a turnkey deployment, an incremental extension, or a greenfield build from source discovery.

01
Path 01

Deploy a Ready-Made Industry Model

From zero to analytics-ready DWH in days, not months

Your organization needs a data warehouse but has no existing model. Pick a battle-tested industry template — Finance (IFDM), Telecom (ITDM), HR (HRDM), or others — and deploy a fully structured star-schema DWH with pre-built dimensions, fact tables, and SCD Type 2 handling.

Real-World ScenarioA mid-size bank · regulatory reporting

A mid-size bank wants to consolidate regulatory reporting. They connect their core banking system, select the IFDM (Finance Data Model), and AutoWarehouse instantly provisions Foundation, Analytical, and Semantic layers. The ETL Code Generator exports production-ready Airflow DAGs and dbt models — deployed to the bank's own infrastructure within a week.

01
Industry Templates
Refined across 80+ projects. Star-schema with dimensions, facts, bridges, and SCD Type 2.
02
Database DNA Report
Row counts, profiling, heavily-used vs zero-record detection, reference/lookup classification, anomaly flags.
03
Deterministic Code Generation
Zero hallucination. Airflow, dbt, Snowflake, Spark, PL/SQL — independently versioned, licensable plugins.
OutcomeAnalytics-ready data warehouse with production ETL code, deployed on your own infrastructure. Weeks instead of months.
02
Path 02

Extend & Customize an Existing Model

Evolve your DWH with new requirements — without starting over

You already have a data model or a partial warehouse. Import it, overlay new business requirements, and let AutoWarehouse extend the model — adding dimensions, facts, or entire subject areas while preserving what already works.

Real-World ScenarioA telecom operator · churn prediction

A telecom operator has been running ITDM for 3 years. New regulatory requirements demand a customer churn prediction subject area. They import their existing model, add new source tables from the CRM, and use the table-level mapping screen to see the full source-to-target picture. The AI suggests column mappings for new tables while preserving all existing mappings. A 'Save as New Version' snapshot ensures rollback safety. The ETL plugin generates incremental dbt models that slot into their existing pipeline.

01
Source-to-Target Table Mapping
Full table-level view of the entire warehouse — spot coverage gaps at a glance.
02
Similar Column Detection
Sidebar highlights similar/identical names across tables: join candidates, duplicates, standardization.
03
Model Versioning
Save as a new copy. Branch, experiment, compare — with full version history and rollback.
OutcomeExtended data model with surgical precision — new subject areas integrated without disrupting existing production pipelines.
03
Path 03

Build from Scratch with AI Discovery

From business requirements to a fully modeled DWH — AI-assisted, human-approved

No existing model, complex source systems, specific business requirements. AutoWarehouse connects to your sources, generates a comprehensive Database DNA report, helps you design the target model from discovered insights, and maps everything — with human review and approval at every step.

Real-World ScenarioA manufacturing conglomerate · 12 ERP instances

A manufacturing conglomerate with 12 ERP instances across 4 countries needs a unified analytics platform. AutoWarehouse connects to all source systems and generates a Database DNA report: 2,400 tables profiled, 847 identified as actively used, 340 flagged as reference/lookup tables, 198 anomalies detected. The AI generates a comprehensive database dictionary with column-level documentation. Data engineers review and approve the discovery. They design a custom target model using insights from the DNA report, map all 847 active tables, and export PL/SQL ETL scripts and Spark jobs — all verified and approved before any code touches production.

01
Comprehensive Database Discovery
Inductive analysis: row counts, profiling, anomaly detection, table classification — an interactive, customer-facing report.
02
AI-Generated Database Dictionary
Auto docs per table and column: inferred descriptions, data types, relationships, quality scores — with human review.
03
Data Quality Dashboard
Real-time null %, record counts, unique distributions, anomaly alerts, and completeness scores.
04
Plugin-Based ETL Export
Deterministic exporters for Airflow, dbt, Snowflake, Spark, PL/SQL — each an independent plugin.
OutcomeA fully documented, quality-assessed, mapped, and code-generated DWH — built from scratch with full traceability from source discovery to production deployment.
Across every path

Built on the same guarantees

Whichever way you start, the same engineering principles hold — from the first profiled table to the code that ships to production.

Deterministic ETL
100% deterministic code generation — predictable, auditable, and production-safe. No hallucination, ever.
Plugin Architecture
Independent exporters, individually versioned and licensable, with backward-compatible upgrades.
Human-in-the-Loop
AI assists; humans review, approve, and control. Nothing reaches production without explicit approval.

Which path fits
your project?

Tell us about your data warehouse needs. We'll show you the fastest path to production.