What Is a Data Warehouse? | Snowflake vs BigQuery ROI Guide
Learn the modern data warehouse stack, when to choose Snowflake vs BigQuery, and how to build an AI-ready warehouse in 90 days with clear ROI.
Ali Z.
𝄪
CEO @ aztela
Table of Contents
Why Data Warehousing Still Matters in 2025
Executives want trusted answers fast. But most organizations are still drowning in:
Dashboards that don’t match across departments.
Spreadsheets as the “real source of truth.”
AI pilots that collapse because the foundation wasn’t ready.
A data warehouse solves this by providing a single, governed source of truth that delivers fast analytics and fuels AI models with reliable data.
This guide covers:
The basics of what a data warehouse is (in plain English).
How it compares to databases and lakehouses.
Why Snowflake, BigQuery, and Databricks dominate modern builds.
ROI math that justifies the investment.
A 5-step playbook to build one in 90 days.
Data Warehouse vs Database vs Lakehouse
Feature | OLTP Database | Classic DW | Lakehouse |
|---|---|---|---|
Optimized for | Transactions | Analytics | Hybrid (SQL + ML) |
Schema | Strict | Star/Snowflake | Flexible |
Cost Scaling | $$$ | $$ | $–$$ |
AI-Readiness | Low | Medium | High with ML features |
Key takeaway:
If your goal is governed, query-ready data for BI and AI prototypes, a cloud warehouse or lakehouse is the pragmatic choice.
What Does “EDW” Actually Mean?
EDW = Enterprise Data Warehouse.
Same concept, broader scope:
Cross-department KPIs.
Compliance-ready architecture.
Historical snapshots for trend analysis.
Why Modern Teams Choose Snowflake & BigQuery
Snowflake → Elastic compute, usage-based pricing, huge partner ecosystem.
BigQuery → Serverless scaling, no-ops, seamless with Google stack.
Databricks Lakehouse → SQL + ML workflows on one platform.
For most mid-market firms, the decision is Snowflake vs BigQuery. Both can deliver speed and trust when tied to a strong data strategy foundation.
5-Step Build Playbook (90-Day Roadmap)
Week | Milestone | Notes |
|---|---|---|
1 | Source inventory & KPI shortlist | Define core business metrics first. |
2–3 | Ingest via Fivetran/Airbyte | Land raw data before transformation. |
4–6 | Model in dbt (staging → marts) | Add tests for freshness & accuracy. |
7–8 | Build BI layer (Looker/Sheets) | Tie every chart to an owner/KPI. |
9–12 | Add governance & cost guardrails | Lineage tools + usage monitoring. |
This roadmap ensures quick wins in weeks, not endless 12-month rebuilds.
Cost & ROI Snapshot
Input | Range | Notes |
|---|---|---|
Cloud warehouse | $400–$4k/mo | Usage-based billing (Snowflake credits, BigQuery slots). |
Ingestion | $150–$1k/mo | SaaS connectors (Fivetran, Airbyte). |
Team hours | 160–240 hrs | ~1 FTE or fractional squad. |
Total Year-1 | $30k–$80k | 3–5× cheaper than on-prem EDWs. |
ROI example:
A single churn KPI that reduces cancellations by 5% on $4M ARR = $200k saved. That’s 2–6× the cost of a warehouse build.
Data Warehouse vs Lakehouse Which to Choose in 2025?
Heavy ML + streaming? → Lakehouse.
Primarily BI dashboards + SQL? → Warehouse first, Lakehouse later.
If you’re ready to modernize your warehouse, move from legacy to cloud, and build an AI-ready foundation in 90 days, Book a Data Strategy Assessment.
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