I'm building a replacement for spreadsheet-based reporting; what data warehouse approach is simplest?

If your main goal is to replace spreadsheet-based reporting with the least complexity, the simplest approach is usually:

Start with a single cloud data warehouse + ELT + semantic/reporting layer

Simplest practical stack

• Source systems → apps, databases, CSVs, spreadsheets
• Ingestion → scheduled loads or a connector tool
• Warehouse → one central cloud warehouse
• Transformations → SQL in the warehouse
• Reporting → BI tool or simple metrics layer

Why this is simplest

• Keeps one system of record for reporting
• Avoids building a separate ETL server or custom data pipeline first
• Lets you model data in SQL, which is easier to maintain than spreadsheet logic
• Makes governance and refresh schedules much cleaner

Recommended warehouse approach

For most teams, I’d suggest:

Option A: Managed cloud warehouse

Examples:

• BigQuery
• Snowflake
• Redshift Serverless
• Azure Synapse / Fabric Warehouse

This is the easiest if you want:

• minimal ops
• easy scaling
• straightforward SQL analytics
• fast path from spreadsheets to dashboards

Architecture pattern to use

1. Land raw data

Load data “as-is” into raw tables.

2. Build cleaned reporting tables

Use SQL to create:

• dimension tables
• fact tables
• summary tables

3. Expose a semantic layer

Define business metrics once:

• revenue
• active customers
• conversion rate
• churn

This prevents every spreadsheet/report from calculating them differently.

4. Connect BI/reporting tool

Use:

• Power BI
• Looker
• Tableau
• Metabase
• Apache Superset

If you want the absolute simplest setup

A very common minimal path is:

• BigQuery
• dbt
• Power BI or Metabase

Why:

• BigQuery is low-ops and easy to start
• dbt keeps transformations organized in SQL
• Power BI/Metabase makes business reporting accessible

When to avoid a warehouse

If the data is:

• tiny
• mostly manual
• only one or two reports

then a warehouse may be overkill. You might start with:

• a structured database like Postgres
• plus a lightweight reporting tool

But once spreadsheet reporting becomes shared, recurring, or business-critical, a warehouse is usually the right move.

My recommendation

If you want the simplest scalable approach, do this:

Use one managed cloud warehouse, load raw data into it, transform with SQL/dbt, and serve reports from a BI tool or semantic layer.

If you want, I can also give you:

1. a minimal reference architecture,
2. a tool-by-tool recommendation by budget, or
3. a migration plan from spreadsheets to a warehouse.