How Smart Data Modeling Can Cut Hundreds of Thousands in Compute Costs Monthly

Recent conversations with several data team leaders reveal two common pain points: data volume is growing and cloud bills are soaring, while teams work overtime and business units complain about slow data delivery.

Root Cause

The core issue is inadequate data modeling.

Step 1: Manage Every Field

Effective modeling requires subtraction, not just mirroring business processes. For each new field ask:

Who needs it?

How will it be used?

Can it be omitted?

Many fields that seemed useful initially are never accessed. Removing fields that haven’t been accessed for three months reduces scan size and speeds up queries.

Step 2: Implement Real Data Layering

Data should flow through clear layers: ODS, DWD, DWS, and ADS.

1. ODS (Original Data Store)

Simply ingest and store raw data. Avoid complex cleaning or transformation here to keep the layer stable and fast for downstream consumption.

2. DWD (Detail Data Store)

Perform cleaning, joining, and labeling once. Produce a clean, reliable detail dataset that serves all downstream analyses, eliminating duplicated processing.

3. DWS (Summary Data Store)

Pre‑compute common aggregates (e.g., daily sales totals, monthly active users) once and reuse them across multiple reports, dramatically cutting redundant computation.

4. ADS (Application Data Store)

Serve specific reports or APIs with minimal filtering and format conversion, leaving heavy calculations to lower layers. This keeps front‑end response times fast.

Step 3: Pre‑Aggregate When Possible

If business only needs trends, aggregate second‑level data to minute‑ or hour‑level, cutting data volume by over 90% and reducing downstream compute and storage pressure.

Identify the most frequently used dimension combinations with business stakeholders and compute them in advance, avoiding on‑the‑fly processing of billions of rows.

Step 4: Classify Data as Hot or Cold

Hot data (e.g., today’s orders) requires millisecond‑level response; cold data (e.g., three‑year‑old logs) can be stored in cheap archival storage and accessed infrequently.

Recent 7‑day data → high‑performance storage.

7‑90‑day data → standard storage.

Older than 90 days → cheapest archival storage.

Step 5: Document the Model to Save Labor

A chaotic model raises communication costs and onboarding time. Create a "model card" for each core table in the team wiki, detailing purpose, source, key business rules, owners, and downstream applications.

This simple documentation can double the efficiency of data‑related communication, allowing analysts to focus on real analysis instead of hunting for definitions.

Immediate Actions You Can Take

Review your three most critical tables and prune unused fields.

Identify the five longest‑running, most resource‑intensive jobs; check for duplicate calculations and consolidate them in the DWS layer.

Meet with business owners to confirm their top five dashboards or metrics and ensure the data pipeline meets their latency expectations.

By following these steps, organizations can significantly reduce unnecessary compute costs, improve query performance, and free resources for higher‑value analytical work.