UD4D — Use data for decisions

Reducing cloud data platform costs by more than 40%

We tuned the platform’s heaviest workloads and data model to cut cloud spend significantly, without touching reporting.

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Reducing cloud data platform costs by more than 40%

About the client

Multinational pharmaceutical manufacturer running SAP-based planning and operations across 140+ markets.

The challenge

The client needed to reduce Snowflake operating costs while maintaining reporting capabilities and supporting future growth.

  • Data flows not optimized for performance, consuming a significant share of Snowflake compute resources and increasing platform operating costs
  • Reporting views performing poorly for end consumers while generating disproportionate compute usage
  • Raw Data Vault layer for SAP IBP and SAP S/4HANA sources not optimized for MPP performance, delivering limited business value due to architectural issues
  • Limited visibility into cost drivers made it difficult to prioritize optimization efforts

The solution

UD4D launched a multi-phase optimization initiative targeting the highest-cost areas of the data platform.

  • Analysis of compute consumption across data warehouse workloads
  • Optimization of the most resource-intensive data pipelines and reporting views
  • Identification and removal of unused Data Vault objects
  • Refactoring of downstream dependencies
  • Introduction of an optimized Raw Data Vault model to improve reuse and efficiency

Business impact

  • 40%+

    lower compute consumption

    Daily Snowflake usage fell from 70 to 40 credits per day, directly lowering platform operating costs.

  • $44K

    annual cost savings

    Estimated annual savings achieved while maintaining full business functionality.

  • 80

    reporting views optimized

    43 refactored in phase 1 and 37 in phase 2, targeting the highest-consumption views and their downstream dependencies.

  • 15

    SAP tables rebuilt in the Raw Data Vault

    Reworked the most compute-intensive tables to cut redundant processing and enable reuse across downstream solutions.

Frequently asked questions

What was the goal?

To reduce Snowflake operating costs while maintaining reporting capabilities and supporting future growth.

How did UD4D reduce the costs?

Through a multi-phase optimization that analyzed compute consumption, optimized the most resource-intensive pipelines and reporting views, removed unused Data Vault objects and introduced an optimized Raw Data Vault model.

Which technologies were involved?

Snowflake, SAP IBP and SAP S/4HANA sources, Data Vault modeling and Dynamic Tables.

What were the results?

Daily Snowflake usage fell from 70 to 40 credits (over 40% lower, about $44K a year), 80 reporting views were optimized and 15 SAP tables were rebuilt in the Raw Data Vault, with reporting unchanged.

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