86% Drop in Data Processing Costs for a U.S. Marketing Tech Company

How a Platform Migration and Query Optimization Saved $154K Annually

Agus Velazquez

Feb 20, 2024

An American marketing technology company partnered with Blyze Labs to address its growing data inefficiencies and rising cloud costs. Their previous tech stack had become too complex and costly to scale, with pipelines running on outdated infrastructure and poor query practices. In just three months, Blyze Labs led a full optimization and platform migration that reduced processing costs by 86%, saving approximately $154,000 annually while modernizing their data infrastructure.

At a Glance

💸 86% reduction in data processing costs
💰 $154,000 USD in annualized savings
🔄 Query and pipeline refactoring across core data workflows
🚀 Platform migration to Databricks for unified orchestration

Industry: Marketing Technology
Tech Stack: Google BigQuery · Databricks · SQL

Challenge

The client was facing significant operational and financial strain due to:

Poorly optimized SQL queries consuming large amounts of compute
Multiple orchestration tools increasing maintenance overhead
Non-incremental data pipelines leading to full table refreshes daily
A fragmented and unscalable data stack

They needed to regain control of their cloud spend while improving the scalability and maintainability of their data workflows.

Our Approach

Blyze Labs conducted a detailed technical assessment and implemented a structured solution across three core workstreams:

Query Refactoring:
Identified and optimized the most expensive queries using SQL best practices to reduce data scanned and runtime.
Incremental Data Loading:
Introduced incremental refresh logic to large, frequently updated tables, eliminating full reloads and improving efficiency.
Platform Migration to Databricks:
Migrated scheduled workflows from disparate tools to Databricks, consolidating orchestration and streamlining operations under a single platform.