Big Data Energy

Client Challenge / Project Overview

Big Data Energy faced a critical financial threat due to the architecture of its data operations on Google Cloud:

• Unsustainable Cloud Consumption: The company's Google Cloud BigQuery bill had reached nearly $10,000 USD per month due to the excessive resources consumed by its data processing logic.

• Inefficient Processing Logic: An endpoint designed to extract BigQuery data to generate a CSV file (used by a vendor) was consuming enormous resources because every query scanned unnecessarily large portions of the tables.

• Mandate for Change: The need for optimization was accelerated when Google announced a new limit on data processing in BigQuery, effective September 1st, forcing Big Data Energy to immediately audit their consumption.

Solution Implemented

Blue People executed a comprehensive Optimization Plan focused on reducing the volume of data BigQuery was required to scan per query.

• Storage Architecture Redesign: The team audited the structure of the data and improved how information was organized within the cloud.

• Advanced Query Optimization (Partitioning & Clustering): They implemented partitioning and clustering on the BigQuery tables. This strategic move grouped data by criteria (like account and meter numbers) and ensured queries only scanned necessary data segments, not the entire table.

• Strategic Logic Relocation: Heavy data manipulation logic was moved out of the BigQuery layer and executed using a cost-efficient Google Cloud Function (serverless).

• Query Code Improvement: The SQL queries were refactored for maximum efficiency, a task partially supported by the use of ChatGPT.

Key Features

• Implementation of data partitioning and clustering to limit data scanning and ensure surgical data retrieval.

• Successful migration of data processing logic to a cost-effective Google Cloud Function.

• The ability to audit and replicate high-cost queries to empirically demonstrate a 98% efficiency gain.

Key Results and Benefits

The strategic intervention provided immediate and massive financial ROI for Big Data Energy:

• 98% Reduction in Data Processed Per Query: A test on a previous high-cost query showed processing volume dropped from 2.5 Terabytes (TB) to just 300 Gigabytes (GB). This translated into a 98% decrease in both data processed and billing cost for that specific operation.

• Massive Monthly Cost Reduction: The Big Data Energy Google Cloud BigQuery bill dropped from nearly $10,000 USD to approximately $3,000+ USD in the first month following the optimization.

• Projected Annual Savings of $72,000 USD: The sustained monthly reduction is estimated at $6,000 USD, projecting an estimated annual savings of $72,000 USD for Big Data Energy.

Conclusion

The Big Data Energy case study powerfully demonstrates how specialized cloud architecture consulting and FinOps strategy generate immediate, quantifiable business outcomes. By strategically restructuring BigQuery and optimizing the querying logic, Blue People transformed a major operational cost center (nearly $10,000/month) into a managed expense. This resulted in a projected annual saving of $72,000 USD, immediately highlighting the strategic value of technically sound and cost-efficient cloud architectures. The project successfully solved the billing crisis, future-proofed Big Data Energy’s data operations, and earned immediate appreciation from the client team for the "outstan