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Eliminating a $50,000 Annual Expense by Migrating a Legacy Database to BigQuery

Eliminating a $50,000 Annual Expense by Migrating a Legacy Database to BigQuery

Eliminating a $50,000 Annual Expense for a Legacy Accounting Database

For many businesses, legacy systems are a necessary burden—expensive to maintain yet still required for historical data access. This was the challenge faced by a mid-sized company that had already migrated to a modern accounting platform but continued to pay $50,000 annually just to keep an old MS SQL database running for reference.

The system required ongoing IT maintenance, including SSL certificate renewals, backups, and user management, despite being accessed only for historical records. The costs and operational overhead were adding up, and the company needed a smarter solution.

By migrating the legacy database to Google BigQuery, they reduced costs for this system by 99.8%, eliminating infrastructure expenses while maintaining seamless, secure access to historical data. This case study explores how they made the transition without disrupting business operations.

The Costly Burden of Legacy Systems

Even after migrating to a modern accounting platform, this company couldn’t fully retire its old MS SQL-based system. The historical data stored in it was still needed for accounts receivable and accounts payable inquiries, forcing the business to keep the system online.

However, maintaining the legacy database came with significant costs and inefficiencies:

  • $50,000 in annual expenses for infrastructure, licensing, and support.
  • Ongoing IT maintenance, including SSL certificate renewals, backups, and system updates.
  • User friction, with employees juggling extra logins due to a lack of Single Sign-On (SSO).
  • Security risks, as the system was still writable, making it vulnerable to accidental or intentional data modifications.

Despite these costs, the system was rarely used beyond historical data lookups. The company needed a way to retain access without the financial and operational burden of maintaining a full database server.

That’s when we worked with them to explore Google BigQuery as a cost-effective alternative.

he Game-Changing Solution: Migrating to Google BigQuery

To eliminate the high costs of maintaining their legacy MS SQL database, the company needed a scalable, low-maintenance alternative that would:

Preserve access to historical financial data ✅ Eliminate costly infrastructure and licensing fees ✅ Reduce IT workload by removing system maintenance requirements ✅ Ensure data integrity with a secure, read-only format

Seamless Migration in Just One Week

The company opted to migrate the legacy database to Google BigQuery, Google’s fully managed, serverless data warehouse. The transition was fast and straightforward, requiring:

  • Exporting the MS SQL database as structured data files
  • Loading the data into Google BigQuery without the need for cleaning or transformation
  • Building simple, user-friendly reports in Looker Studio to allow accounting staff to quickly retrieve historical invoices and payment records

Within one week, the company had fully transitioned, turning a costly, high-maintenance system into an efficient, pay-as-you-go solution.

Also See: Choosing the right data warehouse!

A Truly Read-Only System for Data Integrity

Unlike the legacy system, where data could still be modified, the BigQuery solution ensured historical records remained untouchable. This removed security risks while allowing employees to search and retrieve past transactions instantly, with no IT intervention.

The result? A dramatic reduction in costs, improved efficiency, and a frustration-free experience for both IT and accounting teams.

Massive Cost Savings and Long-Term Impact

By migrating their legacy MS SQL database to Google BigQuery, the company reduced costs for this system by 99.8%. What once required a $50,000 annual budget for infrastructure, licensing, and maintenance was now a pay-as-you-go model costing just a few dollars per month.

Key Results:

📉 99.8% Cost Reduction – Eliminated expensive servers, licensing, and IT overhead. ⚡ Zero Maintenance – No more backups, SSL renewals, or server patching. 🔒 Enhanced Security – Read-only access prevented accidental or malicious data changes. 🔍 Seamless Data Access – Looker Studio dashboards enabled quick, self-service reporting. 👨‍💻 Freed IT Resources – No more time spent maintaining an unused system.

A Model for Future Cost Optimization

This case study highlights how modernizing legacy systems doesn’t have to be complex or expensive. By leveraging cloud-based solutions like Google BigQuery, businesses can:

Reduce operational costs while retaining access to critical data ✔ Eliminate unnecessary IT maintenance and system complexity ✔ Improve security and user experience with simplified access to historical records

For companies still holding onto costly legacy systems for “just in case” data access, this migration strategy offers a proven path to massive savings and increased efficiency.

Get Ahead of the Threat—Before It Gets Ahead of You.

Stop Overpaying for Legacy Systems—Make the Smart Move to BigQuery

📉 Cut costsEliminate IT maintenance 🔍 Access historical data instantly 🚀 Start your transition today! At Inventive HQ, learn how we can help you modernize your systems and slash unnecessary expenses.

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Frequently Asked Questions

Find answers to common questions

For a typical small business with 50-200GB database: Cloud SQL instance (2 vCPU, 8GB RAM) costs $200-300/month. Migration project costs: $5,000-15,000 depending on complexity. DIY with Google Database Migration Service: minimal cost but 40-80 hours of internal IT time. Hiring a consultant: $150-250/hour for 30-60 hours = $4,500-15,000. Total first-year cost: $7,400-19,600 (migration + 12 months hosting). Compare this to on-prem SQL Server licensing ($1,500-3,000/year) plus server hardware ($5,000-10,000 amortized over 5 years). Cloud breaks even in year 2-3 for most SMBs.

90% of standard SQL Server databases can lift-and-shift with zero code changes. Google Database Migration Service handles this automatically. Exceptions requiring rework:

  1. Databases using SQL Agent jobs (need converting to Cloud Scheduler)
  2. Heavy use of SQL Server-specific features like Analysis Services
  3. Applications with hard-coded server names.

Migration time: 2-8 hours for 50GB database, 24-48 hours for 500GB+. Plan 2-4 weeks for testing. If your app uses standard SQL queries and Entity Framework, expect zero application changes.

Trade-offs: (1) Less control—you can't RDP into the server or install custom DLLs, (2) Slight vendor lock-in (although PostgreSQL is portable), (3) Network latency if app servers are on-prem (10-50ms vs. 1-5ms local). Benefits: (1) No more patching—Google handles it, (2) Automated daily backups with point-in-time recovery, (3) 99.95% uptime SLA vs. your on-prem server's 95-99%. Real gotcha: egress fees. If you're pulling 500GB/month of data out of Google Cloud, that's $40-80/month in transfer fees. Keep app servers in same cloud region to avoid this.

If your app is compatible with PostgreSQL (most ORMs work with both): PostgreSQL is 40-60% cheaper. Same 2 vCPU/8GB RAM instance: SQL Server costs $300-400/month, PostgreSQL costs $150-200/month. Migration complexity: SQL Server to Cloud SQL is easier (2-4 weeks), SQL Server to PostgreSQL requires code changes (4-8 weeks testing queries, stored procedures). Best path: stick with Cloud SQL if you have complex stored procedures or need SQL Server compatibility. Switch to PostgreSQL if you're running basic CRUD operations and want long-term cost savings.

Set up billing alerts immediately: $200, $400, $600 thresholds. Main cost drivers:

  1. Storage grows 20-40% per year—set up automatic cleanup of old data
  2. High IOPS during business hours (10,000+ IOPS costs $300-500/month)
  3. Cross-region backups ($0.02/GB/month adds up).

Use committed use discounts: commit to 1 year, save 25%; commit to 3 years, save 52%. For a $300/month instance, this saves $90-155/month. Monitor weekly for first 3 months. Common mistake: leaving development databases running 24/7. Shut down dev instances nights/weekends, save 60%.

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