Whitepaper: Scalable BI Architecture with Google Cloud

Executive Summary

This whitepaper presents a proven framework for designing and implementing scalable Business Intelligence architectures on Google Cloud. Based on real implementations for LATAM companies, the framework integrates FinOps practices, semantic governance, and AI automation.

Key benefits:

• 40-60% reduction in BigQuery operational costs
• 2-3x improvement in dashboard performance
• 100% consistency in critical business metrics
• Scalability 10x without architectural redesign

Design Principles

1. Separation of Concerns: Each layer has a clear responsibility. Ingestion, transformation, semantic, and presentation are decoupled.
2. Cost-Aware by Design: All architectural decisions consider the cost impact. Partitioning, clustering, and caching are requirements, not optional.
3. Governance from Day One: Security, auditing, and lineage are implemented from the start, not as afterthoughts.
4. Single Source of Truth: The Semantic Layer is the only authorized source of metrics and dimensions. Duplication is not allowed.
5. Automation over Manual Work: Testing, deployment, monitoring, and alerts are fully automated.

Implementation Roadmap

5 Tips to Optimize BigQuery Costs

BigQuery is a powerful tool, but without the right optimizations it can become a significant expense. These 5 tips are based on real optimizations we have implemented in production projects.

1. Date partitioning is non-negotiable
   If your table is larger than 1GB and grows daily, it MUST be partitioned. Partitioning dramatically reduces data scanning. Example: a query over 365 days of data can be reduced to scanning only 1 day with a proper WHERE filter.
   
   Typical savings: 95-98% in cost per query
2. Use clustering for frequent filter columns
   After partitioning by date, add clustering on the columns you frequently use in WHERE or JOIN. For example: user_id, product_id, region. BigQuery will physically organize the data to optimize these queries.
   
   Typical savings: 30-60% additional over partitioning
3. Avoid SELECT * at all costs
   BigQuery charges per bytes scanned. SELECT * scans all columns, even those you don't need. Specify ONLY the columns you will use. If your table has 50 columns and you only need 5, you are paying 10x more than necessary.
   
   Typical savings: 80-90% in exploratory queries
4. Monitor INFORMATION_SCHEMA.JOBS daily
   BigQuery has metadata tables that allow you to see all executed queries, their cost, who executed them, and how long they took. Set up a dashboard or AI agent to review this daily and send alerts when it detects expensive queries.
   
   Benefit: Early detection of problematic queries before they impact the budget
5. Consider flat-rate slots for predictable usage
   If your monthly spend exceeds $2,000 on BigQuery on-demand, analyze migrating to flat-rate slots. The break-even is around 400TB processed/month. For predictable workloads, flat-rate can reduce costs by 40-60%.
   
   Typical savings: 40-60% for stable and predictable workloads

Looker PDTs: When to Use Them and When Not

Persistent Derived Tables (PDTs) are one of Looker's most powerful features, but also one of the most misused. Here is a practical guide based on real projects.

When to USE PDTs

• Executive dashboards with multiple users: If you have a dashboard that 20+ people open several times a day, a PDT will pre-calculate the results and serve them from cache. Typical reduction: from 15s to 0.5s per load.
• Complex and expensive queries: If your query does multiple JOINs on large tables and costs $5+ each time it runs, it's worth pre-calculating it. The cost of materializing the PDT is quickly amortized.
• Heavy aggregations: If you are calculating aggregated metrics over millions of rows (e.g., revenue by day, by product, by region), a PDT with the pre-calculated aggregation will improve performance dramatically.

When NOT to use PDTs

• Constantly changing data: If your data updates every minute, a PDT with a 1-hour refresh doesn't work. Users will see stale data and lose confidence.
• Fast and cheap queries: If your query takes 2 seconds and costs $0.01, you don't need a PDT. The overhead of maintaining the PDT is not worth it.
• Infrequent use: If the dashboard is used once a week, it doesn't justify maintaining a PDT constantly refreshing. Better to use direct query.

FinOps for Data Teams: Where to Start

FinOps (Financial Operations) applied to data platforms is the practice of optimizing costs while maintaining or improving service quality and speed. It's not about cutting budgets, but about maximizing value for every dollar invested.

1. Visibility: You can't optimize what you don't measure

The first step is to implement real-time cost monitoring:

• Cost dashboard by team: Use labels in BigQuery to separate queries by team (marketing, finance, operations). This way you can identify who consumes more resources.
• Expensive query tracking: Set up alerts when a query exceeds a threshold (e.g., $10). Review it immediately and suggest optimizations.
• Trend analysis: Monitor whether costs grow proportionally to the value generated. If your costs grow 50% but your business only grew 10%, there's a problem.

2. Accountability: Each team owns its costs

Assign monthly budgets per team and send weekly reports. When teams see the impact of their decisions, they naturally optimize.

3. Optimization: Quick wins first

Start with low-effort, high-impact optimizations:

1. Week 1: Identify and partition the 5 largest tables
2. Week 2: Replace SELECT * in the 10 most expensive queries
3. Week 3: Implement 3 PDTs for executive dashboards
4. Week 4: Set up automatic alerts for queries > $10

With these quick wins you typically achieve 30-40% cost reduction in the first month, which generates momentum for deeper optimizations.