5 Tips for Mastering BigQuery Performance

Google BigQuery is one of the most powerful data warehouses in the cloud, offering unmatched scalability, near real-time analytics, and serverless execution. But as with any technology, performance doesn’t just depend on the platform—it depends on how you use it.

Over the years, I’ve worked with enterprises using BigQuery for everything from retail churn prediction to AI-powered ETL pipelines. Along the way, I’ve identified a handful of best practices that consistently deliver faster queries and lower costs.

Here are five tips for mastering BigQuery performance:

1. Partition and Cluster Your Tables

One of the most common mistakes I see is teams running queries against entire datasets when they only need a subset of the data.

Partitioning splits your data by a column (like DATE or TIMESTAMP), so BigQuery only scans the relevant partitions.

Clustering further organizes your data within partitions based on fields like customer_id or region.

👉 This combination drastically reduces the amount of data scanned and makes queries much faster and cheaper.

2. Use the Storage Write API for Efficient Ingestion

Instead of loading data with batch jobs or streaming inserts, the BigQuery Storage Write API allows you to ingest data at scale with:

• Lower latency
• Higher throughput
• Automatic deduplication

When building real-time analytics pipelines with Pub/Sub and Dataflow, the Storage Write API keeps ingestion costs under control while ensuring clean, reliable datasets.

3. Optimize Queries with SELECT Only What You Need

BigQuery pricing is based on the amount of data scanned. If you’re running SELECT * queries, you’re paying to scan every column—even those you don’t need.

• ✅ Always select only the necessary columns.
• ✅ Use sharded queries where possible.
• ✅ Test queries in the console with EXPLAIN to see how much data will be scanned before running them.

Even small adjustments here can save gigabytes (and thousands of rupees) in scan costs.

4. Leverage Materialized Views and Result Caching

BigQuery automatically caches query results for 24 hours. If you rerun the same query, it’s free.

For commonly used aggregations (like daily sales totals), use Materialized Views. They store pre-computed results and update incrementally as new data comes in. This cuts query times from minutes to seconds.

👉 Example: Instead of recalculating churn probability features daily, materialize the features once and reuse them.

5. Monitor Performance with Query Execution Plans

BigQuery gives detailed insights into how queries run. Use:

• EXPLAIN / EXPLAIN ANALYZE → See stage-by-stage execution details.
• Information Schema → Monitor query performance, slots used, and costs across your project.
• Looker Studio Dashboards → Build monitoring dashboards for query trends and cost optimization.

This feedback loop is key: by analyzing slow queries, you can spot inefficiencies, rewrite them, and optimize schemas.

Final Thoughts

BigQuery is more than just a data warehouse—it’s a scalable analytics engine that can power AI, ML, and BI workloads when used correctly. By mastering partitioning, ingestion, query optimization, caching, and monitoring, you can unlock its full potential while keeping costs predictable.

At Syncverse Solutions, we apply these principles daily in client projects—whether it’s building contract-first ETL pipelines, real-time retail analytics, or Vertex AI integrations.

📌 Remember: Performance isn’t about running queries faster. It’s about creating a foundation where your business gets answers in seconds, not hours.

✍️ Written by Saurabh Kaushik, Founder of Syncverse Solutions