Analytics Buckets

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Expect rapid changes, limited features, and possible breaking updates. share feedback as we refine the experience and expand access.

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Analytics buckets enable analytical workflows on large-scale datasets while keeping your primary database optimized for transactional operations.

Why Analytics buckets?

Postgres tables are purpose-built for transactional workloads with frequent inserts, updates, deletes, and low-latency queries. Analytical workloads have fundamentally different requirements:

• Processing large volumes of historical data
• Running complex queries and aggregations
• Minimizing storage costs
• Preventing analytical queries from impacting production traffic

Analytics buckets address these requirements using Apache Iceberg, an open-table format specifically designed for efficient management of large analytical datasets.

Ideal use cases

Analytics buckets are perfect for:

• Data warehousing and business intelligence - Build scalable data warehouses for BI tools
• Historical data archiving - Retain large volumes of historical data cost-effectively
• Periodically refreshed analytics - Maintain near real-time analytical views
• Complex analytical queries - Execute sophisticated aggregations and joins over large datasets

By separating transactional and analytical workloads, Supabase lets you build scalable analytics pipelines without compromising your primary Postgres performance.