Analytics Buckets

Store large datasets for analytics and reporting.

This feature is in alpha

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

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.