
# 

Analytics Buckets

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Store large datasets for analytics and reporting.

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##### This feature is in alpha

Expect rapid changes, limited features, and possible breaking updates. [share feedback](https://github.com/orgs/supabase/discussions/40116) 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?[#](#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](https://iceberg.apache.org/), an open-table format specifically designed for efficient management of large analytical datasets.

## Ideal use cases[#](#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.
