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* Docs skeleton
* Create strcuture
* Revert "Docs skeleton"
This reverts commit 1788972dd2.
* Add analytics
* basic updates
* Fix docs reference
* Apply suggestions from code review
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* docs(storage): analytics buckets
* Apply suggestions from code review
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* Apply suggestions from code review
* format
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Co-authored-by: Danny White <3104761+dnywh@users.noreply.github.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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35 lines
1.7 KiB
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35 lines
1.7 KiB
Plaintext
---
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title: 'Analytics Buckets'
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subtitle: 'Store large datasets for analytics and reporting.'
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---
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<Admonition type="caution" title="This feature is in alpha">
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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.
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</Admonition>
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Analytics buckets enable analytical workflows on large-scale datasets while keeping your primary database optimized for transactional operations.
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## Why Analytics buckets?
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Postgres tables are purpose-built for transactional workloads with frequent inserts, updates, deletes, and low-latency queries. Analytical workloads have fundamentally different requirements:
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- Processing large volumes of historical data
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- Running complex queries and aggregations
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- Minimizing storage costs
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- Preventing analytical queries from impacting production traffic
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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.
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## Ideal use cases
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Analytics buckets are perfect for:
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- **Data warehousing and business intelligence** - Build scalable data warehouses for BI tools
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- **Historical data archiving** - Retain large volumes of historical data cost-effectively
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- **Periodically refreshed analytics** - Maintain near real-time analytical views
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- **Complex analytical queries** - Execute sophisticated aggregations and joins over large datasets
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By separating transactional and analytical workloads, Supabase lets you build scalable analytics pipelines without compromising your primary Postgres performance.
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