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fixes minor typo in callout <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Documentation** * Updated link text formatting across storage analytics guides for improved consistency. <!-- review_stack_entry_start --> [](https://app.coderabbit.ai/change-stack/supabase/supabase/pull/46040?utm_source=github_walkthrough&utm_medium=github&utm_campaign=change_stack) <!-- review_stack_entry_end --> <!-- end of auto-generated comment: release notes by coderabbit.ai -->
35 lines
1.7 KiB
Plaintext
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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