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* feat(docs): add scaffolding for iceberg docs * docs:(storage/iceberg): Analytical buckets docs * Apply suggestions from code review Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Charis <26616127+charislam@users.noreply.github.com> * fix(docs): update analytics buckets docs * nav reorg * doc: analytics buckets limit section * fix ci errors --------- Co-authored-by: fenos <fabri.feno@gmail.com> Co-authored-by: Inian <inian1234@gmail.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
25 lines
1.3 KiB
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25 lines
1.3 KiB
Plaintext
---
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title: 'Analytics Buckets'
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subtitle: ''
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---
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<Admonition type="caution">
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This feature is in **Private Alpha**. API stability and backward compatibility are not guaranteed at this stage. Reach out from this [Form](https://forms.supabase.com/analytics-buckets) to request access
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</Admonition>
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**Analytics Buckets** are designed for analytical workflows on large datasets without impacting your main database.
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Postgres tables are optimized for handling real-time, transactional workloads with frequent inserts, updates, deletes and low-latency queries. **Analytical workloads** have very different requirements: processing large volumes of historical data, running complex queries and aggregations, minimizing storage costs, and ensuring these analytical queries do not interfere with the production traffic.
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**Analytics Buckets** address these requirements using [Apache Iceberg](https://iceberg.apache.org/), an open-table format for managing large analytical datasets efficiently.
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Analytics Buckets are ideal for
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• Data warehousing and business intelligence
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• Historical data archiving
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• Periodically refreshed real-time analytics
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• Complex analytical queries over large datasets
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By separating transactional and analytical workloads, Supabase makes it easy to build scalable analytics pipelines without impacting your primary Postgres performance.
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