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Supabase · Storage · all subjects

analytics buckets

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Create analytics bucket with JavaScript SDK

Use supabase.storage.analytics.createBucket() to create an analytics bucket in JavaScript. Example: const { data, error } = await supabase.storage.analytics.createBucket('analytics-data')

Create analytics bucket with Python SDK

Use supabase.storage.analytics().create() to create an analytics bucket in Python. Example: response = supabase.storage.analytics().create('analytics-data')

Create analytics bucket via dashboard

Navigate to Storage section in Supabase Dashboard, click Create Bucket, enter a bucket name, select Analytics Bucket as the bucket type, and click Create.

Analytics buckets in Private Alpha

Analytics buckets feature is in Private Alpha. API stability and backward compatibility are not guaranteed at this stage. Request access through the analytics-buckets form.

Analytics buckets enable separation of analytical and transactional workloads

Analytics buckets enable analytical workflows on large-scale datasets while keeping your primary database optimized for transactional operations. They address the different requirements of analytical workloads: processing large volumes of historical data, running complex queries and aggregations, minimizing storage costs, and preventing analytical queries from impacting production traffic.

Analytics buckets ideal use cases

Analytics buckets are ideal for data warehousing and business intelligence (building scalable data warehouses for BI tools), historical data archiving (retaining large volumes of historical data cost-effectively), periodically refreshed analytics (maintaining near real-time analytical views), and complex analytical queries (executing sophisticated aggregations and joins over large datasets).

Postgres tables are optimized for transactional workloads not analytical workloads

Postgres tables are purpose-built for transactional workloads with frequent inserts, updates, deletes, and low-latency queries. Analytical workloads have fundamentally different requirements and would not perform efficiently on Postgres when handling large volumes of historical data with complex queries and aggregations.

Analytics buckets per project limit

During the alpha stage, the default limit is 2 analytics buckets per project. This limit can be adjusted on a case-by-case basis.

Namespaces per analytics bucket limit

During the alpha stage, the default limit is 10 namespaces per analytics bucket. This limit can be adjusted on a case-by-case basis.

Tables per namespace limit

During the alpha stage, the default limit is 10 tables per namespace. This limit can be adjusted on a case-by-case basis.

Analytics buckets feature status

Analytics buckets is an alpha feature with rapid changes, limited features, and possible breaking updates expected.

Analytics buckets pricing during alpha

Analytics buckets are free to use during the alpha phase. However, users will still be charged for the underlying egress costs associated with these buckets.

Analytics buckets alpha feature status

Analytics buckets are in alpha and users should expect rapid changes, limited features, and possible breaking updates. Supabase requests feedback via their GitHub discussions.

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