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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>
39 lines
1.6 KiB
Plaintext
39 lines
1.6 KiB
Plaintext
---
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title: 'Creating 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 use [Apache Iceberg](https://iceberg.apache.org/), an open-table format for managing large analytical datasets.
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You can interact with them using tools such as [PyIceberg](https://py.iceberg.apache.org/), [Apache Spark](https://spark.apache.org/) or any client which supports the [standard Iceberg REST Catalog API](https://editor-next.swagger.io/?url=https://raw.githubusercontent.com/apache/iceberg/main/open-api/rest-catalog-open-api.yaml).
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You can create an Analytics Bucket using either the Supabase SDK or the Supabase Dashboard.
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### Using the Supabase SDK
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```ts
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import { createClient } from '@supabase/supabase-js'
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const supabase = createClient('https://your-project.supabase.co', 'your-service-key')
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supabase.storage.createBucket('my-analytics-bucket', {
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type: 'ANALYTICS',
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})
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```
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### Using the Supabase Dashboard
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1. Navigate to the Storage section in the Supabase Dashboard.
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2. Click on "Create Bucket".
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3. Enter a name for your bucket (e.g., my-analytics-bucket).
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4. Select "Analytics Bucket" as the bucket type.
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<img alt="Storage schema design" src="/docs/img/storage/iceberg-bucket.png" />
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Now, that you have created your Analytics Bucket, you can start [connecting to it](/docs/guides/storage/analytics/connecting-to-analytics-bucket) with Iceberg clients like PyIceberg or Apache Spark.
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