Today we're launching Supabase Analytics Buckets in private alpha. These are a new kind of storage bucket optimized for analytics, with built-in support for the Apache Iceberg table format.
Analytics buckets are integrated into Supabase Studio, power table-level views instead of raw files, and can be queried using the new Supabase Iceberg Wrapper, also launching in alpha.
Why Iceberg
Apache Iceberg is a high-performance, open table format for large-scale analytics on object storage. It brings the performance and features of a database to the flexibility of flat files.
We chose Iceberg for its bottomless data model (append-only, immutable history), built-in snapshotting and versioning (time travel), and support for schema evolution. Iceberg is also an open standard widely supported across the ecosystem. Supabase is committed to open standards and portability, and Iceberg aligns with that goal by enabling users to move data in and out without being locked into proprietary formats.
Setting up Analytics Buckets
Once your project has been accepted into the alpha release program, Analytics buckets can be created via Studio and the API. To create an analytics bucket, visit Storage > New bucket in Studio.
Analytics buckets are a separate bucket type from standard Supabase Storage buckets. You can't mix file types between the two.
They're stored in a new system table: storage.buckets_iceberg. These buckets are not included in the storage.buckets table and objects inside them are not shown in storage.objects. However, the listBuckets() endpoint returns a merged list of standard and analytics buckets for consistency with Studio and API consumers.
After creating the bucket, we're met with connection details. Copy the WAREHOUSE, VAULT_TOKEN, and CATALOG_URI values and and create an Iceberg namespace and table using your preferred method. The example below uses pyiceberg to create a namespace market with table prices:
_38import datetime_38import pyarrow as pa_38from pyiceberg.catalog.rest import RestCatalog_38from pyiceberg.exceptions import NamespaceAlreadyExistsError, TableAlreadyExistsError_38_38# Define catalog connection details (replace variables)_38WAREHOUSE= ..._38VAULT_TOKEN = ..._38CATALOG_URI= ..._38_38# Connect to Supabase Data Catalog_38catalog = RestCatalog(_38 name="catalog",_38 warehouse=WAREHOUSE,_38 uri=CATALOG_URI,_38 token=VAULT_TOKEN,_38)_38_38# Schema and Table Names_38namespace_name = "market"_38table_name = "prices"_38_38# Create default namespace_38catalog.create_namespace(namespace_name)_38_38df = pa.table({_38 "tenant_id": pa.array([], type=pa.string()),_38 "store_id": pa.array([], type=pa.string()),_38 "item_id": pa.array([], type=pa.string()),_38 "price": pa.array([], type=pa.float64()),_38 "timestamp": pa.array([], type=pa.int64()),_38})_38_38# Create an Iceberg table_38table = catalog.create_table(_38 (namespace_name, table_name),_38 schema=df.schema,_38)
Back in Studio, we can see the newly created our newly created Namespace with 0/1 connected tables
Click connect and select a Target Schema to map the Iceberg tables into. It is reccomended to create a standalone schema for your tables. Do not use the public schema because that would expose your table over the project's REST API.
Querying Analytics Buckets
Viewing an analytics bucket in Supabase Studio redirects you to the Table Editor. Instead of exposing raw Parquet files, the system shows a table explorer, powered by the Supabase Iceberg Wrapper.
The wrapper exposes Iceberg tables through a SQL interface, so you can inspect and query your data using Studio, or any SQL IDE. This makes analytical data feel like a native part of your Supabase project.
In this case the corresponding SQL query to access the data would be
_10select_10 *_10from market_analytics.prices;
Writing to Analytics Buckets
Writing is a work in progress. We're actively building Supabase ETL, which will allow you to write directly from Postgres into Iceberg-backed buckets. We'll also add write capability to the Supabase Iceberg Wrapper as soon as write support lands in the upstream iceberg-rust client library. This will complete the workflow of write → store → query, all inside Supabase.
Once live, that enables bottomless Postgres storage through shifting records into Analytics Buckets, all using open formats. As a bonus, Iceberg gets us time travel for free.
Alpha Launch Limits
Analytics Buckets are launching in private alpha with the following constraints:
- Two analytics buckets per project
- Up to five namespaces per bucket
- Ten tables per namespace
- Pricing will be announced in a few weeks
- You cannot store standard objects in analytics buckets
Roadmap and What's Next
This launch marks the first step toward full analytical capabilities in Supabase. Over the next few months, we'll introduce SQL catalog support so you can explore Iceberg table metadata directly from the database. Studio will also gain deeper integration for schema inspection, column-level filtering, and time travel queries. Our goal is to make Supabase a full-featured HTAP backend, where you can write, store, and query analytical data seamlessly.
Try It Out
Join the waitlist here to get early access and start working with bottomless, time-travel-capable analytics data inside Supabase.