Supabase Pipelines is now in Public Alpha

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Blog / product<br>Supabase Pipelines is now in Public Alpha<br>21 Jul 2026<br>6 minute read

Riccardo BusettiEngineering

Today we're moving Supabase Pipelines into public alpha. The biggest addition is schema change support: Pipelines can now detect supported schema changes in your source tables and apply them to the destination automatically. This release also brings a faster initial sync and a new destination request form for ClickHouse, Snowflake, and DuckLake.

Supabase Pipelines replicates your Supabase Postgres data to external analytical systems in near real time. It is powered by Supabase ETL, our open source change-data-capture pipeline written in Rust. Supabase ETL reads changes from Postgres through logical replication, copies existing table data, streams inserts, updates, deletes, and truncates, and writes them to your destination.

The first destination available to everyone in public alpha is Google BigQuery .

Why Supabase Pipelines?#

Postgres is excellent for transactional workloads: reading a user profile, inserting an order, updating a subscription, or serving your application.

Analytics workloads are different. They often scan large amounts of data, aggregate across many rows, and power dashboards, reports, notebooks, and downstream systems. Running those queries directly on your production database can add load to the same system your application depends on.

Supabase Pipelines gives you a reliable way to move production data into systems built for analytics, while keeping your application workload on Postgres.

You get:

A complete initial sync of your selected tables.

Near real-time replication after the initial sync finishes.

At-least-once delivery for database changes.

Destination-side tables that stay aligned with your source schema.

A managed Dashboard experience for creating, monitoring, and controlling pipelines.

The ability to add and remove tables to your replication pipeline without having to restart replication from scratch.

If you are deciding between Supabase Realtime and Supabase Pipelines, read Realtime or Pipelines? How to choose the right tool. The short version: Realtime is for live user experiences over WebSocket. Supabase Pipelines is for reliable data movement into analytical systems.

What changed since private alpha#

Since introducing Supabase Pipelines, we've been focused on making it faster, more predictable, and easier to operate.

We improved performance across the board, reduced memory usage, and lowered replication latency. This work is ongoing. Supabase Pipelines is still in alpha, and we're continuing to tune throughput, batching, and backpressure as we learn from more workloads.

We also improved the initial sync. It can now be parallelized across tables and within a table, which lets pipelines load existing data much faster before switching to ongoing replication.

The biggest new feature is schema change support .

Schema changes are now replicated#

When your Postgres schema changes in a supported way, Pipelines applies the same change to your destination automatically.

That means your BigQuery schema can stay up to date as your Postgres schema changes, without requiring a manual destination migration for every supported change.

The current version supports:

Adding columns.

Removing columns.

Renaming columns.

Changing column nullability and defaults.

This is an important step toward one-to-one replication between your operational database and your analytical destination.

There is more to do. We're working on expanding schema change support to cover more cases, including additional type changes and other table-level changes. Some destinations may also support the operations above in different ways due to their inherent limitations. For now, unsupported schema changes may still require manual handling depending on the change and destination.

How it works#

Supabase Pipelines uses Postgres logical replication under the hood.

When you create a pipeline:

You choose the tables you want to replicate.

Supabase Pipelines creates an initial sync of the selected tables.

The initial sync runs with parallel workers based on the configured parallelism level.

After the sync completes, the pipeline switches to ongoing replication.

New changes are read from the replication slot, batched, and written to the destination.

Supported schema changes are detected and applied to the destination schema.

The pipeline is designed for reliable data movement. If a destination is temporarily unavailable or the pipeline restarts, replication resumes from the last acknowledged position. It can also detect and recover from many transient failures automatically, while surfacing issues that require intervention.

Available destinations#

BigQuery is the first destination available to everyone in public alpha.

BigQuery is Google's serverless data warehouse. It works well for...