Solve: How to Write Data Back When Redshift Sharing Says No

Solve: How to Write Data Back When Redshift Sharing Says No

If you’ve tried to INSERT, UPDATE, or DELETE a Redshift table received via data sharing, you’ve already seen the wall:

Redshift doesn’t allow writes on shared (external) tables.

It’s not a permission issue. It’s not a bug. It’s the design.

But what if your use case truly requires it? What if the consuming side needs to write new data that must eventually live in the producer’s cluster?

You can’t write directly—but you can reroute.

The Simplest Path: Stage It in S3

The most approachable workaround uses a write → stage → ingest pattern. The consumer cluster writes to its own local table or S3 export, and the producer ingests that data on a controlled schedule.

Here’s a clean pattern that works:

1. Consumer writes to S3: 

sql

unload ('select * from staging_table')
to 's3://your-staging-bucket/data/'
iam_role 'arn:aws:iam::123456789012:role/redshift-unload-role'
format as json;

2. Producer pulls from S3: 

sql

copy target_table
from 's3://your-staging-bucket/data/'
iam_role 'arn:aws:iam::123456789012:role/redshift-copy-role'
format as json;

With this pattern, both teams retain control over their domain. The consumer writes locally. The producer decides when and how to accept changes.

API-Driven Ingestion: For Near Real-Time

If you're dealing with small, frequent inserts—like logging, transactions, or feedback—you can expose a write endpoint via API Gateway and Lambda. The consumer sends JSON payloads to an HTTPS endpoint, and the backend inserts into Redshift using the psycopg2 Python driver or the Redshift Data API.

This pattern adds complexity but opens the door to automation, validation, and even retries. It’s an enterprise-ready solution if governance and real-time flow matter.

Consider a Shared Data Lake Instead

If both sides need to write and query shared data, Redshift’s architecture may not be the best fit. A shared S3-based data lake using Apache Iceberg or Delta Lake can allow multi-writer coordination across Spark, Athena, Redshift Spectrum, and more.

Yes, it’s more complex. But if your teams need bidirectional flexibility, this model is more future-proof and open.

In Closing

You can’t change how Redshift handles data sharing—but you can design around it. Whether you pick S3 staging, API-driven pipelines, or a data lake foundation, the key is this:

Don’t fight Redshift’s boundary—respect it, and reroute.

Your write flow doesn’t need to be direct. It needs to be dependable.

* * * 

Written by Aaron Rose, software engineer and technology writer at Tech-Reader.blog.