Redshift and Databricks. Same app.

Who each is for

Redshift is the choice for a team that's standardized on AWS and wants a managed, columnar warehouse for structured, already-modeled data. It's a good fit when the data is already relational and the team wants predictable SQL performance without managing servers directly.

Databricks is the choice when raw or semi-structured data needs real transformation before it's query-ready, and when the same platform needs to support both SQL analysts and people writing Spark or Python against the same tables. A Databricks SQL warehouse is the SQL-facing layer on top of that, and it's what QueryFlow connects to.

Side by side

One difference worth calling out plainly

Databricks works as a Data Sync target with Insert, Update and Upsert in 1.7. Redshift does not, in this release, it's a query source and a scheduled-job source, and results can be delivered to S3, SFTP, a local file or email, but Data Sync itself isn't wired up to write into Redshift yet. If your workflow specifically needs field-mapped writes into a target table, that currently points you at Databricks or BigQuery, not Redshift.

A worked example

Both speak SQL close enough to standard that the same logical query barely changes:

-- Redshift
SELECT region, SUM(revenue) AS total
FROM analytics.public.orders
WHERE order_date >= DATEADD(day, -7, GETDATE())
GROUP BY region;

-- Databricks
SELECT region, SUM(revenue) AS total
FROM gold.sales.orders
WHERE order_date >= current_date() - INTERVAL 7 DAYS
GROUP BY region;

The real difference between the two shows up further upstream, in how the orders table got built in the first place, not in a query like this one against an already-clean table. If that table started life as a pile of raw JSON events, Databricks is more likely where the transformation happened; if it was structured from the start, Redshift handling it directly is just as reasonable.

Browsing each one

Redshift's Explorer lists databases, then schemas, then tables, the standard relational three levels. Databricks adds Unity Catalog on top: catalog, then schema, then table, which is one level deeper if your Databricks setup uses more than one catalog to separate raw, cleaned and production-ready data. Either way, the right-click actions on a table, an auto-generated SELECT with a LIMIT, Insert table name, or Copy name, work the same, so switching between the two mid-session doesn't mean relearning the sidebar.

Setup effort, honestly

Redshift's IAM setup tends to be a one-time lift handled by whoever manages AWS access, after which the connection itself is just picking the right keys. Databricks's setup is arguably simpler for an individual: generate a personal access token yourself, copy two fields off the warehouse's own connection page, and you're connected, no AWS console involved. A service principal for a shared or automated Databricks connection is a bit more setup than a personal token, but still doesn't touch AWS IAM at all.

When to pick each

Pick Redshift when the data is already structured and the team wants a straightforward SQL warehouse on AWS. Pick Databricks when raw data needs real transformation before analysts can use it, or when Python and Spark work needs to sit next to the SQL. Plenty of AWS-based teams run both: Redshift for the modeled marts, Databricks upstream for the messier processing.