Redshift vs. Snowflake for Analyst Workloads: What We See in Practice
The most common framing of the Redshift vs. Snowflake question focuses on infrastructure cost, data engineering features, or which one your cloud provider prefers. This is not that framing. This is about what analysts notice on a Tuesday afternoon when they're running queries.
We see both warehouses regularly through Row Zero connections. Neither is strictly better for analyst work. But they behave differently in ways that matter once you understand the mechanics.
How Each Warehouse Handles Ad-Hoc Queries
Redshift Classic is a cluster-based columnar store. Compute and storage are tightly coupled in that model. Your cluster is always on, which means queries start immediately, but you pay for idle capacity. Redshift Serverless changes this by decoupling storage from compute, though it introduces a cold-start cost when the endpoint has been inactive for a while.
Snowflake separates compute from storage completely. Virtual warehouses auto-suspend after a configurable idle period, typically two to fifteen minutes. When a suspended warehouse receives a query, it resumes in a few seconds. For teams that run queries in bursts across a work day, this is often cheaper than a permanently running cluster. For teams running continuous heavy workloads, Snowflake's per-second credit billing can cost more than a right-sized Redshift cluster.
Sort Keys and Micro-Partitions: Where the Difference Shows Up
If you run Redshift Classic and your analysts are writing queries that don't align with the table's sort key, you get full table scans. Redshift uses zone maps to skip blocks, but only for predicates that match the sort key order. A query like:
SELECT user_id, SUM(revenue)
FROM events
WHERE event_type = 'purchase'
AND created_at BETWEEN '2025-10-01' AND '2025-10-31'
GROUP BY 1;will scan very differently depending on whether the table is sorted by created_at or by user_id. If neither column is the sort key, Redshift scans the full table. Most analysts don't know the sort key, and they shouldn't have to, but the performance gap is real and shows up as wall-clock time.
Snowflake's micro-partition model handles predicate pushdown more automatically. Snowflake stores metadata about min and max values per column per micro-partition, so a date range filter on any column can skip irrelevant partitions without requiring a pre-declared sort order. For ad-hoc analyst queries that jump across different filter columns, this is a meaningful practical advantage.
Result Caching
Snowflake's query result cache is aggressive: if the exact same query runs twice within 24 hours and the underlying data hasn't changed, the second run returns instantly from the cached result. This is useful for dashboards and repeated analysis, but matters less for ad-hoc exploration where queries are rarely identical.
Redshift has leader node result caching for specific query patterns, but it's narrower in scope and less consistent across query shapes. In practice, analysts running exploratory queries see little benefit from Redshift's cache compared to Snowflake's.
Concurrency: Where Teams Hit Queues
Redshift Classic uses Workload Management queues to handle concurrent queries. If your WLM configuration has a small concurrency slot limit (often the default of five), analysts can queue behind each other. Concurrency Scaling adds capacity automatically, but there's a spin-up delay and cost implications depending on your pricing tier.
Snowflake's multi-cluster warehouses scale horizontally within a warehouse tier. When concurrency pressure increases, additional clusters activate automatically. For teams where ten analysts might run reports at the same time, Snowflake's model tends to be smoother without requiring WLM tuning.
What This Means in Row Zero
Row Zero pushes every query down to the warehouse via SQL. The spreadsheet doesn't pull data into its own execution layer. The performance characteristics above show up directly in how fast your spreadsheet updates.
If you're on Redshift and running queries that miss sort keys, you'll see the full scan cost. If you're on Snowflake and your virtual warehouse just resumed from suspend, you'll see a two-to-four second resume delay before the first query runs. These aren't Row Zero delays, they're warehouse delays. Row Zero surfaces them honestly rather than hiding them behind a loading spinner.
One Thing Both Get Right
Both warehouses handle large GROUP BY and window function queries well for columnar storage reasons. An analyst writing:
SELECT
DATE_TRUNC('week', order_date) AS week,
region,
SUM(revenue) AS total_revenue,
RANK() OVER (
PARTITION BY DATE_TRUNC('week', order_date)
ORDER BY SUM(revenue) DESC
) AS rank
FROM orders
WHERE order_date >= '2025-01-01'
GROUP BY 1, 2;will get a reasonable result on either warehouse for tables in the hundreds of millions of rows range, without needing to think about indexes or execution plans the way you would on a row-oriented database.
The analyst's bottleneck on both warehouses is almost never the aggregation itself. It's the scan before the aggregation: how many rows does the warehouse have to touch to find the ones you care about. Sort keys on Redshift and clustering keys on Snowflake address this, but they require someone to have configured them thoughtfully when the table was created. If that setup work wasn't done, ad-hoc analyst queries pay the full scan cost regardless of which warehouse you're on.
We're not saying Snowflake is the right choice because of these differences. For teams with steady, high-throughput workloads, Redshift's always-on cluster model is often more cost-effective. The right choice depends on your usage pattern, not on which benchmark post you read last.