If you’re comparing Snowflake vs Amazon Redshift for analytics-heavy workloads, it’s tempting to search for a single “winner” based on a few benchmark charts. But in real life, benchmarks can be misleading unless you understand what’s being measured, how it was configured, and what your workload actually looks like.

This guide breaks down how to think about Snowflake vs Redshift benchmarks for intensive analytical workloads, what typically drives performance, where each platform tends to shine, and how to run a benchmark you can trust—without turning it into a months-long science project.

Why “Benchmarks” Often Don’t Translate to Your Real Workload

A benchmark is only as useful as its resemblance to your environment. Most public comparisons unintentionally (or intentionally) bias results through:

• Different warehouse sizes / cluster sizes (not cost-equivalent)
• Caching effects (warm vs cold runs)
• Concurrency settings (queueing and WLM vs separate virtual warehouses)
• Storage layout differences (distribution/sort keys, micro-partition pruning, compression)
• Data ingestion method (COPY vs streaming vs ELT)
• Query mix (simple scans vs complex joins, window functions, UDFs)
• Governance & security features enabled (which can affect performance)

If your goal is a decision you can defend, the best approach is to benchmark based on:

1) your top queries, and

2) your expected concurrency, and

3) cost per query / cost per hour, not just raw runtime.

The Core Architectural Differences That Impact Benchmark Results

Before diving into typical benchmark patterns, it helps to connect performance behavior to architecture.

Snowflake: Separation of Compute and Storage (and Why It Matters)

Snowflake’s compute runs in virtual warehouses that can be resized independently. This usually makes it strong for:

• Bursting compute for heavy transformations
• Spinning up isolated compute for different teams
• Handling concurrency by scaling out warehouses

It also means your performance benchmark can swing dramatically depending on warehouse size and whether results are cached.

Redshift: Cluster-Based Performance with Strong Control Knobs

Redshift traditionally relies on cluster sizing and tuning (though serverless changes the story). It tends to perform well when:

• You have predictable workloads and can tune for them
• You optimize distribution styles, sort keys, and WLM
• You want tight integration with AWS services and networking controls

In benchmarks, Redshift performance can look incredible if tuned well—and disappointing if it’s treated like a “set it and forget it” system.

For deeper security and compliance considerations (which often affect platform selection beyond speed), see: Snowflake vs Amazon Redshift: the complete guide to security and compliance.

What “Intensive Analytical Workloads” Usually Mean (and How They Stress Systems)

Not all “heavy analytics” is the same. Most enterprise workloads fall into a few benchmark-relevant buckets:

1) Large Table Scans + Aggregations

Examples:

• Daily metrics across billions of events
• Funnel analysis over clickstream data
• Big grouping sets and rollups

What matters:

• Columnar storage efficiency
• Partition pruning / zone maps
• Effective caching (when allowed)

2) Join-Heavy Star/Snowflake Schemas

Examples:

• Customer 360 models
• Multi-fact joins with slowly changing dimensions (SCD2)
• Data marts powering BI dashboards

What matters:

• Join strategies and statistics
• Data distribution (especially for Redshift)
• Clustering / micro-partition pruning (Snowflake)

3) Concurrency-Heavy BI + Ad Hoc Exploration

Examples:

• 200+ dashboard users at 9am
• Mixed workloads (scheduled ELT + analysts running custom SQL)

What matters:

• Isolation between workloads
• Queueing behavior
• Ability to scale out without destabilizing performance

4) Transformation-Heavy ELT / Feature Engineering

Examples:

• dbt transformations at scale
• Building ML features from event logs
• Incremental models and backfills

What matters:

• Compute elasticity
• Spill to disk behavior
• Cost efficiency for long-running jobs

If your “analytics-intensive” use case also includes near-real-time pipelines, the upstream architecture can dominate warehouse performance. A practical starting point is: Apache Kafka explained: your practical guide to realtime data processing and streaming.

Typical Benchmark Outcomes: Where Snowflake Often Wins vs Where Redshift Often Wins

Below are common benchmark patterns teams report—assuming each platform is competently configured.

Snowflake Often Looks Better When…

• Concurrency is high, especially with multiple teams and mixed workloads
• You need fast elasticity (scale up/down quickly, clone environments, isolate workloads)
• You want minimal tuning to reach “good enough” performance
• You run frequent, repeatable queries that benefit from caching and optimized storage

Benchmark watch-out: If the test allows warm cache runs, Snowflake can look unrealistically fast compared to a cold start scenario.

Redshift Often Looks Better When…

• You can invest in tuning (sort keys, dist keys, WLM) aligned to a stable workload
• You’re deeply integrated into AWS and want consistent networking/security patterns
• You have predictable reporting windows and can right-size clusters for them
• You have workloads that benefit from carefully planned data distribution

Benchmark watch-out: Redshift can be benchmarked in a “highly tuned lab condition,” which may not match what your team can maintain long term.

Performance Is Only Half the Benchmark: Include Cost per Workload

A benchmark that ignores cost can lead to an expensive surprise later.

Metrics worth tracking in your Snowflake vs Redshift benchmark

For each test scenario, capture:

• Runtime (p50/p95): average and worst-case
• Cost per query: estimate credits vs cluster cost allocation
• Throughput: queries/hour under concurrency
• Queue time: especially important for BI
• Failure rate: timeouts, memory spills, retries
• Operational overhead: tuning effort, maintenance time, on-call load

A platform that’s 20% faster but 2x the cost—or significantly harder to operate—may not be the “winner.”

How to Run a Benchmark You Can Trust (Practical Checklist)

Here’s a realistic approach to benchmarking Snowflake vs Redshift for intensive analytics without overengineering it.

Step 1: Choose the Right Dataset (and Size It Up Honestly)

Use either:

• A representative production snapshot (preferred), or
• A standard dataset (TPC-DS/TPC-H) scaled to match your data volume

If you only benchmark at small scale, you’ll mostly measure caching and metadata efficiency—not true scan/join pressure.

Step 2: Define Query Suites Based on Business Reality

Create three suites:

1. BI suite: dashboard queries, slices, filters, top-N
2. Analyst suite: exploratory joins, window functions, semi-complex SQL
3. Batch suite: ELT transformations, backfills, large aggregations

Step 3: Test Cold and Warm Cache Separately

Run each suite:

• Cold: after cache-clearing steps (as much as possible)
• Warm: repeated runs to simulate dashboard refresh cycles

Report both—because both matter.

Step 4: Test Concurrency, Not Just Single-Query Speed

Single-query performance is only one slice of “analytics-intensive.”

• Simulate 10, 50, 100 concurrent users
• Mix query types (short + long)
• Track queue time and p95 latency

Step 5: Normalize for Cost

Do not compare:

• “Snowflake Medium” vs “Redshift large cluster” without cost parity

Instead, compare:

• Same hourly cost bands, then evaluate runtime and throughput

Step 6: Keep Configuration Transparent

Document everything:

• Snowflake warehouse sizes, auto-suspend, multi-cluster settings
• Redshift node types, RA3 vs serverless, WLM queues, sort/dist choices

That documentation becomes your decision audit trail.

Real-World Example Benchmark Scenarios (That Mimic Common Enterprise Patterns)

Scenario A: “Morning Dashboard Rush”

• 150 users hit Power BI/Tableau dashboards between 8–10am
• Queries are repetitive but concurrent
• Data updates hourly

What tends to matter:

• Concurrency scaling
• Workload isolation (ELT shouldn’t crush dashboards)
• Predictable p95 latency

Scenario B: “End-of-Month Financial Close”

• Huge joins, rollups, and reconciliation logic
• Long-running SQL with heavy aggregations
• High importance, lower concurrency

What tends to matter:

• Sustained performance over hours
• Spill behavior and stability
• Cost control during peak compute

Scenario C: “Product Analytics on Event Data”

• Billions of clickstream rows
• Frequent re-aggregation
• Analysts iterating quickly with semi-ad hoc queries

What tends to matter:

• Scan efficiency and pruning
• Iteration speed (time-to-first-result)
• Cost per exploration hour

If your event analytics depends on reliable ingestion and ELT patterns, you may also want to align your benchmark with your pipeline toolchain. A helpful reference for modern ELT thinking is: From ETL to ELT: a practical playbook for building modern data pipelines with Airbyte and dbt.

Key Takeaways: Choosing Between Snowflake and Redshift Based on Benchmarks

When teams say “Snowflake is faster” or “Redshift is cheaper,” they’re usually summarizing a specific workload under specific assumptions.

Use this decision lens:

• Choose Snowflake when you value elasticity, workload isolation, and concurrency scaling with less tuning.
• Choose Redshift when you have AWS-native constraints, a tunable and stable workload, and the team maturity to manage distribution/WLM optimization—or when Redshift’s pricing model aligns better with predictable usage.

The best benchmark is the one that mirrors your top 20 queries, your peak concurrency, and your operational reality.

FAQ: Snowflake vs Redshift Benchmarks for Analytics-Heavy Workloads

1) Are Snowflake vs Redshift benchmarks reliable?

They can be, but only if the benchmark setup matches your workload. Public benchmarks often differ in warehouse/cluster sizing, caching conditions, and tuning levels. The most reliable results come from testing your own query set under cost-equivalent configurations.

2) What’s the biggest benchmarking mistake teams make?

Benchmarking only a handful of “hero queries” with a single user. Many real problems show up under concurrency: queueing, unpredictable p95 latency, and workload interference between ELT jobs and BI dashboards.

3) Should I benchmark with TPC-DS or with my own queries?

Ideally both. TPC-DS is useful for standardized comparison, but your own SQL captures reality: data skew, business logic, and query patterns. If you must choose one, choose your production queries (anonymized if needed).

4) How do I make Snowflake vs Redshift benchmarking fair on cost?

Normalize by cost bands. For example, decide on a target hourly spend and configure Snowflake warehouse size (and concurrency scaling) and Redshift cluster/serverless settings so that the expected hourly cost is comparable—then compare runtime and throughput.

5) Does caching “invalidate” benchmark results?

No—caching is part of real-world performance. But you should report cold and warm results separately. Warm-cache performance is especially relevant for dashboards and scheduled reporting; cold-cache performance matters for new queries and incident recovery scenarios.

6) Which platform is better for high concurrency BI dashboards?

Often Snowflake, because you can isolate workloads using multiple warehouses and scale concurrency more directly. Redshift can also perform well, but you’ll likely rely more on WLM configuration and careful workload management.

7) Which platform is better for join-heavy analytics?

Either can be strong. Redshift may excel when distribution and sort keys are tuned to the join patterns. Snowflake often performs well without as much manual tuning, but results depend on warehouse size, clustering behavior, and data layout.

8) Should I include ETL/ELT runtime in my benchmark?

If your decision is about end-to-end analytics performance, yes. Many teams underestimate how much “warehouse performance” is really “pipeline performance.” Include ingestion + transformation timing for critical models, especially if you run frequent incremental loads or backfills.

9) How long should a meaningful benchmark take?

A practical benchmark can be done in 1–2 weeks if you limit scope to:

• Top 20–50 queries
• 2–3 concurrency levels
• 1–2 dataset scales

Longer benchmarks usually mean the scope wasn’t defined tightly enough.

10) What should I produce at the end of the benchmark to support a decision?

Deliver a simple decision pack:

• Query suite results (p50/p95 runtime, throughput, queue time)
• Cost estimates per scenario
• Configuration details and assumptions
• Operational notes (tuning effort, maintainability, monitoring needs)
• A recommendation aligned to business priorities (latency, cost, governance, team skill)