Snowflake vs Azure Benchmarks¶

Status: Authored 2026-04-30 Audience: Data architects, platform engineers, performance engineers evaluating migration impact Disclaimer: Benchmarks are illustrative and based on typical federal workloads. Your results will vary based on data volume, query complexity, warehouse sizing, and network topology. Always run your own benchmarks on your own data.

1. Benchmark methodology¶

Test environment¶

Test categories¶

1. Point queries -- single-row lookups by primary key
2. Scan queries -- full-table or large-partition scans with aggregation
3. Join queries -- multi-table joins (star schema, snowflake schema)
4. Complex analytics -- window functions, CTEs, nested subqueries
5. Concurrent users -- throughput under increasing concurrency
6. Warehouse startup -- cold start to first query result
7. Streaming ingestion -- end-to-end latency for event ingestion
8. AI inference -- LLM function call latency

2. Query performance benchmarks¶

Point queries (single-row lookup)¶

Analysis: Databricks wins on point queries due to Delta Lake file pruning and Photon engine optimizations. Fabric's T-SQL layer adds overhead for simple lookups.

Scan queries (aggregation over large partitions)¶

-- Test query: aggregate 100M rows
SELECT
    d_year, d_quarter,
    SUM(ss_sales_price) AS total_sales,
    COUNT(DISTINCT ss_customer_sk) AS unique_customers
FROM store_sales
JOIN date_dim ON ss_sold_date_sk = d_date_sk
WHERE d_year BETWEEN 2022 AND 2025
GROUP BY d_year, d_quarter
ORDER BY d_year, d_quarter;

Analysis: Databricks scans less data due to Delta Lake Z-ORDER on date columns. Photon engine accelerates the aggregation. Snowflake's micro-partition pruning is effective but scans more data when partitions are not perfectly aligned with the query filter.

Join queries (star schema, 5-table join)¶

-- Test query: star schema join
SELECT
    i_category, i_brand,
    s_state, s_city,
    d_year, d_moy,
    SUM(ss_sales_price) AS total_sales,
    SUM(ss_quantity) AS total_quantity
FROM store_sales
JOIN item ON ss_item_sk = i_item_sk
JOIN store ON ss_store_sk = s_store_sk
JOIN date_dim ON ss_sold_date_sk = d_date_sk
JOIN customer ON ss_customer_sk = c_customer_sk
WHERE d_year = 2024
  AND s_state = 'VA'
GROUP BY i_category, i_brand, s_state, s_city, d_year, d_moy
ORDER BY total_sales DESC
LIMIT 100;

Analysis: Databricks auto-broadcasts smaller dimension tables, reducing shuffle. Snowflake relies on hash joins uniformly.

Complex analytics (window functions)¶

-- Test query: running totals with window functions
SELECT
    customer_id,
    order_date,
    amount,
    SUM(amount) OVER (PARTITION BY customer_id ORDER BY order_date
                       ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS running_total,
    ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY amount DESC) AS rank_by_amount,
    LAG(amount, 1) OVER (PARTITION BY customer_id ORDER BY order_date) AS prev_amount,
    amount - LAG(amount, 1) OVER (PARTITION BY customer_id ORDER BY order_date) AS delta
FROM orders
WHERE order_date >= '2024-01-01'
QUALIFY ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date DESC) <= 10;

Analysis: Window functions are comparable across platforms. Databricks edges ahead on larger partitions due to Photon's columnar processing.

3. Warehouse scaling benchmarks¶

Vertical scaling (warehouse size impact)¶

Scaling efficiency: Both platforms scale approximately linearly with size. Databricks shows slightly better scaling efficiency at larger sizes due to Photon optimizations.

Horizontal scaling (concurrency impact)¶

Tested with the same 10-query workload at increasing concurrency:

Analysis: Databricks auto-scaling handles concurrency more gracefully because it scales per-node rather than cloning entire warehouses. Snowflake multi-cluster warehouses handle spikes but with higher latency during cluster spin-up.

4. Warehouse startup benchmarks¶

Analysis: Snowflake's warm start is fast. Databricks classic warehouses are slower to start but serverless warehouses match or beat Snowflake's cold start time. For interactive workloads, serverless is the recommended Databricks option.

5. Streaming ingestion benchmarks¶

End-to-end latency (event to queryable)¶

Analysis: For near-real-time requirements, Azure Data Explorer (ADX) significantly outperforms both Snowpipe Streaming and Autoloader. Autoloader is better suited for micro-batch (seconds-to-minutes latency) rather than true streaming.

Streaming cost comparison (100K events/sec sustained)¶

6. AI capability benchmarks¶

LLM inference latency¶

LLM quality comparison¶

Tested on federal document summarization task (500 documents):

Search benchmark (RAG pipeline)¶

7. Data sharing benchmarks¶

Share access latency¶

Share setup complexity¶

8. Cost-performance ratio¶

Cost per TPC-DS query (normalized)¶

Methodology: Cost-performance score = (Snowflake cost _ Snowflake time) / (Platform cost _ Platform time). Higher is better.

Sustained workload cost (8 hours/day, 22 days/month)¶

9. Benchmark caveats¶

What these benchmarks do not capture¶

1. Your specific data distribution -- TPC-DS is a standard benchmark; your data may have different characteristics
2. Your specific query patterns -- a Snowflake-optimized workload may perform differently than a generic benchmark
3. Network latency -- benchmarks were run in-region; cross-region access patterns will differ
4. Warm cache effects -- production workloads benefit from caching more than cold-start benchmarks show
5. Reserved capacity pricing -- 25-40% discounts on Databricks/Fabric are not reflected in hourly rates
6. Snowflake credit commits -- negotiated rates may differ from list pricing

How to run your own benchmarks¶

1. Export top 50 queries from Snowflake query_history (by frequency and duration)
2. Deploy a Databricks SQL Warehouse (one size smaller than your Snowflake warehouse)
3. Migrate the test queries (see dbt tutorial for SQL translation)
4. Load a representative data sample (1-10% of production)
5. Run each query 5 times; discard first run (cold cache); average remaining 4
6. Compare latency, data scanned, and cost per query
7. Scale to production-size data for final validation

Related documents¶

• TCO Analysis -- cost comparison with 5-year projections
• Warehouse Migration -- sizing and optimization guidance
• Why Azure over Snowflake -- strategic comparison
• Feature Mapping -- complete feature comparison
• Master playbook -- Section 7 for original cost comparison

Last updated: 2026-04-30 Maintainers: CSA-in-a-Box core team