Introducing 5XL SQL Warehouses: A Practical Guide to Meeting SLAs for Your Most Demanding Workloads

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While data volumes are growing, the time windows to process them are getting stricter. Enterprise customers are generating more data than ever, and the speed is only accelerating with more agentic workflows. At the same time, the SLAs for when it needs to be refreshed leave less and less room, whether it's nightly ETL that must land before business hours or complex BI dashboards that users expect to load in seconds.

Long-running ETL pipelines and resource-intensive analytical queries are pushing against the limits of what a 4XL can deliver. One option is to rearchitect: break pipelines into smaller, parallelized steps or pre-aggregate tables for dashboards. But that adds significant engineering overhead. For many teams, that tradeoff doesn't make sense when all they need is more horsepower.

Today, we're announcing that the 5XL Databricks SQL Warehouse is now in Public Preview, giving customers access to the largest SQL warehouse available on Databricks. Compared to 4XL, it doubles the compute: more cores for parallelism, more memory to keep data in-memory, and more resources to power through the heaviest operations without rearchitecting your pipelines.

In benchmarks, analytical queries run ~1.8x faster. For terabyte-scale ETL inserts, customers have seen 2x or better. 5XL's hourly rate is roughly twice 4XL's, but faster completion means less time billed, so total cost is often comparable or lower.

In this post, we walk through how to evaluate whether 5XL is right for your workloads, set up a comparison, and monitor performance after deployment.

 

Step 1: Identify Your Best Candidates

Not every workload benefits from 5XL equally. The biggest gains come from resource-intensive, long-running operations that can take advantage of 5XL's additional parallelism and compute headroom.

Look for these patterns in your current environment:

• Long-running ETL is the strongest signal. Queries that run for tens of minutes, especially MERGE, CTAS, and INSERT operations processing terabytes of data,are prime candidates. These benefit most from 5XL's ability to spread work across more cores and hold more data in memory.
• Complex BI and analytical queries. Dashboard queries that scan large datasets, involve heavy joins or aggregations, or hit memory limits on 4XL can also see meaningful improvements. If your users are waiting on slow-loading dashboards backed by terabyte-scale tables, 5XL gives those queries more room to execute efficiently.
• High I/O workloads. If your queries are reading or writing 1TB+ of data per execution, the additional parallelism in 5XL can significantly reduce wall clock time.
• Check for SLA pressure. If your batch windows are tight and you're regularly finishing close to the deadline on 4XL, 5XL gives you a direct margin of safety.

Workloads that match these patterns are the best candidates for 5XL.

 

Step 2: Set Up a Comparison

We recommend a controlled comparison before moving production workloads to 5XL.

Tips for setting up a controlled comparison:

1. Pick a stable, repeatable workload. Ideally a nightly or daily batch ETL that processes similar data volumes each run. This minimizes noise when comparing across days.
2. Establish a baseline. Run your workload on 4XL for 2–3 days and record wall clock time, disk spill, and DBU consumption for each run.
3. Resize to 5XL and repeat. Resize the same warehouse to 5XL (this takes just a few minutes when the warehouse is stopped), then run the same workload for 2–3 days under identical conditions.
4. Control for variability. If your workloads fluctuate (e.g. data volume is growing rapidly that the input & output metrics are growing daily even for the same jobs), this can skew results. If feasible, consider creating a test 5XL warehouse and sending workloads to both warehouses for the most accurate comparison.
5. Compare the following metrics:
1. Wall clock time per query (total_duration_ms in Query History System Table)
2. End-to-end batch completion time
3. Disk spill volume (spilled_local_bytes in Query History System Table) — reduction here confirms the extra memory is being utilized
4. Total DBU consumption for the entire workload run

 

Step 3: Understand What to Expect

Based on customer results and internal performance testing:

• Queries above a minute: Expect, on average, more than 25% latency reduction compared to 4XL. For large 1TB+ ETL inserts or joins of 1TB+ tables, latency improvements of 2x or more have been observed. Disk-spill-heavy queries see the largest gains.
• Queries under a minute: For sub-minute queries, if latency is driven by queueing time rather than execution time, increasing max clusters on a 4XL is typically more effective than moving to 5XL.

5XL is purpose-built for long-running, resource-intensive operations.

 

Step 4: Understand the TCO Impact

The obvious question: does 2x the compute mean 2x the cost?

Not necessarily. 5XL has a higher hourly rate, but if your queries finish significantly faster, the total DBUs consumed can be similar or even lower. Here's the math for an example we observed:

Warehouse Size	4XL	5XL
Hourly rate	528 DBU/hr	1,042 DBU/hr
Batch duration	121 min	64 min
Total DBUs	1,074	1,111
Cost diff	~3.4%
Time saved	1.9x

For SLA-sensitive complex workloads, the math is straightforward: roughly 2x faster, at comparable total cost.

 

Step 5: Monitor and Iterate

After deploying 5XL in production:

• Track wall clock time and batch completion. This is the metric that typically matters most for ETL workloads and the clearest way to demonstrate ROI against your SLAs.
• Watch total DBU consumption, not just query times. Faster queries translate to less time billed. Track total DBUs per batch to confirm TCO stays flat or improves.
• Monitor disk spill trends. A reduction in disk spill confirms that the additional memory is being utilized effectively, which often contributes to both faster queries and lower total compute work.

Getting Started

5XL SQL Warehouses are available now in Public Preview for Serverless and Pro in all cloud regions. Create a warehouse, run your heaviest workloads through a controlled test, and measure the difference.

If your batch windows are tight and your 4XL is hitting its limits, this is built for you. Reach out to your Databricks account team with questions or to share your results. We'd love to hear what 5XL does for your workloads.