In the Age of AI, An Enterprise Ready Data Foundation Starts with Business-Critical Capabilities
By: James Dinkel
1. Uptime and Reliability
Uptime and reliability is your first essential ingredient. Availability and responsiveness of your data platform is what ensures business continuity. Without it, you could be in big trouble. And when it comes to disaster recovery (DR), the difference between “managed” and “manual” becomes clear the moment something goes wrong.
There are Three Types of Disaster Recovery
- In-Region: Failover within a cloud region (across Availability Zones)
- Cross-Region: Failover across cloud regions within the same provider.
- Cross-Cloud: Failover across entirely different cloud providers.
Snowflake enables all three natively and intuitively. Databricks requires significant manual setup, code, and ongoing maintenance — often falling short of enterprise availability standards.
In practice, here’s what that means. In October 2025, during a major AWS outage, Snowflake seamlessly failed over more than 300 mission-critical applications — keeping customers operational without disruption. Databricks, on the other hand, offered no public results from that same event (not a good sign).
Why the difference? It’s not about which cloud you choose. It’s about architecture and approach.
Snowflake was built with resiliency tools baked in, not bolted on. Databricks leaves much of the heavy lifting to customers.
Let’s examine how Snowflake and Databricks handle disaster recovery to understand the differences in their approaches.
Snowflake:
- Within a region: Data automatically stays in sync across zones, so operations continue even during localized outages.
- Across regions: Snowflake can replicate everything — not just your data, but also your users, roles, and security settings. Failing over is as easy as flipping a switch (or redirecting a URL).
- Across clouds: The same process works whether you’re on AWS, Azure, or Google Cloud. Your apps can even keep using the same connection while Snowflake handles the switch in the background.
In short: Snowflake automates resilience across zones, regions, and even clouds and has already proven it works in real-world incidents.
Databricks
- Within a region: While serverless (SQL and ML-Inferencing) do support multiple availability zones – the other components do have issues. Specifically, they can recover from a single-zone failure, but it can take an estimated time of up to 15 minutes, and only one zone can be down at a time. Some parts of the data (e.g. external tables) still need to be managed manually.
- Across regions: There’s no built-in disaster recovery. Customers must manually copy workspaces, users, and governance policies, re-create configurations, and sync code. That setup can take months and may still not meet uptime targets.
- Across clouds: Things get even harder. Many of the cumbersome tools Databricks uses for automation don’t work across cloud providers and cloning or replication features don’t carry over.The bottom line: Snowflake’s disaster recovery is automatic and proven under pressure. Databricks’ approach is mostly do-it-yourself, leaving customers to build and maintain resiliency on their own.
Snowflake 
Databricks
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-- UDF Function
CREATE OR REPLACE FUNCTION mask_subquery(email STRING)
RETURNS STRING
RETURN CONCAT('xxxx@', SPLIT(email, '@')[1]);
-- Query
CREATE OR REPLACE TABLE default.masked_users_high_activity2 AS
SELECT
u.user_id,
(
SELECT mask_subquery(MAX(u2.email))
FROM users u2
WHERE
u2.user_id = u.user_id AND
(
SELECT COUNT(*)
FROM user_activity ua
WHERE ua.user_id = u2.user_id
) > 5
) AS masked_email_if_high_activity
FROM users u
WHERE EXISTS (
SELECT 1
FROM user_activity ua1
WHERE
ua1.user_id = u.user_id AND
ua1.activity_type = 'suspect page'
);
Results: As expected, on complex queries, Snowflake vastly outperforms Databricks by almost 2x (margin of 103% and 78% for masked and unmasked queries, respectively). The mask on Databricks comes with a 3-second penalty, whereas Snowflake has none.
Use Case | Time (seconds) |
Snowflake, unmasked | 12.0 |
Snowflake, masked | 12.0 |
Databricks, unmasked | 21.4 |
Databricks, masked | 24.3 |
Test 2: Simple, conditional masking
Purpose of the test: The objective is to conditionally mask the email address based on the user’s country.
User’s table with geo information
- Data Size: 409 Million
- Description: Customer-related data
- Columns: Index, CustomerId, FirstName, LastName, Company, City, Country, Phone1, Phone2, Email, SubscriptionDate, Website
Masking UDF: The objective of the masking logic is to conditionally mask the email address based on the user’s country. This is done using a rule-based approach where email addresses are masked only if the country does not begin with a letter between ‘A’ and ‘H’ (inclusive).
-- UDF
CREATE OR REPLACE FUNCTION mask_pii_country_new(email STRING, country STRING)
RETURNS STRING
RETURN CASE
WHEN UPPER(SUBSTR(country, 1, 1)) BETWEEN 'A' AND 'H' THEN email
ELSE REGEXP_REPLACE(email, '^[^@]+', 'xxxx')
END;
-- Query
CREATE OR REPLACE TABLE default.customers_dynamic_mask_temp AS
SELECT
firstname,
lastname,
country,
mask_pii_country_new(Email, country) AS masked_email
FROM default.customers_dynamic_mask
WHERE SubscriptionDate > '2021-01-01';
Results: Also, as expected, on the simpler query, Snowflake still outperforms Databricks by a margin of 12% on both the unmasked and masked queries.
Use Case | Time (seconds) |
Snowflake, unmasked | 22.7 |
Snowflake, masked | 24.3 |
Databricks, unmasked | 25.48 |
Databricks, masked | 27.49 |
In both cases, we used a small Snowflake warehouse and a small Databricks serverless cluster:
- Snowflake: small, enterprise=$6/hr., business critical=$8/hr
- Databricks: small serverless, cost=12DBU/hr = $8.40/hr.
What powers this high Snowflake performance and efficiency?
Snowflake’s platform powers simplicity across the customer journey:
- Simplicity in set-up – Snowflake is a fully managed platform and does not require elaborate set-up processes.
- Simplicity in platform scaling – Snowflake is built on a foundation of high performance and efficiency. With elastic scaling, micro-partitions, high concurrency support, automatic performance improvements, and intelligent workload optimization features, Snowflake is one of the fastest analytics platforms in the industry.
- Simplicity in performing complex analytics – Snowflake has had robust analytics capabilities for years, including support for vectorized UDFs, stored procedures & multi-table transaction support, and automatic MV refreshes.
- Simplicity in enabling strong end-to-end security and governance – Snowflake governance is foundational with Horizon catalog – out-of-the-box row filtering, dynamic data masking, tag-based masking, fine-grained access controls with no significant performance impact.
Conclusion
Snowflake has meticulously built an industry-leading analytics platform that is not only fully managed and constantly improving but also extends to meet customers’ requirements as an open lakehouse, modern warehouse, or a global data mesh while preserving its simplicity. In addition, all of this is backed by a robust engine that powers one of the fastest data analytics platforms in the market and provides cost efficiencies. While Databricks might make several claims but many of them fall apart because of the complexity Databricks passes along to the users. Don’t just take our word, try Snowflake today. Need help? Squadron Data can help you get started.
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