Extend Your Snowflake Governance Into Databricks

Snowflake and Databricks are often seen as competitors. And to some extent, they are – especially for smaller companies that don’t have the resources to manage both.

However, a large number of organizations are using both. They’re taking a best-of-breed approach, choosing the right tool for each workload to achieve the best performance and cost efficiency.

Managing even one of these platforms is challenging. Entire industries exist around governing Snowflake and Databricks. Managing both means more complexity and a harder time operating your governance program. It also means reduced visibility and control over your data.

Without a unified governance structure, security gaps appear, operational inefficiencies creep in, and worst of all, the risk of a compliance or data breach increases.

Many organizations that use Snowflake and Databricks together do so because they complement each other.

Snowflake is great for structured data, SQL-based analytics, and a well-governed data warehouse. Traditionally, organizations use it for financial reporting, customer analytics, and operational dashboards, where SQL-based querying and compliance are key. 

Databricks, built for big data, machine learning, and processing unstructured data at scale, is often used for real-time data processing, AI model training, and streaming analytics – workloads that require high-performance distributed computing.

An e-commerce company might store transactional sales data in Snowflake for financial reporting but use Databricks to analyze customer behavior trends using machine learning. A healthcare company may use Snowflake to store structured patient records while running Databricks to process massive genomic datasets.

By using both, teams can optimize for performance and cost. Satori’s data security platform easily allows organizations to enforce the same security policies on both Snowflake and Databricks, simplifying their operations and reducing the risk of a compliance or data breach. 

Over the past few years, both Snowflake and Databricks have expanded their offerings with many overlapping capabilities. The platforms today are much more similar than they were when they first started out; still, there are reasons why companies might use them both. Organizations that have undergone mergers and acquisitions might have inherited an environment that uses a different data platform. Another possible reason is spreading risk across multiple vendors, applying the appropriate security model to each workload.

In Satori, you can create datasets, which are collections of data store objects such as tables or schemas from one or more data stores that you want to govern access to as a single unit.

Here is an example of a dataset in Satori containing multiple data stores, including Snowflake and Databricks:

Managing security across multiple platforms is hard enough. However, the real challenge is ensuring security and governance stay intact when data is shared between them.

Databricks’ Lakehouse Federation lets users query Snowflake data from Databricks. But the connection is made using service accounts for authentication, bypassing user-specific security policies enforced on Snowflake. 

That means:

• A simple connection can completely undermine your security perimeter.
• Data governance policies applied in Snowflake don’t carry over to Databricks unless explicitly replicated in Databricks.

Without a solution, security teams have no visibility or control over how sensitive data is accessed once it leaves Snowflake.

Power BI’s Import Mode is another major governance risk. Organizations widely use it to speed up dashboards for end users. But behind the scenes, it copies data from the source database (Snowflake, Databricks, or another data platform) into Power BI Analysis Services, a columnar database used by Power BI under the hood.

The problem? It connects via a service account, just like with Lakehouse Federation.

That means:

• Any security policies enforced in Snowflake or Databricks disappear the moment data is copied.
• Once inside Power BI, there’s no visibility or control over how that data is used. 

Read more about implementing row-level security in Power BI with Satori.

Satori integrates with Snowflake, Databricks, and Power BI to enforce consistent security policies – no matter where data is accessed.

For Lakehouse Federation: Satori ensures user-specific policies remain enforced even when Databricks queries Snowflake.

For Power BI Import Mode: Satori applies security controls when Power BI queries Analysis Services, preventing unauthorized access.

Using both Snowflake and Databricks makes sense for a lot of organizations. But without proper governance, data security falls apart.

Satori provides a unified security layer across both platforms, ensuring your data stays protected no matter where it moves.

Want to see how Satori secures Snowflake, Databricks, and Power BI? Book a demo today.