The Databricks vs Snowflake Wars: Comparing 7 Critical Capabilities

A discussion on Databricks vs Snowflake highlights two of the leading vendors in this rapidly evolving cloud data platform landscape. This article aims to provide an in-depth comparison of Databricks and Snowflake by comparing their origins and capabilities.

Whether you’re a data engineer, business analyst, data scientist, or technology decision-maker – understanding the nuances between the two platforms is crucial in order to maximize the potential of your business’s data strategy.

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Understanding Databricks and Snowflake

To get started, let’s address the most basic questions “What is Databricks?” and “What is Snowflake?“

Databricks offers advanced data engineering and data science workflows while also being the industry leading data lakehouse. It can process data up to 12X faster than competitors, handle advanced machine learning and generative AI models, and combines the data warehouse / data lake, data pipelines, data catalogs all into a single platform while continuing to enable advanced governance capabilities.

Snowflake is a cloud-based data warehousing platform that enables the storage, processing, and exploration of data. Snowflake is a data warehouse that is primarily focused on business intelligence, primarily offering the capability of store and query data at scale, though more recently they have begun to offer data science on the cloud, by far the more difficult market to break into.

What is the difference between Snowflake and Databricks?

While Snowflake and Databricks have a lot of overlap when it comes to storing and querying data at scale, there are some critical differences that are worth understanding.

Unlike competitors, the Databricks architecture started with data engineering and data science at the core – incorporating industry leading capabilities such as the Apache Spark framework for big data workloads, MLflow for end-to-end machine learning lifecycles, and Time Travel for model reproducibility.

With these capabilities already established, Databricks has successfully expanded cloud data warehousing, including its “lakehouse” approach, leveraging its storage framework based on the open source Delta Lake project. This enables Databricks to seamlessly interoperate across many disparate data processing engines while avoiding vendor lock-in.

Snowflake’s founders, on the other hand, aimed to harness the power of the cloud to create a centralized data warehouse storage solution for businesses. Their goal was to enable storing and accessing data at scale in a cloud-native environment. They have since expanded their offerings to incorporate tooling for data engineering and data science, largely through third party integrations, and historically lacking the depth offered by Databricks- though that is changing over time.

Databricks vs Snowflake: Capabilities Comparison

Consider the below chart when trying to better understand the capabilities of Databricks vs. Snowflake:

Databricks vs Snowflake: How are they Competitors?

When discussing modern data platforms, Databricks vs Snowflake often comes up in conversation. Though they appear similar due to the ability to store and access data, understanding whether they are direct competitors requires delving into their core functionalities and target use cases.

Databricks – The Unified Analytics Platform

Databricks positions itself as a unified data and analytics platform, built on top of Apache Spark. It’s designed for massive-scale data engineering and collaborative data science in the cloud data lake. Databricks excels in machine learning workflows and real-time analytics thanks to its ability to handle streaming data efficiently. It provides a collaborative environment for data scientists, engineers, and business analysts to work together on complex data tasks.

However, Databricks has been slowly and successfully evolving their business to where now they are directly competing with Snowflake. It just so happens that moving from AI / Analytics and data pipeline workloads as their speciality to offering cloud data warehousing is a lot easier than trying to move in the other direction, which is what Snowflake has been trying to do.

With Databricks significant investment in the open source Delta Lake project- and as its largest contributor- they successfully tackled the significant challenge of seamless interoperability across various data processing engines, eliminating the issue of vendor lock-in. Then in mid- 2024 they also acquired Tabular, a commercial implementation of a competing open source storage framework called Iceberg. Like Databricks with Delta Lake, Tabular is the creator of, and largest contributor to, Iceberg. This only solidifies Databricks’ position in the cloud data warehouse space, making it tougher on Snowflake who is using Iceberg as the foundation of the recently announced Polaris Catalog, which they promptly released as an open source project, in a very obvious move to combat Databricks’ influence.

Snowflake – The Cloud Data warehouse

Snowflake is a cloud-based data warehousing solution, with additional capabilities being added all the time. Its strengths lie in its ability to store and retrieve vast amounts of data quickly and efficiently, making it ideal for business intelligence and reporting, which is typically accomplished through partnerships with the likes of Sigma Computing and Salesforce Tableau.

As mentioned earlier, as Databricks has begun to take more and more cloud data warehouse market share from Snowflake, Snowflake has conversely been moving more into the AI / Analytics and data pipeline space by quickly acquiring companies such as Neeva, Streamlit and Applica in the last few years, to try to catch up to Databricks. Technically speaking, this is a much more difficult hill to climb for Snowflake than it is for Databricks to move into the cloud data warehouse space.

Snowflake is also attempting to shore up their cloud data warehouse position by creating the Polaris Catalog, a direct open source alternative to Delta Lake and Iceberg. Though Polaris is built on Iceberg, Snowflake released it as a separate open source project now.

Overlap & Distinction

There is now a lot of overlap in capabilities, particularly in the area of big data processing and storage. Databricks has been more focused on advanced analytics and handling complex data processing tasks, often involving data science or machine learning. Databricks is pursuing the standard cloud data warehouse agenda with customers more and more, but they come from the data science engineering heritage. Snowflake, conversely, is optimized for storing and analyzing structured data, with a strong focus on ease of use and scalability in data warehousing.

How can you choose between Snowflake and Databricks?

Choosing between Databricks and Snowflake requires a deep understanding of both the current and future needs of the business. In some scenarios, Databricks’ wider range of capabilities gives them the upper hand. In other scenarios, Snowflake’s ease of use can be a deciding factor.

In some organizations, it may even make sense to use both in complementary roles – with data science and data engineering teams leveraging Databricks alongside analytics and business intelligence teams working in Snowflake to democratize data across the business. However, with the databricks snowflake wars as they are now being called, it will not likely be that way much longer.

Conclusion

In conclusion, both Databricks and Snowflake are powerful platforms within many companies’ data stacks. Choosing between the two, or even commingling each system’s strengths, can be a daunting task, even moreso now with the companies competing head-to-head on most agendas in the “snowflake databricks wars”.

If you’re looking for support in making a decision regarding snowflake vs databricks, or need help implementing or optimizing them within your organization, contact us at Graphable. With our deep expertise across applications, data analytics consulting, data engineering, and artificial intelligence – we’re happy to help your team along their data strategy journey.

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