Snowflake vs. Databricks: who’s better for your company’s AI initiatives? Competition between the two has been heating up for years, and their recent announcements at the 2024 Snowflake Summit and Databricks’ Data+AI conference reflect the intensifying shift towards enabling AI capabilities for their users.
It's becoming increasingly difficult to compare Snowflake and Databricks as their offerings continue to converge. I’ll break down six key differences between them, with cost being the most important to consider.
I’ll give my verdict on which platform is best for your AI strategy and explain how our team at StepChange can help accelerate that process–because speed of execution is a number one priority for many data teams today.
Databricks vs. Snowflake: Platform differences
The biggest difference between them comes down to their core architecture and primary use cases.
Snowflake is a data warehouse designed for structured data and is optimized for SQL-based analytics and reporting.
Databricks operates on a data lakehouse architecture, which combines elements of data warehouses and data lakes. All this means is that it can handle both structured and unstructured data (such as text, images, and logs) in a more unified manner. This makes it an ideal choice for AI workloads, which often involve diverse and complex data sources.
Of course, there are lots of nuanced differences so a strict binary of “data warehouse vs. data lakehouse” greatly oversimplifies things.
Both platforms have been rapidly evolving, with Snowflake increasingly supporting unstructured data and machine learning workflows, while Databricks is enhancing its SQL capabilities and performance for structured data analytics.
Their 2024 summit announcements have indicated that this convergence of features will only intensify, making it harder to figure out which platform aligns with what you want to do with your data and how you can execute it quickly.
Let’s now look at the five most important areas where these platforms still differentiate themselves.
Data processing
Databricks is highly versatile, handling all data types—unstructured, semi-structured, and structured—in their original formats, making it ideal for processing large volumes of raw data like visuals and documents. It’s built on Apache Spark's distributed framework, excels in data lake architecture, and is particularly strong in streaming, machine learning, and real-time analytics.
Snowflake, optimized for semi-structured and structured data, transforms semi-structured data into a structured format for processing and is best suited for data warehousing and analytics workloads, and traditionally has had limited support for unstructured data (typically stored externally in AWS S3 etc.). However, this is rapidly changing with the launch of Iceberg Tables, which combines the benefits of a Snowflake table with the flexibility of using open data formats and your own cloud storage.
Data storage management
Snowflake acts as a data warehouse solution and handles data storage natively, optimizing storage and compute separately. This greatly simplifies data management as it takes care of hardware, configuration, and software updates automatically.
Databricks, however, typically relies on an external storage solution (like AWS S3, Azure Blob Storage) and primarily focuses on compute and analytics. This allows for flexibility in using existing data lakes and different cloud providers.
Scalability & optimization
Both Databricks and Snowflake are designed to handle large-scale data processing, but they approach scalability differently based on their architectures.
Snowflake is designed for instant elasticity, allowing it to scale storage and compute resources independently. It scales primarily through its multi-cluster warehouse feature, which allows the system to automatically add or remove clusters to manage workloads efficiently. This makes it particularly effective for environments that require high concurrency, such as cloud data warehousing and analytical workloads like aggregated reports and dashboards.
On the other hand, Databricks leverages Apache Spark’s cluster management to scale compute resources dynamically. Users can manually adjust the number of nodes in a cluster depending on the workload, which is especially useful for tasks like heavy data transformation, ingestion, and machine learning.
Most users know that Snowflake’s scalability is more automated and user-friendly, while Databricks offers greater flexibility and control, which can be powerful but may require more manual tuning.
However, Databricks has been working to simplify the process of running Spark jobs with serverless compute, which eliminates the need to manage and configure Spark clusters manually. When you submit a Spark job, the necessary compute resources are automatically provisioned, and the job is executed without any intervention needed from you. This "serverless" approach abstracts away the complexity of managing infrastructure, letting you focus on the data processing and analytics tasks themselves.
ML/AI capabilities
While the gap is narrowing between both companies, Databricks has built-in support for various ML frameworks, a machine learning runtime, and Managed MLflow for managing the ML lifecycle. This makes it particularly suited for advanced analytics projects that require heavy data processing and machine learning.
Snowflake has been catching up through major partnerships and integrations such as Snowpark, which allows users to execute machine learning workloads directly within Snowflake, and Snowflake AI & ML Studio, which provides no-code interfaces and support for working with LLMs. The latter is particularly interesting as it allows even non-technical users to build AI models even without having deep technical expertise.
Integrations
The types and breadth of integrations are not a major differentiator between Snowflake and Databricks. Both platforms offer extensive integration options with popular data sources, ETL tools, and business intelligence platforms.
Snowflake is generally stronger in integrations that support traditional data warehousing, BI, and structured data analytics, with a growing focus on AI/ML and unstructured data. Databricks excels in integrations related to big data, AI/ML, and data science, offering more flexibility for handling complex, large-scale data processing tasks, especially in real-time or with unstructured data.
Snowflake is increasingly adding support for unstructured data and machine learning workflows, but its integrations in these areas are still developing compared to its traditional strengths.
Ease of use
Snowflake is designed to be easier for users, with a more opinionated setup that reduces the complexity typically associated with data warehousing solutions. It’s particularly user-friendly for those familiar with SQL. This makes it accessible to a broader range of users, including data analysts, business users, and less technical team members, which is an important consideration as AI model development often requires cross-functional engagement.
Databricks offers powerful capabilities but requires more knowledge to configure and optimize, especially when dealing with Spark-based data processing workflows. This can make it less accessible to non-technical users or teams without skilled data engineers. The complexity can be a barrier to entry for some teams, potentially requiring more time and resources to onboard effectively.
Databricks vs. Snowflake: Which costs more?
Their pricing models are quite different. Snowflake charges based on the amount of data stored and compute time used, which can be paused or scaled independently. Databricks’ pricing is based on the cost of cloud resources consumed (DBUs), which can vary based on the type of workloads and the configurations of the cloud services used.
But the ongoing and unresolved debate between platform differences or pros and cons between Databricks and Snowflake is a red herring since many companies use both. One reason why is that it’s a strategic decision to avoid vendor lock-in, as well as leveraging the unique strengths of each platform. Having alternatives can provide leverage in vendor negotiations, potentially leading to better pricing or service terms. (You can more credibly shift to another platform if you’re already using it.)
Hence, the real difference that matters is cost. And the cost differences between Snowflake and Databricks can be complex and they depend on how you choose to manage specific workloads and configurations.
They also have significantly different pricing models, which complicates any direct cost comparison between Snowflake and Databricks. Snowflake offers a pricing structure that separates compute and storage costs, allowing users to scale and pay for each independently. This can lead to cost savings when you need to store large amounts of data without constantly querying it, as you can scale down compute resources when they're not needed.
On the other hand, Databricks charges for the compute resources consumed, using Databricks Units (DBUs) which are a measure of processing capability per hour, influenced by the type and size of the compute instance. This model might result in higher costs during intensive processing tasks but could be more cost-effective for scenarios requiring heavy data processing over shorter periods.
Databricks may be able to run certain workloads cheaper than Snowflake, as they offer more granular control and tuning options for compute resources. This includes features like Spot Instances that can provide significant cost savings.
The decision between these two platforms should therefore consider not only the raw numbers associated with their costs but also how each platform’s pricing strategy aligns with your specific workload patterns, data processing needs, and budget constraints.
The better platform for AI strategy
Here’s a hypothetical question: if you got to start your company’s data infrastructure over tomorrow, which would you go with?
How you answered that probably cuts through the endless debates over which platform is better or worth the cost of migration. It’s a question dependent on your specific use cases, the technical expertise of your team, and your long-term goals.
Here’s what we think: If your company is serious about supporting AI initiatives and products, we unequivocally recommend Databricks as the better choice over Snowflake.
We’re not alone in thinking this way. But the answer isn’t that obvious as both companies have tightened their marketing to reflect a heavier AI focus. If you take a look at their respective home pages, Snowflake’s use of “AI Data Cloud” is difficult to distinguish from Databricks’ “Data Intelligence Platform”.
So why should you consider a migration to Databricks? In our experience, many of our clients have in fact transitioned to Databricks to add AI capabilities to their business for some or all these reasons:
- A stronger focus and more mature offerings for machine learning, including tools like MLflow for managing the ML lifecycle
- A more native and integrated experience for data scientists and ML engineers, with features like notebooks, libraries, and model serving capabilities This makes it ideal for developing and scaling AI solutions.
- It’s built on top of open-source technologies like Spark, which are widely used in the AI/ML community. Databricks is better aligned with the tools and practices in the AI/ML community, and ensures you’re at the cutting edge of what’s possible in AI.
- Snowflake's capabilities for handling different tiers of data are not as flexible or cost-effective compared to other platforms designed for complex data processing like Databricks. It’s just a cheaper option in the long run and enough to justify a migration today.
The fourth reason is often big enough on its own to justify a migration. Why? The initial switch to Databricks might require an investment in the migration and training, but the long-term savings from more efficient data processing and the ability to leverage scalable resources will likely offset these costs. This is particularly relevant for companies where data processing is a core aspect of their operations. Reducing runtime and using resources more efficiently translate directly into cost savings.
If any of these resonate, or if you’re curious about whether Databricks might be a fit for your business, stay tuned for our next article on what it takes to migrate from Snowflake to Databricks.
We’ll also discuss why your migration plan should take into account the opportunity cost of different migration strategies. Opportunity costs are often overlooked in deciding whether and how to migrate–but you need to evaluate these costs against the anticipated benefits of a new platform with enhanced AI capabilities.
Migrating from Snowflake to Databricks to unlock AI innovation
If you're noticing that your expenses on Snowflake are escalating, or if you're concerned that its limitations in performance and scalability might be hindering your AI initiatives, it’s the right moment to evaluate other options.
Snowflake, while robust for many use cases, may not always meet the demanding requirements of advanced AI applications that require extensive data processing and real-time analytics capabilities.
Choosing the wrong platform can be a limiting factor in your company’s AI strategy. A misaligned platform choice can not only delay progress but can also lead to increased costs and missed opportunities in leveraging AI for competitive advantage.
We can help you first assess whether a transition to Databricks is the right move for your organization. Our team is experienced with complex migrations and can manage the entire process on time and on budget.
Book a call with us to learn more about how we can help your company shift into high gear with your AI initiatives.
