Databricks vs. Snowflake: Key differences explained

When you look for an analytics and data engineering solution, two main platforms come to mind: Databricks and Snowflake. Both offer strong capabilities, but they’re built on fundamentally different architectures. Understanding those differences and determining the best tool for your business is difficult, especially if you’re just starting to build a data ecosystem.

In this article, we’ll compare Databricks vs. Snowflake to help you understand how these platforms work, what they offer, and where they excel.

What is Databricks?

Databricks is a unified analytics and AI platform that is built on the lakehouse architecture. It offers a range of data engineering capabilities and open data lake storage, making it perfect for supplementing AI or big data analytics workflows. 

A defining Databricks feature is its Delta Lake, which adds ACID transactions to cloud object storage. For engineering teams that need governance and consistency in their data lakes, Delta Lake is a fundamental addition. Databricks also offers a range of collaborative notebooks for different programming languages, allowing teams to work together in a shared environment.

Due to its flexibility and lakehouse components, Databricks excels at large-scale processing across structured, semi-structured, and unstructured data. 

Key features

The three standout features of Databricks are:

• Delta Lake: Delta Lake allows ACID transactions, enforces strict schemas in lakes, and offers Delta Time Travel for cloud storage. These capabilities improve data reliability and streamline governance, preventing inconsistencies in datasets and letting you roll back to previous versions if needed.
• Lakehouse infrastructure: Lakehouse architecture combines the low-cost, scalable storage of a data lake with the structured data management of a data warehouse. This allows teams to run analytics, support BI queries, and feed machine learning workloads without needing two different systems.
• Unified analytics: Databricks centralizes engineering, data science, business intelligence, and ML into one platform, removing data silos and boosting collaboration.

Use cases

Databricks prioritizes large-scale processing and engineering, which makes it useful for:

• AI model training: Supports distributed model training, experiment tracking, and data ingestion at scale for effective AI development.
• Complex pipelines: Allows construction of complex transformation pipelines for structured and semi-structured datasets.
• Real-time analytics: Feeds data into dashboards and other business intelligence (BI) tools, enabling real-time analytics.

Advantages

Here are the main advantages of Databricks:

• Scalability: Lake structure and distributed Apache Spark processing engine let you scale data processing and transformation dynamically. 
• AI-first design: Built-in AI capabilities, like vector search and MLflow, support complex AI use cases.

Disadvantages

Here are the main disadvantages of Databricks:

• Learning curve: The diverse range of features is difficult to master, especially compared to most alternatives to Databricks.
• Platform dependency: Databricks competitors point out that many workflows within the platform are tightly bound within the Databricks ecosystem and Apache Spark or Scala. If you want to migrate to another platform, this may cause trouble down the line.
• Complexity: Engineers must carefully plan workloads and manage clusters to optimize platform efficiency.

What is Snowflake?

Snowflake is a cloud-native data warehouse that makes it easy to deliver high-performance SQL analytics. It uses AI natively to simplify transformation and analytics, letting teams query datasets with natural language. On the technical side, Snowflake separates storage and compute, allowing independent scaling and more predictable billing.

Automatic infrastructure management and performance optimization within the platform help get high-quality BI insights without the upfront manual work.

Key features

Snowflake excels in a number of modern analytics workflows due to the following features:

• Fully managed, easy-to-use platform: Snowflake handles infrastructure provisioning, scaling, and performance optimization automatically, giving you a high-performance environment without the technical setup or ongoing maintenance.
• AI-powered analytics with Cortex: Snowflake Cortex AI lets you query data in natural language and run tasks like classification or summarization directly within the platform.
• High-performance BI: Snowflake excels at rapid querying across large datasets, making it ideal for powering dashboards, reports, and broader BI workflows.

Use cases

Snowflake focuses on delivering high-quality analytics and supporting BI reporting workflows. Some of its main use cases are:

• AI analytics: With Cortex and built-in LLM capabilities, Snowflake enables natural language querying and automated insights directly on your data.
• BI insights: Directly connects to BI dashboards, providing SQL-based reporting for teams.
• Data collaboration: Allows data sharing and duplication across departments to boost agility and reduce delays in getting data to the right people.
• Data warehousing: Provides a centralized hub for semi-structured and structured data, breaking down data silos.

Advantages

Here are the main benefits of Snowflake:

• Simple to use: Snowflake is an SQL-first platform with some AI-native capabilities that make querying straightforward in both SQL and natural language.
• Fully managed: The vast majority of Snowflake is fully managed, meaning Snowflake handles infrastructure maintenance, scaling, and performance optimization. Shifting these tasks to a third party reduces the operational burden on teams.
• Strong scaling: Snowflake can process workloads concurrently, maintaining stable performance even across large data jobs.

Disadvantages

While Snowflake is a scalable solution, it comes with a few notable drawbacks:

• Inflexible architecture: Snowflake competitors point out its infrastructural rigidity, which reduces customizability.
• Consumption-based pricing: Without careful governance-based controls, poor workflows lead to unexpectedly high Snowflake bills. Alternatives to Snowflake, like Databricks, may not expose teams to the same costs due to tighter control over compute resources.
• Limited ML tooling: Though it offers AI features, Snowflake isn’t ideal for training ML models due to its reliance on external frameworks for model development. 

Databricks versus Snowflake: Key differences

The difference between Snowflake and Databricks goes far beyond just architecture. Here are the main factors that set them apart.

Performance

Snowflake is optimized for SQL queries and BI workloads, which makes it ideal for analytics and reporting. Databricks, on the other hand, focuses on large-scale transformations, complex data engineering workflows, and high-performance ML training.

Scalability

Databricks allows you to scale clusters independently, helping add power as needed and maintain performance even within larger pipelines. Snowflake also supports independent scaling, but in the form of separate storage and compute layers. It lets you increase compute power without paying for extra storage and scale storage without affecting compute costs, giving full flexibility and control over both performance and spend.

Ease of use

Teams that know how to use SQL find Snowflake extremely easy to use. Databricks offers more features, but many of them require deeper understanding of data engineering for optimization and use.

Similarities between Snowflake and Databricks

Although Snowflake and Databricks serve different purposes, they share these characteristics:

• Cloud-based: Both platforms are fully cloud-native, letting you implement them into existing cloud environments like AWS, Google Cloud, and Azure.
• Querying language support: Both platforms support SQL analytics, though Databricks additionally supports R and Scala.
• Data lake capabilities: Both platforms can function as a data lake, with each able to handle semi-structured data formats.
• BI support: Both tools integrate with major BI tools and offer native capabilities you can use to build dashboards and reports.
• Marketplaces: Both platforms offer data marketplaces to discover and access third-party datasets, applications, and services.
• PostgreSQL compatibility: Both platforms have PostgreSQL-compatible interfaces, letting you connect existing tools and workflows with minimal infrastructural changes. 

Choosing Databricks or Snowflake

Here are a few considerations to determine which one is more appropriate for your business:

• Team skill sets: If the team relies heavily on SQL, then Snowflake is better. Databricks is more suitable if you have experience with more engineering-heavy workloads.
• Workload types: BI and analytics are better on Snowflake, while ML-first or large-scale data processing is better on Databricks.
• Snowflake vs. Databricks cost: Databricks offers more options for customization, letting you fine-tune workloads and optimize for cost savings. Snowflake’s rigidity makes it slightly more expensive in the long run.
• Data lake vs. warehouse strategies: If lakehouses are a priority, then Databricks makes more sense. For a warehouse-first system, Snowflake is a better choice.
• Tooling ecosystem: How well existing tools integrate with each platform impacts implementation.
• Long-term scalability: Snowflake scales SQL analytics more effectively, while Databricks is better at scaling entire data pipelines and AI workflows.
• Need for ML or AI workloads: If you need to run ML workflows or train AI models, Databricks is the better option.

How Fivetran supports Databricks or Snowflake and simplifies data pipelines

Regardless of the platform you select, Fivetran simplifies data movement and management every step of the way. 

‍Fivetran automates data ingestion into Snowflake or Databricks, provides over 700 pre-built connectors, and fully manages schema drift to maintain high-quality data across your ecosystem.

Reduce engineering burden with Fivetran’s fully automated pipelines. Get started today by requesting a demo.

FAQ

Who are Snowflake’s main competitors?

Snowflake’s main data platform competitors are Google BigQuery, Amazon Redshift, and Databricks.

How do Databricks and Snowflake compare?

Databricks focuses on delivering a lakehouse architecture that supports large-scale data engineering and AI workloads. Snowflake offers cloud-native data warehousing for SQL analytics.

Can I use Databricks and Snowflake together? 

Many companies use both Databricks and Snowflake, the former for data processing or ML workflows, and the latter for analytics and BI.

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