Databricks vs Snowflake: choose the right data warehouse for you

Snowflake and Databricks are both strong data warehouses.

As the importance and complexity of data continues to grow, data analysts and data scientists need to use the right tools to get useful insights. In this in-depth comparison, we will look at Databricks and Snowflake, two of the most famous data platforms. We’ll look at their features, advantages, and disadvantages so you can choose the right tool for your needs.

Databricks wants to combine data warehouses and data lakes into a single platform, while Snowflake is marketing a data warehouse with a software-as-a-service (SaaS) offering that requires less maintenance and can grow. In this article, we will start by talking about what data warehouses and data lakes are and how they work. Then, we’ll talk about the main differences between Databricks and Snowflake.


Databricks and Snowflake are two of the best data warehouse systems that run in the cloud. Both have a range of pricing choices, but there are some major differences between the two. Prices for Databricks are based on a “pay as you go” approach, with compute resources starting at $0.03 per second and storage at $0.005 per gigabyte.

Snowflake’s pricing is also based on a pay-as-you-go model, but prices start at $0.005 per second for compute tools and $0.00001 per gigabyte for storage. One big difference between the two systems is that Databricks’ pricing includes the cost of data transfer, while Snowflake’s pricing does not. This means that Databricks can be more expensive for jobs that need to move a lot of data.

Databricks vs Snowflake Comparison Table

The Databricks vs. Snowflake comparison table shows how these two systems are different in important ways. Databricks is great at integrating data engineering and AI, which makes it easier to work together. Snowflake stands out because of how well it stores data in the cloud and how easy it is to use. It also has powerful analytics solutions. Each platform meets different data needs, so the choice depends on the wants of the user.

PurposeUnified analytics platform for big data and AICloud data warehousing and analytics platform
Use CasesData engineering, data science, machine learningData warehousing, data analytics, data sharing
Data ProcessingOffers unified data processing (ETL, ML, SQL)Focuses on SQL-based data processing
ArchitectureUnified platform for data engineering and analyticsSeparate platform for data storage and analytics
Data StorageIntegrates with various data sources and formatsFocuses on structured data in a data warehouse
PerformanceOptimized for processing large-scale dataDesigned for high-performance querying
ScalabilityAuto-scaling and elasticity for varying workloadsElastic scalability based on usage needs
Advanced AnalyticsIntegrated support for machine learning and AIPrimarily focuses on data analytics
IntegrationIntegrates with various data sources and BI toolsIntegrates with various BI and ETL tools
CostUsage-based pricing modelPay-as-you-go pricing based on usage
ManagementCentralized platform for collaborative data workFocuses on data warehousing management
Data SharingAllows collaborative data sharing and explorationSupports data sharing between organizations
SecurityOffers role-based access and encryption featuresProvides strong security and compliance features
Ecosystem IntegrationIntegrates with Apache Spark and various ML librariesWorks with various data integration tools
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What is Databricks?

Databricks is known for its unified data analytics platform, which mixes data engineering, data science, and business analytics. The most important parts of Databricks are:

  • Unified Data Analytics tool: Databricks gives data engineers, data scientists, and analysts a single tool that makes it easy for them to work together on data projects. This integrated method breaks down data silos and helps people from different departments work together.
  • Collaborative workplace: Teams can share notes, queries, and visualizations in Databricks’ collaborative workplace. This makes it easier for people to work together and speeds up the development of solutions based on data.
  • Apache Spark Integration: Apache Spark is an open-source analytics tool that works well with Databricks. This integration makes it easy for users to work with big datasets and change them in ways that aren’t simple.
  • skills for machine learning: Databricks has advanced AI and machine learning skills. It lets people use famous frameworks like TensorFlow and PyTorch to build, train, and run machine learning models.

What is Snowflake?

Snowflake focuses on data warehousing and analytics that work well in the cloud. Its framework is made so that it can handle huge amounts of data and scale on demand. Snowflake’s most important parts are:

  • Cloud-Native Data Warehousing: Snowflake’s data warehousing system is built from the ground up for the cloud, giving it the benefits of flexibility and scalability. Users can easily go up or down in size based on what they need.
  • Sharing and collaborating on data: Snowflake lets companies share data with partners, customers, and other parties in a safe way. This tool makes it easier to work together and share information.
  • Unique Architecture: Snowflake’s architecture is unique in that it separates compute and storage, so users can grow each one separately. This architecture makes the most of speed and resources.
  • Scalability and Elasticity: Snowflake’s auto-scaling features make sure that speed stays the same even when there are a lot of requests at once. This makes sure that resources are used well and costs are managed well.

Databricks vs Snowflake: Performance and Scalability

Databricks vs Snowflake

Both Databricks and Snowflake do a good job in their own areas of speed and scalability. Databricks uses its optimized data processing engine to make insights and computations happen quickly. It works well with big amounts of data, which makes it a popular choice for tasks that involve a lot of data.

On the other hand, Snowflake is known for its elastic scaling design, which makes it easy to add more resources as workloads grow. Its innovative multi-cluster design makes sure that queries work well even as the amount of data grows. Both systems put an emphasis on handling data efficiently and have great scalability, making them useful for businesses with different data needs and growth plans.

Data Processing and Analytics

When comparing Databricks and Snowflake, it’s important to look at how they process and analyze data. Databricks is a platform that combines data processing with advanced analytics. It does this by using tools like Apache Spark to make data manipulation and machine learning jobs go more quickly.

Snowflake, on the other hand, focuses on data warehousing and analytics. It offers a cloud-based tool for storing and querying data using a specialized architecture. Databricks is great at providing a unified setting for processing and analyzing data, but Snowflake’s strength is in its scalable data warehousing, which makes it easy to query and analyze data. Choosing between the two relies on the data tasks. Databricks is good for integrated analytics and machine learning, while Snowflake is good for robust, scalable data warehousing and analytics.

Data Warehousing

Databricks and Snowflake take different approaches to data storage. Databricks makes it easy for users to analyze and keep data by integrating data warehousing features into its platform. Snowflake, on the other hand, specializes in cloud-based data warehousing and offers a separate service that can be scaled to store and handle data.

Databricks offers a unified setting for analytics and data processing, but Snowflake is better at data warehousing because it offers a dedicated solution. Whether your company chooses Databricks or Snowflake depends on what it wants to focus on: comprehensive data analytics on a single platform (Databricks) or strong, scalable data warehousing (Snowflake).

Databricks vs Snowflake: Integration and Compatibility

Databricks vs Snowflake

Both Databricks and Snowflake have many ways to connect their services. Databricks works well for businesses with complex environments because it works well with many different data sources and analytics tools. As a cloud-based platform for data warehousing, Snowflake works with famous databases, data lakes, and business intelligence tools.

Due to its focus on analytics, Databricks focuses on a wider range of integrations, while Snowflake’s support helps with smooth data management. Your choice should be based on how you want to integrate—Databricks is good for unified analytics, while Snowflake is good for storing and managing all kinds of data.

Databricks vs Snowflake: Security and Compliance

Security and safety are important to both Databricks and Snowflake. Databricks uses advanced security features like role-based access limits and full data encryption. As a cloud data platform, Snowflake puts a lot of focus on security from beginning to end. It does this by providing data encryption, identity management, and compliance certifications.

Both systems protect data and follow regulations, but Snowflake’s focus on the cloud might give it an edge in terms of scalability and making security management easier. Think about the security needs of your company and if the cloud-based approach of Snowflake fits better with those needs.

Databricks vs Snowflake: Ease of Use and User Interface

Both Databricks and Snowflake are easy to use, but they do so in different ways. Databricks gives data scientists and analysts a unified analytics tool with features for working together. Even though Snowflake’s interface is easy to use, it is focused on data storage and management, which makes it more appealing to data engineers and managers.

Snowflake focuses on making it easy to store, get, and handle data, while Databricks focuses on analytics workflows and data exploration. Choose Databricks for jobs that are based on analytics and Snowflake for easier data storage and management.

Databricks vs Snowflake: Machine Learning

Databricks has an environment for machine learning that lets different models be built. It also allows creation in many different programming languages.

Snowflake doesn’t have any ML packages, but it does have connectors that let you connect different ML tools. It also lets you access its storage layer or share query results, which can be used to train and test the models.

Snowflake can be used to build machine learning and analytics, but it needs to be connected to other solutions and doesn’t offer these services out of the box. Instead, it gives you tools that you can use to connect to other libraries or modules on the platform and get to the data.

Databricks vs Snowflake: Customer Support and Documentation

Both Databricks and Snowflake have strong customer help and a lot of information for users. Databricks has different levels of support, such as community forums, while Snowflake gives full customer service. Both platforms help users by giving them thorough documentation, tutorials, and other tools.

Using its community, Databricks might be better for collaborative development, while Snowflake’s dedicated support can help groups that need help right away. When choosing between the two, think about the size of your group, its budget, and how much help it needs.

Databricks : Pros and Cons


  • Unified data analytics platform for collaboration.
  • Seamless integration with Apache Spark for fast data processing.
  • Robust machine learning and AI capabilities.
  • Collaborative workspace accelerates project development.


  • Collaborative features might not be as extensive as other tools.

Snowflake : Pros and Cons


  • Cloud-native data warehousing for scalability.
  • Unique architecture separates compute and storage.
  • Data sharing and collaboration features enhance teamwork.
  • Elasticity and auto-scaling ensure consistent performance.


  • Data sharing and collaboration might not be as extensive as other platforms.
  • Native machine learning capabilities are not available.

Which one should you consider?

Databricks and Snowflake are both used to organize and analyze data, but in different ways. Databricks is an all-in-one platform for data analysis, machine learning, and AI creation that lets people work together. It works well for businesses that want to give their different data teams a single place to work. Snowflake is an expert in cloud-native data warehousing and analytics. It has a unique design and scalable storage and analytics solutions.

Which one you choose between Databricks and Snowflake depends on the needs of your company. Databricks could be the best choice if you want to work on data projects with other people and use machine learning. On the other hand, Snowflake might be a better choice if you need a system for data warehousing that is scalable and has good analytics tools. Consider your organization’s needs, the data ecosystem you already have, and your plans for future growth to figure out which tool will help you achieve your data management and analytics goals the best.


Is Snowflake or Databricks more popular?

Snowflake has 18.33% of the market. On the other hand, 8.67% of the market belongs to Databricks. Snowflake works best for both SQL and ETL tasks. Databricks is best for use cases that involve Data Science/Machine Learning and Analytics.

Does Databricks work with Snowflake?

In the Databricks Runtime, there is a Snowflake connection that can be used to read and write data from Snowflake. If you want to manage searches on Snowflake data, you might like Lakehouse Federation better. See How to use Lakehouse Federation to run searches.

Why use Databricks instead of Snowflake?

Because of how it is designed and built, SQL-based business data use cases work well with Snowflake. Databricks, on the other hand, supports SQL-based business intelligence and a bigger range of use cases, like recommendation engines and intrusion detection.

Editorial Staff
Editorial Staff
The Bollyinside editorial staff is made up of tech experts with more than 10 years of experience Led by Sumit Chauhan. We started in 2014 and now Bollyinside is a leading tech resource, offering everything from product reviews and tech guides to marketing tips. Think of us as your go-to tech encyclopedia!


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