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Databricks vs Snowflake: Similarities & Differences
Companies are constantly collecting massive amounts of data, and making sense of it requires powerful analytics platforms. Two names that often come up in this space in recent years are Databricks and Snowflake. But how do you choose between them?
This blog post will guide you through a general comparison between the key features of both Databricks and Snowflake.
What is Databricks?
Databricks is a unified analytics platform known for its ability to handle big data and machine learning workloads. It was founded by the creators of Apache Spark, and it offers a range of features designed to make data engineering, data science, and business analytics easier. You can learn more about the platform in our Introduction to Databricks course.
Features
Databricks uses the open-source Apache Spark framework to create data lakehouses that can combine the best of data warehouses and data lakes. It also offers a wide range of tools and services, including:
- Collaborative notebooks for Python, Scala, R, and SQL
- Automated cluster management
- Open-source LLM framework DBRX
- Built-in machine learning libraries and support for popular ML frameworks like TensorFlow and PyTorch
- Integration with popular BI tools like Tableau and Looker.
Use cases
Databricks is commonly used for real-time analytics, ETL processes, and machine learning. With its recent release of new AI tools, Databricks can also be used in:
- Building RAG models using Mosaic AI Vector Search
- Creating LLMs using Mosaic AI Pretraining
Advantages
Databricks has several key advantages that stand out among the competition:
- Big data handling: Databricks is specifically designed to handle massive amounts of data and can easily scale to meet growing demands.
- Unified platform: By offering a range of features and tools, Databricks provides a single solution for all your data analytics needs.
- Built-in machine learning and AI support: Databricks has good integrations with common machine learning and artificial intelligence frameworks and libraries, making it appealing for businesses looking to rapidly adopt and incorporate these technologies into their analytics processes.
What is Snowflake?
Snowflake is a cloud-based data warehousing solution that has gained popularity for its simplicity and scalability. It separates storage and compute resources, allowing businesses to scale up or down based on their needs. Our Introduction to Snowflake course takes you through the basics of the platform.
Features
Snowflake offers a fully managed, cloud-native data warehouse that's fast and easy to use.
Some of its notable features include:
- Automatic scaling and resource management
- Standard and extended SQL support
- Ability to handle both structured and semi-structured data
- Secure data sharing
Use cases
Snowflake is typically used in scenarios where businesses need to store and analyze large amounts of data quickly. This includes use cases like:
- Real-time analytics
- Ad hoc queries
- Business intelligence reporting
For example, it can be used to create a centralized data repository for different teams and departments to access and analyze shared data.
Advantages
Snowflake is known for its simplicity, scalability, and speed. Its key advantages include:
- Easy concurrency: Snowflake allows multiple users to query the same data simultaneously without any performance issues.
- Data sharing: With built-in features like Secure Data Sharing, Snowflake simplifies collaboration between teams and partners by allowing secure access to shared data from different accounts.
- Flexibility: Snowflake offers versatile storage options and supports various data formats, making it suitable for different types of workloads.
- Simple interface: Snowflake has a user-friendly interface that requires minimal training and technical expertise, making it accessible for non-technical users as well. Querying can also be done using SQL, which makes it easy to adopt.
You can learn more about how Snowflake works in this practical Snowflake tutorial.
Databricks vs Snowflake: Similarities
The two platforms share some similarities, including the following:
1. Cloud-based
Both Databricks and Snowflake are cloud-based platforms, making them accessible from anywhere with an internet connection. Having a cloud-based solution also means that users can easily scale up or down based on their needs without physical limitations.
2. Scalability
Both platforms have the ability to scale up or down based on demand, providing businesses with flexibility as their data grows. This auto-scaling feature makes it perfect for rapidly moving businesses with dynamic growth.
3. Querying language support
Databricks and Snowflake both offer support for SQL, the most widely used language for querying databases.
SQL is already a commonly used tool among many data analysts and data engineers. This means businesses can tap into the existing SQL skills of their employees to work with these platforms.
4. Data lake capabilities
Both platforms support data lakes, providing businesses with flexibility in storing and analyzing a variety of structured and unstructured data. Since both are designed from the ground up to handle big data, they can easily handle the workload of large data lakes.
Databricks vs Snowflake: Differences
1. Performance comparison
When it comes to performance, both Databricks and Snowflake shine but in different areas.
Databricks
Databricks excels in processing large datasets quickly, making it ideal for real-time analytics and machine learning. Its use of Apache Spark ensures high-speed data processing, even for complex queries.
Source: Databricks
Snowflake
Snowflake, on the other hand, is optimized for data warehousing. Its architecture allows for quick query performance, especially for structured data. The separation of storage and compute resources means businesses can scale each independently, ensuring optimal performance.
Winner
Databricks is the overall winner for performance for its better performance-to-cost ratio. The Databricks query engine is powered by Apache Spark, which makes a better choice for big data processing.
2. Scalability comparison
Scalability is crucial for businesses as their data needs grow. This is where the two platforms differ slightly.
Databricks
Databricks offers robust scalability, especially for big data and machine learning workloads. Its integration with cloud platforms like AWS, Azure, and Google Cloud allows businesses to scale resources as needed.
Snowflake
Snowflake’s automatic scaling feature is great. It allows businesses to scale up compute resources or scale down based on their workload automatically. This is made possible through their multi-cluster warehouses, where the number of clusters is added or removed according to the amount of compute load.
This feature ensures that businesses only use (and pay for) what they need, making it highly efficient.
Winner
Snowflake is the overall winner for scalability, thanks to its automatic scaling feature and versatile storage options.
3. Ease of use comparison
Ease of use can significantly impact the speed at which teams can adopt and start benefiting from a platform. Having a tool that's too difficult to use can lead to low adoption by data teams.
Let's compare both of the platforms below.
Databricks
Databricks offers a collaborative environment that can be complex for beginners but becomes intuitive as users become familiar with the platform. Its support for multiple languages makes it flexible but also adds to the learning curve.
Snowflake
Snowflake is praised for its simplicity and user-friendly interface. Its SQL-based querying language makes it accessible to users familiar with traditional databases. The platform's architecture also simplifies many administrative tasks, making it easier to manage.
Winner
Snowflake takes the crown for ease of use due to its simple interface and minimal technical expertise required. However, if the user is an intermediate one, this difference is not exactly noticeable since they are more well-versed in technical software.
4. Integration capabilities
Integration capabilities have become more of a need in recent years due to the sheer number of tools out there giving rise to varied forms of data workflows.
Databricks
Databricks integrates well with various data sources and platforms, including Hadoop, Apache Kafka, and cloud storage solutions. Its flexibility makes it suitable for diverse data environments.
Some notable integrations include:
- dbt, AirFlow for ETL workflows
- Amazon S3, Google BigQuery and Cloud Storage, Snowflake for data sources
- Qlik, Power BI, and Tableau for business intelligence (BI) tools
Snowflake
Snowflake offers robust integration capabilities with popular BI tools like Tableau, Looker, and Power BI. It also supports data integration platforms like Fivetran and Talend, making it easy to connect with existing data ecosystems.
Winner
Both tools are equally matched in this category, with their extensive integration options. However, Snowflake's compatibility with many BI tools from being longer in the market might give it a slight edge.
5. Security
Security is a top priority for businesses dealing with sensitive data.
Databricks
Databricks offers robust security features, including customer-managed keys, serverless infrastructure, and unified security for data and AI governance.
Customer-managed keys are tightly integrated with providers like:
- Microsoft Azure Key Vault for Azure
- AWS Key Management Service (KMS) using Amazon Web Services (AWS)
Snowflake
Snowflake also offers strong security features, including end-to-end encryption, network traffic security, multi-factor authentication through Access Control, and compliance with various industry standards. Its architecture inherently supports secure data sharing and access control, ensuring data protection.
It also ensures compliance with regulations like HIPAA, PCI DSS, and FedRAMP through its security capabilities and controls.
Winner
In the aspect of security, both platforms provide robust features, making it challenging to choose a winner. However, Databricks' tight integration with cloud providers might give it a slight advantage.
6. Cost
Cost is always a significant factor in choosing a data platform. Its a major factor to consider when balancing the compute load needed for your applications or queries.
Databricks
Databricks offers a pay-as-you-go pricing model, which can be cost-effective for businesses with fluctuating workloads. However, costs can add up quickly, especially for extensive data processing tasks.
Snowflake
Snowflake's pricing model is slightly different. It charges you a fee based on credit use. This is based on the separation of storage and computing, allowing businesses to scale costs based on usage. This model can be more predictable and often more affordable, especially for data warehousing needs. However, this can be restrictive if the user's workload is variable.
Winner
Both platforms offer reasonable pricing models, but overall, Snowflake's separation of storage and compute can be more budget-friendly for businesses with predictable workloads. However, Databricks' pay-as-you-go model can also be cost-effective for fluctuating workloads.
Databricks vs Snowflake: A summary
Here’s an overall summary of the two platforms and their key differences.
Feature |
Databricks |
Snowflake |
Overview |
- Unified analytics platform for big data & machine learning |
- Cloud-based data warehousing solution |
Key Features |
- Apache Spark-based data lakehouses - Collaborative notebooks for multiple languages (Python, Scala, R, SQL) - Automated cluster management - Machine learning & AI support - Integration with BI tools |
- Fully managed, cloud-native data warehouse - Automatic scaling & resource management - SQL support - Handles structured & semi-structured data - Secure data sharing |
Use Cases |
- Real-time analytics, ETL processes, machine learning, AI model building |
- Real-time analytics, ad hoc queries, business intelligence reporting |
Advantages |
- Big data handling & scalability - Unified platform for analytics & machine learning - Built-in AI & ML support |
- Easy concurrency for multiple users - Secure data sharing for collaboration - Versatile storage & data format support - Simple interface for non-technical users |
Performance |
- Optimized for big data, real-time analytics, & ML - High-speed data processing via Apache Spark |
- Optimized for data warehousing & structured data - Quick query performance due to separate storage & compute resources |
Scalability |
- Scalable for big data & ML with integration into AWS, Azure, GCP |
- Automatic scaling with multi-cluster warehouses - Pay only for used resources |
Ease of Use |
- Collaborative, complex for beginners but intuitive for experienced users - Multi-language support adds learning curve |
- Simple, user-friendly interface with SQL querying - Minimal technical expertise required |
Integration Capabilities |
- Wide integration with platforms like Hadoop, Apache Kafka, BI tools (Tableau, Looker), cloud storages |
- Extensive integration with BI tools (Tableau, Looker, Power BI), ETL platforms (Fivetran, Talend) |
Security |
- Customer-managed keys, serverless infrastructure, unified data & AI governance - Integrated with Azure Key Vault & AWS KMS |
- End-to-end encryption, network traffic security, multi-factor authentication, access control - Complies with HIPAA, PCI DSS, FedRAMP |
Cost |
- Pay-as-you-go pricing - Cost-effective for fluctuating workloads |
- Credit-based pricing (separate storage & compute) - Predictable & potentially more affordable for stable workloads |
Final Thoughts
Overall, both Databricks and Snowflake offer powerful solutions for data management and analytics. Databricks excels in real-time data processing and machine learning, while Snowflake shines in data warehousing and ease of use. Your choice will depend on your specific needs and future goals.
If you'd like to learn more about Databricks, our Getting Started With Databricks webinar or the Introduction to Databricks course would be the perfect place to start. For those interested in Snowflake, we also have an Introduction to Snowflake course that’s made for beginners.
Databricks vs Snowflake FAQs
Which is better Snowflake or Databricks?
It depends on your specific needs and use case. Snowflake is a cloud-based data warehousing platform, while Databricks is an analytics and machine learning platform. Overall, Databricks is better for advanced analytics and Snowflake is better for simpler analysis.
Should I learn Spark before Databricks?
While Spark is the underlying technology used by Databricks, it is not necessary to learn Spark before using Databricks. However, having knowledge of Spark can help you get more out of Databricks.
Why Databricks is so popular?
Databricks offers a comprehensive platform for data analytics and machine learning, with features such as collaborative notebooks, automated cluster management, and built-in libraries. It also integrates well with other popular tools and platforms.
Can I use Databricks and Snowflake together?
Yes, Databricks and Snowflake can be integrated to work together for efficient data analysis and storage. This allows users to take advantage of the strengths of both platforms and create a seamless workflow.
Is it necessary to have prior experience with SQL or coding to use Databricks and Snowflake?
Yes, it is necessary to have a good understanding of SQL to use both Databricks and Snowflake. SQL is used for querying and managing data on these platforms.

I'm Austin, a blogger and tech writer with years of experience both as a data scientist and a data analyst in healthcare. Starting my tech journey with a background in biology, I now help others make the same transition through my tech blog. My passion for technology has led me to my writing contributions to dozens of SaaS companies, inspiring others and sharing my experiences.
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