For businesses that rely on data to drive decisions—whether it’s e-commerce platforms tracking customer behavior, financial institutions forecasting trends, or tech companies building AI models—robust data management and analytics systems are a must. As the need for efficient data pipelines and insightful analysis grows, two platforms have emerged as leaders in the field: Databricks and Snowflake.
Founded in 2013, Databricks was initially developed as a unified analytics platform designed to enable large-scale distributed data processing, advanced analytics, and machine learning workflows. On the other hand, Snowflake came out about a year later, positioning itself as a cloud-native data warehousing solution. Its aim was to simplify the process of storing, managing, and querying large amounts of structured and semi-structured data on a cloud architecture.
While distinctly different in their original goals, both companies have since expanded their offerings and to include services and features that often overlap with one another. As the lines between Snowflake vs Databricks blur, it is more difficult for businesses to determine which platform better suits their needs, goals, and infrastructure.
This post covers everything you need to know about Databricks vs Snowflake, their features, similarities, and differences, and which one best suits your business model.
Understanding the Basics of Databricks vs Snowflake
It’s best to have a clear and general definition of what Databricks vs Snowflake each bring to the table as data storing and processing platforms. Understanding their core offerings and primary use cases will help you identify which solution aligns better with your specific needs and workflows.
Moreover, it would help if you had a general understanding of data warehouses, lakes, and lakehouses to better understand which platform suits your business model. We will briefly get into these terms in this post.
What Is Databricks?
In simple words, Databricks comes as a platform for storing, processing, and analyzing large volumes of data, both structured and unstructured. Databricks is the pioneer in combining the best of data lakes and data warehouses to offer what is called a Data Lakehouse.
A Data Warehouse allows for storing structured data in a highly organized schema, suitable for business intelligence and reporting. On the other hand, a data lake uses flat and inexpensive storage formats for vast amounts of raw and unstructured data. It is mainly used for big data processing and exploratory analysis. Databrick’s Lakehouse platform unifies analytics, data science, and AI/machine learning without having to duplicate data between two platforms.
Moreover, Databricks’ workspace lets teams collaborate on tasks such as ETL, machine learning, and analytics using familiar languages like Python, SQL, and R. Databricks comes as a platform-as-a-service (PaaS).
What Is Snowflake?
On the other side of the story, Snowflake stands as an easy-to-use cloud-based data warehouse. Snowflake can run on major cloud providers like AWS, Azure, and Google Cloud. Thanks to its multi-cluster shared data architecture, Snowflake allows multiple users access the same data without performance degradation.
Compared to traditional on-premise data-storing infrastructures, Snowflake is much more scalable and requires minimal maintenance. Moreover, Its Snowflake Data Marketplace enables the secure and seamless sharing of live data across organizations without duplicating it. Snowflake is a software-as-a-service (SaaS) solution available for different businesses and organizations.
Databricks vs Snowflake: A Head-to-Head Comparison
While the line between the services offered by Snowflake vs Databricks is blurry, the two are distinctively different in architecture, ecosystem integration, security, and many other aspects. Let’s break it down to a head-to-head comparison between Databricks vs Snowflake.
Architecture
Snowflakes cloud-based architecture is optimized for structured data and excels in traditional analytical workloads. Designed for data warehousing, Snowflake’s architecture consists of three main layers:
- Storage Layer: Data is stored in cloud object storage, segregating compute and storage for independent scaling. Snowflake optimizes how data is structured, compressed, and accessed.
- Compute Layer: Known as virtual warehouses, this layer allows for concurrent, independent execution of queries with elastic scalability.
- Cloud Services Layer: Provides critical management features, including security, metadata management, and query optimization.
Databricks uses Lakehouse architecture built on Apache Spark. Its architecture is ideal for organizations with multi-format data requirements and advanced analytics needs. It also contained three primary layers:
- Delta Lake: At its core, Databricks employs Delta Lake, an open-source storage format that brings ACID transactions, schema enforcement, and time travel to data lakes.
- Unified Data Management: The architecture supports diverse data types, from structured to semi-structured and unstructured, making it highly versatile.
- High-performance Compute: With its integration with machine learning frameworks and analytics tools, Databricks facilitates complex workloads like AI/ML and real-time data streaming.
Key Architecture differences
While Snowflake is more specialized in structured data warehousing, databricks is adept at handling a broader spectrum of data types. Furthermore, Snowflake is tailored for SQL-based analytics, whereas Databricks focuses on comprehensive data science and machine learning. It is worth mentioning that Databricks also has a SQL data warehouse engine.
Performance and Scalability
In the compute layer, Snowflake allows for automatic scaling through virtual warehouses. This allows for the seamless handling of concurrent workloads as demand increases and scales down when resources are not needed to optimize costs. Its unique multi-cluster architecture ensures that multiple users and workloads can access the platform without bottlenecks. Moreover, Snowflake employs advanced query optimization techniques and columnar storage to accelerate the analytics of structured data.
One of the key features of Databricks is Massive Parallel Processing (MPP) that allows to efficiently process vast amounts of structured, semi-structured, and unstructured data in parallel. Moreover, with the integration of Delta Lake, you can maintain ACID properties even on large-scale data operations and benefit from caching and optimization strategies. Lastly, Databricks supports real-time data streaming, making it ideal for dynamic workloads requiring low latency, such as IoT or financial transactions.
Scalability Differences
Snowflake specializes in scaling traditional data warehousing workloads. Databricks, on the other hand, is more robust in scaling complex and large-scale data engineering and AI/ML tasks.
Ecosystem and Integration
Although not the case in the past, both platforms have become compatible with most major data acquisition vendors. Snowflake is fully integrated with cloud providers like AWS, Azure, and Google Cloud. At the same time, Databricks offers a cloud-agnostic platform that ensures smooth operation across all cloud platforms. Moreover, both platforms integrate with business intelligence tools like Tableau, Power BI, and Looker.
Key Integration Differences
Snowflake is a fully proprietary, managed service with a closed-source code base. While it integrates well with many open-source tools, these integrations are often facilitated through APIs or third-party connectors rather than being built on open-source foundations. On the other hand, Databricks provides native compatibility with many open-source tools and libraries, aligning more closely with organizations that prefer open-source flexibility.
Security and Governance
When it comes to security, Snowflake offers more governance and regulatory compliance through pre-made frameworks. To name a few, Snowflake adheres to SOC.2 Type II, HIPPA, GDPR, and FedRAMP, making it suitable for industries like healthcare and finance right out of the box. Moreover, Snowflake offers dynamic data masking and access policies, enabling organizations to maintain strict control over sensitive information.
Databricks also has a solid security foundation, particularly for data engineering and machine learning workflows, and provides granular access control (RBAC and IAM). Databricks can also leverage the native security features of cloud providers, networking, and identity management.
Key Security differences
While both platforms can offer excellent security measures, they tackle this task differently. Snowflake offers built-in security features for dynamic data masking and compliance across different industries. Databricks, on the other hand, might require some additional configuration and reliance on the underlying cloud provider for some compliance-specific features.
Data Science, AI, and Machine Learning Capabilities
Snowflake primarily focuses on integrating third-party tools and enabling data preparation for AI/ML workflows. One solution the company came up with was Snowpark, an environment that allows data engineers and data scientists to write data transformation and processing code using languages like Python, Java, and Scala within Snowflake’s architecture. Moreover, Snowflake can connect with major platforms like DataRobot, Amazon SageMaker, and Azure Machine Learning.
This is one of the areas in which Databricks proves triumphant over Snowflake. It stands out as a purpose-built platform for data science, machine learning, and AI workflows. It has built-in features that cater to the entire ML lifecycle, from data engineering to model deployment. It natively supports open-source tools like TensorFlow and PyTorch. Thanks to its unified analytics platform, Databricks bridges the gap between data engineering and machine learning. This enables teams to preprocess data, train models, and deploy them seamlessly on the same platform. Also, tools like AutoML allow users to prototype machine learning models without extensive coding.
AI/ML-Related Differences
Snowflake mainly focuses on preparing data for external AI/ML applications, while Databricks provides end-to-end capabilities for building, training, and deploying models. Databricks should be the go-to option if your business relies heavily on AI/ML workflows.
Billing and Pricing Models
Snowflake and Databricks use different pricing models, which reflect their focus and capabilities. While both operate on usage-based pricing, their structures and costs vary significantly.
Snowflake bases its pricing plans on credits and has three key cost components:
- Compute Layer: Virtual warehouses are billed per second with a minimum of 60 seconds. The cost starts at $3 per credit for the Standard Edition and can go up to $4–$5 for Enterprise Editions, depending on the cloud region and subscription type.
- Storage Layer: Storage costs $40 per TB/month on demand, with prepaid options available at a discounted rate of $24 per TB/month.
- Data Transfer Costs: While data ingress is free, egress charges depend on the cloud platform and destination.
Based on the example on Snowflake’s official website, it can look something like this: running a “Large Warehouse” (8 credits/hour) for 8 hours daily with 100 TB of storage might cost approximately $3,384/month, considering compute, service, and storage costs.
Databricks uses DBUs (Databricks Units), which represent the processing capability per second. Pricing varies based on:
- Compute Type: Databricks supports different workloads, including data engineering, analytics, and machine learning. Prices range from $0.07–$0.55 per DBU/hour, depending on the workload type and cloud platform.
- Cloud Platform: Costs vary across AWS, Azure, and Google Cloud. For instance, on Azure, a basic data engineering workload starts at $0.15/DBU/hour, and machine learning workloads are priced higher due to GPU requirements.
- Clusters and Configurations: Databricks offers significant flexibility in cluster configurations, influencing costs. Compute and storage charges apply separately, based on the cloud provider.
With Databricks, moderate machine-learning workloads can cost between $1,500-$5,000 per month based on specific usage and configuration. For an accurate and tailored cost prediction, you can use Databricks’ pricing calculator available on its website.
Databricks vs Snowflake Pricing Differences
The monthly cost for using advanced features of Databricks can be more expensive due to its high-performance compute and flexibility for diverse data formats and AL/ML capabilities. Snowflake generally offers a cost advantage for traditional analytics and SQL-based queries, especially for businesses with simpler data pipelines. However, costs for both platforms depend heavily on workload specifics, resource usage, and cloud provider configurations.
Databricks vs Snowflake: Pros and Cons
When it comes to the differences between Databricks vs Snowflake, both platforms offer many unique strengths tailored to different types of users and workload. Below is a comprehensive table that sums up all the essential features of each system.
| Feature | Databricks | Snowflake |
|---|---|---|
| Primary Use Case | Data science, machine learning, and real-time analytics | SQL-based data warehousing and business intelligence |
| Architecture | Lakehouse architecture with Delta Lake | Cloud data warehouse with separate compute and storage |
| Supported Data | Structured, semi-structured, unstructured | Structured, semi-structured |
| Performance | Optimized for big data and streaming workloads | Optimized for SQL and analytical queries |
| BI Integration | Customizable integration with Tableau, Power BI, etc. | Seamless, native connectors for Tableau, Power BI, etc. |
| AI/ML Support | Advanced ML frameworks and libraries | Limited; relies on Snowpark and external integrations |
| Open Source Compatibility | Extensive; supports Spark, Delta Lake, and more | Limited; closed-source architecture |
| Security and Compliance | Strong, with role-based access, encryption, and auditing | Robust, with built-in advanced compliance features |
| Cloud Platforms Supported | AWS, Azure, GCP | AWS, Azure, GCP |
| Pricing Model | Usage-based via DBUs, granular billing | Usage-based, compute/storage billed independently |
| Ease of Use | Requires technical expertise for advanced workflows | Designed for simplicity and business analyst accessibility |
Databricks vs Snowpark: A Comparative Overview
To compete with Databricks, Snowflake developed Snowpark, a platform for data processing and advanced analytics. While both Databricks and Snowpark are advanced in what they offer, they offer solutions for different tasks. Snowpark is a development environment aimed at enhancing data application functionality within Snowflake’s cloud data platform. It allows developers to write data transformation code in popular programming languages like Python, Java, and Scala.
Snowpark focuses on streamlining the work and offering a user-friendly interface. While advantageous, the UI lacks some of the more advanced features for AI/ML workloads that are otherwise available in Apache Spark, the platform on which Databricks is built. That said, Snowpark allows data engineers and developers to process data natively in Snowflake’s architecture while leveraging its strengths in SQL-based analytics and security.
On the other hand, Databricks still offers a more mature ecosystem for data science and machine learning, even when considering Snowpark. It provides end-to-end solutions for big data processing and complex ML workflows. As mentioned, its Lakehouse architecture allows it to be much more versatile for handling different data formats.
Final Thoughts
When it comes to Databricks vs Snowflake, it’s important to note that both represent leading-edge solutions in the landscape of data analytics and management. Thanks to its lakehouse structure and support for advanced ML workflows, Databricks remains as a robust platform for professional teams who handle a variety of data formats and rely heavily on machine learning and AI.
At the same time, Snowflake’s primary focus is on delivering an easy-to-use system for data warehousing and SQL-based analytics. It is a more appealing option for businesses focused on structured and semi-structured data.
Ultimately, Databricks offers more to the table in terms of advanced features and versatility. While that’s excellent, the complexity might not be something all business models require to tackle their tasks.
FAQs
What are the disadvantages of Databricks?
- Steeper learning curve for non-technical users.
- Higher costs for advanced AI/ML features.
- Limited built-in BI tools, requiring third-party integrations.
- Some compliance features rely on cloud provider configuration.
Why Databricks over Snowflake?
- Handles diverse data formats with Lakehouse architecture.
- Strong open-source tool integration.
Can Databricks and Snowflake work together?
Yes, Databricks and Snowflake can integrate effectively. Organizations can use Snowflake for data warehousing and SQL-based analytics while leveraging Databricks for advanced data science and machine learning tasks.
