Databricks vs. Snowflake: Which Platform Is Right for You?

Apple vs. Microsoft. Marvel vs. DC. Coke vs. Pepsi. Databricks vs. Snowflake. This rivalry has developed to be one of the most compelling of the modern era, with these one-time partners (yes, they used to refer business to each other!) now maneuvering to become the undisputed leader in this space. 

As a potential user of these solutions however, you’re likely stuck in a quandary: both promise to solve your business challenges, and both have their die-hard fans and disillusioned detractors. 

We’ve created this neutral guide to help you through the Databricks vs. Snowflake debate, and choose the solution that’s best for your needs. 

Overview of Databricks and Snowflake

Databricks

Databricks was founded in 2013 by the creators of Apache Spark at UC Berkeley’s AMPLab, with the goal of building a unified platform for big data processing, machine learning, and analytics.

Initially focused on offering a managed cloud-based Spark platform, Databricks has since expanded to a broader lakehouse architecture. It bridges the gap between data lakes (which store unstructured data) and data warehouses (optimized for structured data analytics). Over time, Databricks has integrated capabilities for real-time analytics, data engineering, and machine learning. In 2023, the company acquired MosaicML to enhance its AI and machine learning offerings.

Databricks offers a unified analytics platform for data engineering, data science, machine learning, and business analytics. It supports the likes of Python, R, Scala, SQL, and Java, along with integration for machine learning libraries like TensorFlow and PyTorch.

Going forward, it seems Databricks is focused on advancing the lakehouse architecture, with heavy investment in AI and machine learning, including generative AI tools – attempting to position itself as a leader in end-to-end data and AI management.

Snowflake

Snowflake is primarily a cloud data platform known for its data warehousing capabilities but has expanded into broader data management and analytics services. 

Founded in 2012, Snowflake aimed to build a cloud-native data warehouse solution. Snowflake officially launched its product in 2014 and differentiated itself by offering a cloud-only data warehousing service, which was designed to be easy to scale and manage.

Snowflake grew quickly by capitalizing on the shift to cloud computing. Unlike traditional on-premise databases, Snowflake was built to scale seamlessly across multiple cloud platforms (AWS, Azure, Google Cloud). It introduced features like automatic scaling, near-zero maintenance, and the ability to share data across organizations without copying data. Over time, Snowflake has expanded its focus to include data sharing, data lakes, and support for unstructured data. From a data storage perspective, Snowflake utilizes a columnar format on cloud storage (AWS, Azure, or GCP), handling storage separately from compute.

Snowflake appears to be focusing on expanding its platform beyond warehousing into broader data analytics and application development. It aims to serve as a comprehensive data cloud for enterprises, with a growing focus on supporting data collaboration, unstructured data, and expanding its developer ecosystem.

Databricks vs. Snowflake

In summary, Databricks started with a focus on big data and AI, evolving towards the Lakehouse model, integrating machine learning and analytics across data lakes and warehouses; while Snowflake started as a cloud-native data warehouse, expanding into a multi-cloud data platform aimed at data sharing and analytics.

Critically, both companies are moving towards a future where AI, data sharing, and multi-cloud capabilities are essential to their strategy. For example, Databricks’ website touts “Your data. Your AI. Your future. Own them all on the new data intelligence platform” while Snowflake announces “Everything (including machine learning, building apps, streaming data, python, governance, business continuity, unstructured data, sharing data) is easier in the AI Data Cloud.”

Interestingly, Snowflake can integrate with Databricks and vice versa through connectors, allowing users to leverage both platforms for different use cases. For example, some organizations use Databricks for large-scale data processing and machine learning, and then store the processed data in Snowflake for SQL-based analytics and reporting.

Tips for Choosing Between Databricks and Snowflake

When selecting between Databricks and Snowflake, it’s important to align your choice with your organization’s data strategy, technical resources, and future goals. Here are some factors to consider: 

1. Focus on core needs: What are your organization’s primary data requirements?
   Determine whether your organization’s focus is on structured data and business intelligence or advanced data science, AI, and big data transformations. Understanding these core needs will guide you toward the platform that best meets those priorities.
2. Evaluate data processing workloads: How complex are your data transformation processes? Consider the scale and complexity of your data processing needs, and then evaluate whether Snowflake or Databricks is more likely to get the job done effectively for you.
3. Consider platform complexity: How much technical expertise does your team have?
   Assess your team’s technical capabilities, and choose a solution that supports your team’s current skill set. 
4. Long-term platform goals: What features and capabilities do you need for the future?
   Evaluate the future direction of your data strategy. Whether you plan to build external data applications, invest in machine learning, or rely on third-party tools and services, understanding the platform’s roadmap can help ensure it will continue to meet your evolving needs.
5. Cost and efficiency: How important is cost optimization and resource efficiency?
   Consider the total cost of ownership, including platform costs and the human resources required to manage the system. Ensure you balance platform costs with operational efficiency.

Features And Use Cases Comparison

We’ve created a helpful Databricks vs. Snowflake features and use cases comparison:

Use Case	Snowflake	Databricks
Data Ingestion	COPY INTO for loading data into tables	Autoloader for real-time data ingestion
	Snowpipe for automated ingestion	Interacts with cloud storage natively (e.g., S3)
	3rd-party ETL tools (Fivetran, Stitch)	Supports Apache Iceberg and DBFS for data management
Data Transformations	SQL-based transformations using tasks, stored procedures, or dbt	Spark-based transformations using jobs, tasks, and Delta Live Tables
	Runs SQL workloads in virtual warehouses	Serverless SQL Warehouses support SQL transformations
Analysis & Reporting	Lightweight dashboards with Snowsight	In-built dashboards and notebook plots
	Supports BI tools like Tableau, Looker, Power BI	SQL visualizations and integrated dashboard tools
ML/AI	Snowpark for data science and machine learning	Comprehensive ML framework with managed MLflow and Model Serving
	Snowpark Container Services for hosting models	Strong integration with Python and Spark for AI
Data Applications	SQL-based apps with high-performance query serving	Real-time model serving for external applications
	Container Services for running web apps	Supports external triggers for running Spark jobs
Marketplace	Mature marketplace with native apps and datasets	Marketplace is less mature, focused on technology partnerships
Data Governance & Management	Advanced metadata management and cost management suite (e.g., Snowflake Horizon)	Unity Catalog for comprehensive data governance
Cost Management	Built-in cost visibility and resource monitors	Recently introduced system tables for cost management, with less visibility into cloud costs

Pricing Differences

Both companies claim to lower total cost of ownership. 

Databricks claims that ETL costs up to 9x more on Snowflake than Databricks Lakehouse, while Snowflake maintains that it provides an overall lower TCO with its fully managed service. 

Both Databricks and Snowflake use a usage-based pricing model, but their cost structures differ. 

Databricks offers pay-as-you-go pricing and committed-use discounts, with per-DBU pricing differing across products. Snowflake is priced per credit, which also depends on the tier chosen.

However, TCO is complex to calculate, as it involves both platform costs and human resources. A direct comparison between the two platforms can be misleading and highly dependent on your specific use case.

 

Which is Better: Databricks or Snowflake?

Choosing between Databricks and Snowflake depends on various factors, including your specific use case, your team’s expertise, the total cost in your situation, and your future plans. 

If your organization primarily focuses on SQL-based analytics or business intelligence, or if you’re dealing with more complex data science, machine learning, or large-scale transformations, your choice may vary based on which platform better aligns with your goals. 

Your team’s familiarity with tools like SQL, Python, or Apache Spark will also influence which platform is easier to integrate and manage. 

The total cost of ownership (TCO) can be challenging to calculate, as it includes both platform costs and the human resources required for maintenance. Snowflake may offer simplicity that reduces human overhead, while Databricks could lower compute costs through optimization but may require more technical effort. Your long-term plans, such as building machine learning models, data applications, or relying on third-party integrations, should also guide your decision. 

Ultimately, the right choice will depend on how well each platform fits your organization’s current and future needs.

FAQ

Q: What programming languages are supported by Databricks?

A: Databricks supports Python, R, Scala, SQL, and Java, along with integration for machine learning libraries like TensorFlow and PyTorch.

Q: Can Snowflake integrate with Databricks?

A: Yes, Snowflake can integrate with Databricks through connectors, allowing users to leverage both platforms for different use cases, such as querying Snowflake data within Databricks or combining Snowflake’s SQL analytics with Databricks’ data science tools.

Q: How does Snowflake handle data storage?

A: Snowflake stores data in a columnar format on cloud storage (AWS, Azure, or GCP), and handles storage separately from compute, allowing for scalable and cost-efficient data management.

Meet SQream: Industry-Leading GPU-Accelerated Data Processing

Two of the key considerations for any team evaluating Databricks vs. Snowflake are performance, and cost. 

What if there was a way to turbocharge performance, while slashing costs?

There is – meet SQream. 

SQream harnesses the speed, power, and efficiency of supercomputing reources to give you twice the speed at half the cost. 

SQream Blue presents a compelling alternative – or addition – to these platforms, particularly for organizations seeking improved cost-performance in big data analytics. In recent benchmarks, SQream Blue processed 30 TB of data in just 41 minutes, leveraging advanced GPU parallelism to deliver results 3x faster than Databricks. Furthermore, SQream Blue can offer up to 70% cost savings, making it an attractive solution for enterprises looking to reduce data processing expenses. 

SQream Blue is designed to integrate seamlessly with your existing data lakehouse setup, allowing businesses to offload the most performance-intensive and costly operations to SQream while maintaining flexibility. This hybrid approach can significantly optimize performance and cut down operational costs. Companies can distribute heavy workloads to SQream Blue to ensure scalability and achieve a greater return on investment, without fully replacing their current infrastructure.

Isn’t it time you future-proofed your data with GPUs? Get in touch with the SQream team to learn more.

Summary: Databricks vs. Snowflake

We covered a lot of ground, from the origins of these two platforms, to their current offerings, pricing, use cases, features, benefits, and more. 

We showed which solution could work for different use cases, and explored important questions to consider when choosing the tool that’s right for you. 

While there is no absolute winner when it comes to Databricks vs. Snowflake, there is a clear winner when it comes to the cost and performance, and that’s SQream.