Snowflake vs Databricks Data Intelligence Platform

by Gralio Feb 06, 2025

Snowflake and Databricks cater to distinct data needs. Snowflake is ideal for established businesses prioritizing SQL-based data warehousing and analytics, while Databricks is better for businesses seeking a unified platform for data, analytics, and AI, especially those working with diverse data types or heavily invested in open-source ecosystems.

At Gralio.ai we help to simplify your decision-making process by offering detailed, side-by-side software comparisons like this one, to help you confidently choose the tool that aligns with your business goals.

This comparison was created by analysing 935 reviews and 60 websites, saving 5 hours, 41 minutes of reading.

Snowflake

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Databricks Data Intelligence Platform

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About

Snowflake is a cloud-based data platform designed for businesses to store, manage, and analyze large amounts of data. It works like a giant, virtual warehouse for all your data, accessible from anywhere. Snowflake adapts to your needs, scaling its power and cost up or down on demand. This makes it suitable for tasks like analyzing customer trends, building data-driven applications, or sharing data securely with partners. Its strength lies in handling massive datasets with high speed and efficiency.

Databricks Data Intelligence Platform is a cloud-based platform designed to help mid-size to large businesses manage and analyze data. It offers a range of tools for data warehousing, data engineering, data science, and machine learning, all in one place. Databricks aims to simplify data processes, reduce costs, and enable companies to build and deploy AI solutions. They are well-known for their work with open-source technologies, making them a popular choice for businesses invested in those ecosystems.

Summary

Main difference

Snowflake excels in data warehousing and analytics with a focus on SQL and scalability for structured data, while Databricks shines in unifying data and AI tasks, leveraging open-source tools and a lakehouse architecture suitable for diverse data types including unstructured data and real-time streams.

Relative strengths of Snowflake (compared to Databricks Data Intelligence Platform)

Strong SQL support and data transformation capabilities.
Scalable data warehousing for structured data.
Easy-to-use interface and integration with various data tools.

Relative weaknesses of Snowflake (compared to Databricks Data Intelligence Platform)

Limited machine learning capabilities compared to Databricks.
Can be costly for smaller businesses or those with limited data needs.
Less flexible for unstructured data and real-time data processing compared to Databricks.

Who should use Snowflake VS. Databricks Data Intelligence Platform

Snowflake is a cloud-based data platform ideal for data-driven businesses of all sizes. It excels at managing and analyzing large datasets, offering scalability and high performance. Users praise its ease of use and integration capabilities, while some note limitations in machine learning features and unstructured data support.

Databricks is a robust data intelligence platform best suited for mid-size to large businesses looking to streamline their data processes. Users praise features like the Databricks SQL warehouse and Unity Catalog for improved data governance. However, some have noted slow cluster startup times and room for improvement in the AI assistant. It's a popular choice for companies invested in open-source technologies, particularly within the software, IT, and telecommunications sectors.

Best for small, medium, and large enterprises.
Ideal for Software, IT, and Telecommunications, suitable for various other industries.

Best for medium to large businesses seeking unified data and AI solutions.
Ideal for the software, IT, and telecommunications industries.

Snowflake and Databricks Data Intelligence Platform features

Supported

Partially supported

Not supported

Type in the name of the feature or in your own words tell us what you need

Lakehouse architecture

Supported

Snowflake supports lakehouse architecture through Iceberg integration, simplifying data management and enabling collaboration.

Supported

Databricks Data Intelligence Platform is built on a lakehouse architecture.
Simplified data and AI acceleration

Supported

Snowflake simplifies and accelerates data and AI solutions, from data warehousing to generative AI.

Supported

Databricks simplifies and accelerates data and AI initiatives, from ETL to warehousing and generative AI.
Data intelligence engine

Supported

Snowflake offers data intelligence capabilities through its Snowflake Intelligence platform powered by Cortex AI.

Supported

Databricks Data Intelligence Platform leverages DatabricksIQ to understand data uniqueness, enabling tailored processing and analysis.
Data Warehouse Integration

Supported

Snowflake excels in data warehouse integration, seamlessly connecting with diverse data sources like relational databases, cloud platforms, NoSQL databases, and streaming services. Its compatibility with third-party tools, data sharing capabilities, flexible architecture, support for semi-structured data, and query optimization prowess make it highly capable of integrating with other warehouse databases.

Supported

Databricks Data Intelligence Platform is highly capable of integrating with other warehouse databases.
Data Source Integration

Supported

Snowflake supports integrating data from various external sources, including cloud-based and on-premises sources. It offers pre-built connectors, supports ETL/ELT processes, and allows data transformation during loading.

Supported

Databricks Data Intelligence Platform offers extensive options for integrating with external data sources, including built-in connectors, driver support, and integrations with BI and ETL tools.
Columnar storage

Supported

Snowflake stores data in a columnar format, meaning data for each column is stored together, separate from other columns. This is beneficial for data analytics and business intelligence applications where calculations are often performed over a single data column.

Supported

Databricks Data Intelligence Platform utilizes Apache Parquet, a column-oriented data file format, enabling efficient columnar storage within its lakehouse architecture. This approach optimizes storage and query performance for analytical workloads.

Qualities

Value and Pricing Transparency

-0.26

Neutral sentiment

+0.14

Neutral sentiment
Customer Service

+0.56

Rather positive sentiment

+1

Strongly positive sentiment
Ease of Use

+0.75

Strongly positive sentiment

+0.79

Strongly positive sentiment
Reliability and Performance

+0.81

Strongly positive sentiment

+0.8

Strongly positive sentiment
Ease of Implementation

+0.33

Rather positive sentiment

+0.26

Neutral sentiment
Scalability

+0.93

Strongly positive sentiment

+0.93

Strongly positive sentiment

Snowflake and Databricks Data Intelligence Platform Pricing

No data

User sentiment

Neutral

-0.26

See full Pricing page

No data

User sentiment

Neutral

+0.14

See full Pricing page

Snowflake and Databricks Data Intelligence Platform review insights

935 reviews analysed from and

Users love

The ability to easily scale warehouses based on requirements
Query profile is a very useful feature
The user interface is easy to implement and navigate
Seamless integration with various data tools

The Databricks SQL warehouse serverless feature is excellent.
Unity Catalog improves data governance and accessibility.
Lakehouse flow monitoring provides helpful insights into data processes.
Pandas Spark API allows for easy distributed computing with Python.

Users dislike

Limited machine learning features
Data migration efforts are large to transfer everything from on premises to cloud
The new UI is too confusing
Unstructured data support is lacking

Cluster startup times are slow.
Documentation could be improved with more examples.
AI assistant needs improvement, especially for complex SQL queries and errors.
Occasional unplanned downtime can be disruptive.

Snowflake and Databricks Data Intelligence Platform Ratings

Glassdoor

4.0/5

(749)

Glassdoor

4.3/5

(1199)

Company health

Employee growth

20% increase in the last year

23% increase in the last year

Web traffic

1% increase in the last quarter

7% decrease in the last quarter

Financing

March 2022 - $2B

September 2024 - $14B

How do Snowflake's data sharing capabilities compare to Databricks' Delta Sharing?

Snowflake's data sharing allows sharing selected data securely with other Snowflake accounts or external consumers using secure data shares, without copying or transferring data. Databricks' Delta Sharing, while also enabling secure data sharing, uses an open protocol allowing data sharing with recipients regardless of their computing platform (they don't need Databricks or Delta Lake). So, Snowflake's sharing is optimized within its ecosystem, while Databricks promotes broader cross-platform sharing.

Which platform, Snowflake or Databricks, better integrates with existing NoSQL databases?

Snowflake has more explicit features and documentation surrounding NoSQL database integration. While Databricks can likely integrate with NoSQL databases through its data source integration capabilities, Snowflake emphasizes this integration more directly in its feature set. This suggests Snowflake may offer a smoother, more purpose-built integration experience with existing NoSQL infrastructure.

What are the advantages of Snowflake?

Snowflake offers superior scalability (0.93) compared to Databricks (0.93), allowing it to handle varying data loads efficiently. Furthermore, Snowflake's robust SQL support and data transformation capabilities, combined with excellent data warehousing integration, make it a strong choice for businesses prioritizing traditional data warehousing and BI tasks. User reviews highlight Snowflake's ease of use and navigation, particularly its query profile feature and seamless integration with various data tools. This suggests a smoother user experience for data exploration and analysis.

What are the disadvantages of Snowflake?

Snowflake's disadvantages include limited machine learning capabilities compared to platforms like Databricks, potentially extensive data migration efforts when transitioning from on-premise solutions, a user interface that some find confusing, and less robust support for unstructured data. Additionally, some users have noted concerns about pricing transparency.

Alternatives to Snowflake and Databricks Data Intelligence Platform

Microsoft SQL Server

Microsoft SQL Server is a database management system for businesses of all sizes. It helps you analyze various types of data and can be used in multiple environments, including on your servers and in the cloud. SQL Server offers high performance and strong security features. It allows developers to build applications using different programming languages and provides mobile business intelligence tools.

Alteryx

Alteryx is a data analytics platform designed for businesses of all sizes. It offers a user-friendly, drag-and-drop interface to prepare, analyze, and visualize data without needing coding skills. Alteryx excels at automating data tasks, helping you uncover insights faster and make better decisions. It's used across various departments and industries to improve efficiency and gain a competitive advantage.

Palantir Foundry

Palantir Foundry is a software platform designed to help large organizations manage and analyze their data. It brings all your data together in one place, making it easier to find insights, make decisions, and automate operations. Foundry is used by businesses in various sectors, including finance, healthcare, and government. It is known for its advanced security features and its ability to handle very large and complex datasets.

Google Cloud BigQuery

Google BigQuery is a fully managed data warehouse service that helps businesses analyze massive datasets to gain insights. It's serverless, so you don't need to manage any infrastructure, and it can handle data from various sources. BigQuery is designed for fast query processing, allowing you to quickly explore and analyze your data. It's a powerful tool for data-driven decision-making in any industry, regardless of company size.

Tableau

Tableau is a popular data analytics platform that helps businesses make sense of their data. It offers powerful, user-friendly tools for visualizing data, creating interactive dashboards, and sharing insights across your organization. Tableau is suitable for companies of all sizes and is known for its intuitive drag-and-drop interface, even for non-technical users. It allows you to connect to various data sources, analyze information visually, and discover trends to make better business decisions.

SAS Viya

SAS Viya is a cloud-based data analytics platform designed to help businesses of all sizes make better decisions through data insights. It offers a wide range of tools for data management, analysis, and modeling, including machine learning and artificial intelligence capabilities. SAS Viya allows users to access and analyze data from various sources, build and deploy predictive models, and share insights through interactive dashboards and reports. Its focus is to provide a comprehensive platform for data professionals and business users to collaborate and extract value from their data, enabling faster and more informed decision-making.

Page co-authored by

Michal Kaczor

CEO at Gralio

Michal has worked at startups for many years and writes about topics relating to software selection and IT management. As a former consultant for Bain, a business advisory company, he also knows how to understand needs of any business and find solutions to its problems.

Tymon Terlikiewicz

CTO at Gralio

Tymon is a seasoned CTO who loves finding the perfect tools for any task. He recently headed up the tech department at Batmaid, a well-known Swiss company, where he managed about 60 software purchases, including CX, HR, Payroll, Marketing automation and various developer tools.

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