Choosing between Databricks and Snowflake can be challenging for organizations navigating a modern data infrastructure. While both platforms are powerful in their own right, they have different strengths and weaknesses.
The story of Databricks and Snowflake began with a partnership as each concentrated on different data management areas. While Snowflake focused on data warehousing, Databricks found its place in managed Spark and swiftly extended into machine learning workloads.
Fast-forward to the present, and both systems have experienced significant changes. Today, they are comprehensive, all-in-one data cloud platforms that support a wide range of data use cases.
How are Snowflake and Databricks different? Which one is a better choice for your use case? In this article, I will do my best to provide an objective comparison of their core features, focusing on strengths and trade-offs.
Snowlake: Key Capabilities and Features
Cloud-Native Data Warehouse:
Snowflake is a fully managed, cloud-native data warehouse designed for simplicity. It is highly optimized for SQL workloads and is popular among data analysts for its ease of use and automatic scaling capabilities.
Unlike Databricks, Snowflake is a closed platform. However, it shines in offering built-in features such as automatic scaling, data sharing, and out-of-the-box performance optimization for analytics and BI workloads.
Snowflake has some unique features that distinguish it from other cloud-based data warehousing solutions:
| Snowflake Feature | Definition |
|---|---|
| Scalability | With Snowflake’s auto-scaling capability, the warehouse size can be adjusted based on demand. Snowflake continuously analyzes the workload, taking into account query complexity, resource utilization, and concurrency, to carry out scaling actions. |
| Time Travel | You can conveniently access previous data that may have been altered or destroyed within a specific timeframe. This allows you to recover prior versions of data, offering a complete picture of how data changes over time, which makes auditing and compliance easier. |
| Near-Zero Management | Snowflake is a cloud-based, fully managed platform with no hardware to choose, install, configure, or manage. The platform includes auto scaling, auto suspend, and built-in performance tuning capabilities, eliminating the need for manual administration. |
| Cloning | Zero-copy cloning is a quick and cost-effective solution to duplicate any table, schema, or whole database. Cloning occurs instantly and doesn’t require additional memory until changes are applied to the new copy. |
| Availability | Snowflake controls failover and resource allocation automatically, so you are unlikely to notice any impact from hardware failures or outages. This ensures that you have constant access to your data while maintaining operational continuity. |
| Data Caching | Snowflake includes a caching method that speeds up frequently conducted queries. This minimizes the time required to retrieve data from storage. |
| Data Sharing | You can share your data with others without producing a new duplicate of the existing data. All sharing occurs via Snowflake’s services layer and metadata store, so you just pay for the processing resources required to query the shared data, as storage is not used. |
| Micro-Partitioned Data Storage | Snowflake stores data in encrypted compressed files called micro-partitions. This enables Snowflake to scan only the required micro-partitions rather than full tables, which can greatly improve query performance. |
| User-Friendly Interface | The platform has a web-based interface that allows you to effortlessly manage and manipulate data without having to write complicated code or queries. |
| Snowpark | Snowpark is a set of simple libraries that allow you to process non-SQL code within Snowflake. You can write in Java, Python, or Scala, whichever language you like, and run it in Snowflake’s virtual warehouse. |
| Automatic Performance Tuning | The platform is equipped with a powerful query optimization engine that can automatically fine-tune query settings. This enables you to seamlessly query big databases without the need for manual tuning or configuration. |
Benefits of Snowflake
Simplicity and SQL-first Approach
Snowflake’s SQL-centric architecture is one of its biggest strengths. It is designed to cater to data analysts, offering a familiar SQL interface for querying and analyzing data. Its ease of use and out-of-the-box performance optimization make it ideal for organizations that need a fast, efficient, and scalable solution for structured data.
Automatic Scaling and Performance
Snowflake’s ability to automatically scale resources is a strong differentiator. This allows users to focus on querying data without having to worry about resource management or manual scaling, offering seamless performance during high-concurrency workloads.
Data Sharing and Collaboration
Snowflake’s Data Sharing feature enables easy, live data sharing across different Snowflake accounts. It is built for collaboration, making it particularly valuable for organizations that need to share data across teams or with external partners without the need to copy or move data.
Databricks: Core Features and Advantages
What is Databricks? Databricks is optimized for data engineering, machine learning, and advanced analytics. It is built on Apache Spark and integrates with open-source technologies like Delta Lake and Apache Iceberg, making it a flexible choice for big data and machine learning use cases.
Databricks provides powerful data engineering and data science processes while also serving as the industry’s top data lakehouse. It can process data up to 12 times quicker than competitors, manage complex machine learning components and generative AI models, and consolidate the data warehouse/data lake, data pipelines, and data catalogs into a single platform while still enabling advanced governance features.
The platform offers the following features:
| Databricks Feature | Definition |
|---|---|
| Robust Analytics Platform | Databricks offers a unified platform for data engineering, data science, and artificial intelligence, allowing teams to collaborate. |
| Interactive Workspace | It comes with an interactive workspace with support for Python, R, Scala, SQL, as well as notebooks for data exploration and visualization. |
| Data Pipelines | Users can create data intake, transformation, and machine learning pipelines. |
| Performance + Scalability | Databricks uses Apache Spark to provide excellent performance and scalability for large data analytics and AI workloads. |
| AI/Machine Learning | It includes libraries and tools for creating, training, and deploying machine learning models at scale. |
| Automation Of Machine Learning Lifecycle | Databricks automates the full machine learning lifecycle, including experiment tracking, model packaging, and model deployment, with MLflow and other tools. |
| Delta Lake | Delta Lake adds ACID transactions, data quality enforcement, time travel and other reliability features to data lakes stored in cloud object stores such as Amazon S3. |
| Rich Visualizations | Users can create rich visualizations and dashboards that provide insights. |
| Enterprise-Grade Security | Databricks offers enterprise-grade security, which includes access controls, encryption, auditing, and other features. |
Benefits of Databricks
Flexibility for Diverse Workloads
Databricks is designed for more than just data analytics; it excels in data engineering, machine learning, and real-time streaming. The platform handles structured, semi-structured, and unstructured data at scale, making it ideal for complex workflows.
Open Source Approach
One of Databricks’ key advantages is its commitment to the open-source ecosystem. It integrates with Apache Spark, Delta Lake, and Apache Iceberg, allowing users to work with open data formats and avoid being locked into a proprietary system. This flexibility is especially appealing for organizations that value open-source innovation.
Unified Platform for AI and Big Data
With Databricks, users can integrate data engineering, data science, and machine learning in one unified platform. It’s optimized for distributed computing and can easily handle massive datasets across diverse formats.
Snowflake vs Databricks: Key Differences
Databricks has become more focused on sophisticated analytics and difficult data processing jobs, which frequently involve data science or machine learning. In contrast, Snowflake is designed for storing and analyzing structured data, with a heavy emphasis on simplicity of use and scalability in data warehousing.
Let’s cut through the weeds and dive itnno the major differences between these solutions.
Architecture Comparison
Databricks vs Snowflake are two cloud platforms, one known for performance and simplicity and the other for enterprise-level experience.
Snowflake
Snowflake employs a novel hybrid architecture that incorporates parts of shared disk and shared nothing architectures. The storage layer stores data in centralized cloud storage that is available to all computing nodes, similar to a shared drive. However, the computation layer uses independent Virtual Warehouses that process queries concurrently, similar to a shared nothing design.
Databricks
Databricks is a unified data analytics platform that offers a complete solution for data engineering, science, machine learning, and analysis. The Databricks architecture is designed to handle large data sets and is based on Apache Spark, a strong open-source processing engine.
Performance and Scalability
Excellent query performance and scalability are key needs for any data warehouse. Snowflake and Databricks use distinct designs to achieve optimal performance.
Performance
Snowflake is designed specifically for high-performance SQL analytics applications. Its columnar storage, clustering, caching, and optimizations deliver superior performance for concurrent searches over structured data. However, performance degrades with semi-structured data. Overall, Snowflake provides push-button analytics performance with minimal customization.
Databricks, on the other hand, is intended to provide low-latency performance across both batch and real-time workloads. Users have various options for customizing performance, including advanced indexing, caching, hash bucketing, query execution plan optimization, and more.
This high level of customization enables customers to configure and modify performance for structured, semi-structured, and unstructured data workloads. However, using these advanced tweaking capabilities requires some level of experience.
Scalability
Snowflake’s architecture is fundamentally scalable. It uses a shared disk and shared nothing architecture, with distinct storage and computing resources. This decoupled approach enables Snowflake to expand these resources independently as your data and query volumes fluctuate. Snowflake’s data warehouse can be readily scaled by adding extra storage nodes, allowing you to accommodate data volume expansion while maintaining query performance.
Databricks provides extensive customization and control while scaling clusters. Users can customize the node kinds, sizes, and amounts to optimize for their individual workload. This allows for greater flexibility in tailoring clusters as needed. However, there are practical constraints to growing due to existing infrastructure and expenses. Furthermore, optimizing node configurations requires some technical expertise when managing Databricks clusters.
Ecosystem and Integration
Snowflake and Databricks take different approaches to ecosystems and integration.
Snowflake
Snowflake has established a comprehensive ecosystem of technological alliances and integrations. It connects to popular business intelligence tools such as Tableau, Looker, and Power BI, allowing for easy viewing and dashboarding of Snowflake data. Snowflake also has pre-built and third-party connections for ingesting and analyzing data from popular SaaS apps.
Furthermore, Snowflake provides strong integrations with all major cloud platforms, including AWS, Azure, and GCP. This enables enterprises to efficiently host on their choice cloud infrastructure.
Databricks
Databricks’ platform for data engineering, machine learning, and analytics is built on top of the open source Apache Spark environment. It seamlessly interacts with popular BI tools such as Tableau, Looker, and Power BI while preserving Spark’s powerful data processing capabilities, allowing for quick data visualization.
Databricks includes a wide selection of connectors for ingesting data from various sources such as databases, data lakes, streaming sources, and SaaS applications. This is made possible by Spark’s networking frameworks and the vibrant open source environment that surrounds Spark. Databricks also connects seamlessly with AWS, Azure, and GCP services like as Snowflake.
The Databricks Lakehouse architecture extends data management skills such as data cataloging to data lakes, allowing for an open yet managed lakehouse environment. The Databricks marketplace expands its offerings with partner solutions for BI, data integration, monitoring, and more.
Security and Governance
Snowflake and Databricks both include powerful security and governance capabilities to ensure data safety and compliance.
Snowflake
Snowflake offers powerful security tools to protect data and ensure compliance. Snowflake has a multi-layered security architecture that includes network security, access control, and end-to-end encryption.
Snowflake automatically encrypts all data at rest with AES-256 and provides comprehensive governance capabilities with features such as column-level security, row-level access controls, object tagging, tag-based masking, data classification, object dependencies, and access history. These built-in controls assist you protect sensitive data, track usage, ease compliance, and provide visibility into user activity.
Databricks
Databricks takes data security very seriously, incorporating it into every tier of their Lakehouse Platform. Transparency fosters trust, and Databricks publicly shares information about how the platform is secured and operated. The platform undergoes extensive penetration testing, vulnerability management, and adheres to safe software development norms.
Databricks offers powerful data governance features for the lakehouse across various clouds via Unity Catalog and Delta Sharing. Unity Catalog is a centralized data catalog that provides fine-grained access control, auditing, provenance tracking, and discovery for data and AI assets. Delta Sharing enables secure data sharing between businesses and platforms.
Schema Evolution and Data Governance
Snowflake
The solution provides seamless schema evolution and built-in governance features that make managing structured data simple. Automatic schema evolution and data governance are integrated into the platform.
Databricks
With the introduction of Unity Catalog, Databricks has significantly enhanced its governance capabilities, adding fine-grained access control, data lineage tracking, and schema management. This helps Databricks users achieve similar levels of governance to Snowflake, making both platforms comparable in this regard.
Concurrency and Workload Isolation
Snowflake
Snowflake’s multi-cluster architecture allows for automatic, isolated scaling of workloads without affecting performance, making it easy to handle high-concurrency use cases.
Databricks
Databricks supports high-concurrency workloads, but it can require more manual tuning compared to Snowflake’s automatic scaling. However, Unity Catalog now offers better workload isolation and fine-grained access control, ensuring that concurrent queries and teams can work independently.
Data Lineage and Versioning
Snowflake
Snowflake has built-in data governance tools that offer time travel, allowing users to access historical data and track changes across tables.
Databricks
With Unity Catalog, Databricks now provides robust data lineage tracking, a feature previously missing from the platform. It allows users to audit and understand data flows across pipelines. In addition, lakeFS enhances Databricks’ data versioning capabilities, offering a Git-like version control system for data, making it easy to manage data changes over time, at a repository level.
Simplified Data Sharing and Collaboration
Snowflake
Snowflake’s Secure Data Sharing feature allows organizations to share live data with partners and teams securely and efficiently. This feature is seamless and requires no copying or movement of data.
Databricks
While Databricks doesn’t have a direct equivalent to Snowflake’s data-sharing feature, lakeFS can add branch-based collaboration, allowing teams to work on isolated versions of data without conflicts, which is useful in scenarios involving multiple teams or complex workflows.
Data Science, AI, and Machine Learning Capabilities
Finally, let’s explore the cutting-edge area of data science and machine learning by comparing Databricks to Snowflake. Both platforms have similarly significant capabilities in this area.
Snowflake
Snowflake is meant to store and analyze big datasets. While it doesn’t have native machine learning capabilities like Databricks, it does provide the infrastructure required for machine learning activities.
Snowflake enables the loading, cleansing, transformation, and querying of enormous amounts of structured and semi-structured data. This data can be used to train and deploy machine learning (ML) models using external tools. SQL queries can be used to extract, filter, aggregate, and transform data into features usable by machine learning algorithms.
Snowflake provides connections and tools for exploratory data analysis. It also supports Python, user-defined functions, stored procedures, external functions, and the Snowpark API for data pretreatment and transformation. The Snowpark API enables Python and custom user-defined functions to be run within Snowflake for feature engineering and data transformation before being exported to external machine learning platforms.
Databricks
Databricks provides an integrated platform for developing and implementing powerful end-to-end machine learning pipelines. It includes pre-installed distributed machine learning frameworks, packages, and tools that enable high-performance modeling on large datasets.
Databricks automates hyperparameter tuning, model selection, visualization, and interpretability using AutoML. Its feature store enables data engineers/teams to manage and exchange machine learning features, resulting in faster development.
MLflow, an open source platform for managing the entire machine learning lifecycle, is given to manage trials (to record and compare parameters and results), register models, and package ML models for deployment. Models produced using Databricks may be deployed directly for real-time inference using REST APIs and readily incorporated into a variety of applications.
Snowflake vs Databricks: Comparison By Use Case
Data Ingestion
To interact with data, it must first be ingested to the underlying system. For Snowflake, this usually entails performing a COPY INTO command to import the data into a database that Snowflake can then query. Snowflake also includes tools like Snowpipe, which allows you to automatically load data into Snowflake.
Most Snowflake clients will additionally use a third-party solution like as Fivetran, Stitch, or Airbyte to import data from various sources (application databases, external APIs, etc.) into Snowflake.
Databricks customers, on the other hand, interface directly with data stored in the cloud. However, controlled Volumes is similar to Snowflake tables in that Databricks administers the table for you.
Snowflake’s investments in Apache Iceberg will allow more customers to store their data directly in the cloud and interact with it, similar to the Databricks strategy.
Data Transformations
When your data is ingested to the cloud platform, you may wish to modify or enrich it in some way. Both platforms offer a range of options for achieving this.
Snowflake is a SQL-based data warehouse, so most users do data transformations in pure SQL utilizing tasks, stored procedures, or third-party transformation and orchestration tools such as dbt. All SQL workloads are executed in Snowflake’s virtual warehouses.
Databricks users take advantage of jobs, which allows them to submit a Spark job to a cluster of compute machines in their cloud. With Databrick’s recent advances in its serverless SQL warehousing offering, pure SQL data transformations utilizing tools like dbt are becoming increasingly widespread.
Analysis and Reporting
Both Databricks and Snowflake provide its users a variety of options for analysis and reporting. Snowflake lets you create lightweight dashboards directly in Snowsight, or you may use Streamlit to develop custom data apps.
Databricks is a very well-built dashboarding product that some businesses use instead of a third-party business intelligence platform. Databricks provides several more advanced ML features, such as controlled MLflow and Model Serving.
With the advent of Snowpark Container Services, I anticipate that many Snowflake users will be able to start hosting ML models directly in Snowflake.
Building Data Applications
A data application is a product or feature that is used to provide live data or insights to clients outside the company.
Because of its high-performance SQL data warehouse, many organizations (including SELECT) construct their applications directly on Snowflake and serve application queries directly from Snowflake virtual warehouses.
For Databricks, the key use case for “external data applications” is the model serving features they provide, while similar SQL query serving should soon be possible with the investments they are making in their data warehousing offerings.
Data Governance and Management
Both platforms have pre-built tools for governance and management.
Snowflake offers hundreds of metadata datasets available for free to all users via the Snowflake account use database. It includes a sophisticated cost management package that includes significant tools such as budgets and resource monitors.
Databricks’ Unity Catalog product provides a very comprehensive data catalog offering, allowing customers to manage and comprehend all of the data in their environment.
Snowflake vs Databricks Pricing
Both Databricks and Snowflake provide usage-based pricing, which means you pay for what you use.
Note that Databrick’s pricing consists of two kinds of charges:
- Databricks charges for overhead and platform.
- The underlying cloud expenses from AWS, Azure, and GCP from the servers that Databricks spins up in those accounts.
As with any usage-based cloud platform, expenses can quickly grow if not properly managed or monitored.
When making cost-related decisions or comparisons between platforms, keep in mind the entire costs of ownership from (a) the platform provider and (b) the persons completing the work.
Conclusion
Both Databricks and Snowflake have their unique strengths. Snowflake provides a user-friendly, SQL-first data warehouse that requires minimal management overhead and automatic scaling, making it ideal for business intelligence and analytics use cases.
On the other hand, Databricks offers a flexible, powerful platform for data engineering, machine learning, and advanced analytics, with an open-source ecosystem that fosters innovation.
Ultimately, the choice between Databricks and Snowflake depends on your organization’s needs.
