Snowflake vs. Databricks: A Comprehensive Guide

Balázs Aszódi
Balázs Aszódi
28 Jul 2023 · 21 min read

As more and more companies embrace the cloud, it is vital to find the right data platform that fits their needs. This can help unlock the full potential of organizational data and drive innovation within the organization. This guide aims to provide a comprehensive comparison between the two popular cloud-based data platforms, Snowflake vs. Databricks, to help you understand their concept and features and eventually make an informed decision. 

Quick Summary

Read the main points of our Snowflake vs. Databricks comparison in the table below, and scroll down to find out more details about each area.

AvailabilityAzure, AWS, and Google CloudAzure, AWS and Google Cloud
Data Platform ConceptSnowflake Data Cloud – enables managed data storage, processing, and analytics solutionsDatabricks Lakehouse – combines the ACID transactions and data governance of enterprise data warehouses with the flexibility and cost-efficiency of data lakes to enable business intelligence and machine learning on all data
Data processing & engineeringScalable and efficient handling of large volumes of data primarily focused on data warehousing and analytics for structured data using custom ETL processes via Snowpark, SnowpipeEfficiently managing vast amounts of data, both batch and streaming. Handles structured, semi-structured, and unstructured data with complex data processing tasks by leveraging Apache Spark via AutoLoader, Delta Live Tables, and Workflows
Data warehousingOffers a Cloud Data Warehouse solutions with many traditional features, leveraging it fully managed warehousesDatabricks SQL enterprise data warehousing built into the Lakehouse Platform. Providing a serverless data warehouse with the purpose-built for analytics and BI use cases.
Real-time analyticsNo native support – need to integrate with real-time streaming technologies like Apache KafkaComprehensive support for real-time analytics by leveraging the streaming capabilities of Apache Spark
Machine learningNo native support – need to integrate with machine learning platforms like Databricks, Amazon Sagemaker, or DataikuBoth Databricks and Snowflake are following a consumption-based pricing model and pre-purchased capacity, which means a specific purchased commitment for a period of time. With commitment purchases, discounts are available from each vendor. Both vendors offer different pricing tiers based. Each pricing tier offers additional feature enhancements.
GovernanceDesigned for tables and external staged files, and structured/unstructured data with some limitationsUnity Catalog provides unified governance for all data and AI assets, including files, tables, external catalogs, machine learning models, and dashboards in your lakehouse on any cloud
SecurityBoth Snowflake and Databricks offer comprehensive security features to safeguard data and ensure adherence to regulatory requirements.
CollaborationPurpose-built for ML workloads, collaborative environment, and native support for major machine learning frameworks, such as TensorFlow, PyTorch, and Scikit-learnData sharing with features such as Marketplace and Data Exchange, comprehensive governance, and integrations with Jira, Slack
PricingBoth Databricks and Snowflake follow a consumption-based pricing model and pre-purchased capacity, which means a specific purchased commitment for a period of time. With commitment purchases, discounts are available from each vendor. Both vendors offer different pricing tiers. Each pricing tier offers additional feature enhancements.
Key use casesExcels in traditional SQL-based data warehousing functions and is a solid choice for standard data transformation and analysisStands out in end-to-end integration, real-time data processing, and advanced capabilities for data engineering, machine learning, and artificial intelligence workloads.
Snowflake vs. Databricks: the quick comparison table

Both Databricks and Snowflake have come a long way since their inception. Snowflake started as a cloud data warehouse with unlimited scalability, proceeding to address data engineering and ML needs with Snowpipe and Snowpark. Databricks followed the opposite path, featuring a robust solution for data engineering and ML from the start, later developing a solution for data warehousing with Databricks SQL, Unity Catalog, Photon, and Delta Live Tables. 

Both platforms continue to release new features, constantly evolving to improve and address the needs of their users – but they are pretty different. Let’s find out just how different.

What Is Snowflake?

Snowflake is a cloud-native, fully managed, highly scalable software-as-a-service (PaaS) data platform. It is designed to enable users to store and analyze large amounts of data using cloud-based infrastructure. 

Snowflake’s unique architecture separates storage and computes to a shared-nothing MPP (Massively Parallel Processing) query cluster, where each set of nodes stores a portion of data and shared-disk data storage. This unique approach allows for scaling independently, improving performance and cost-effectiveness. 

Snowflake supports a wide range of data-related tasks, data warehousing, data lakes, data engineering, data science, and data application development. It can handle structured, semi-structured, and unstructured data and provides a variety of features like auto optimization, data replication, and secure data sharing.

Snowflake’s high-level architecture

Snowflake Data Cloud

Snowflake enables data storage, processing, and analytic solutions that are faster, easier to use, and far more flexible than traditional offerings. The Snowflake data platform is not built on any existing database technology or “big data” software platforms such as Hadoop

Aiming to break down both internal and external data silos and democratize data access and insights, Snowflake supports six major data workloads and use cases: data lake, data warehouse, data engineering, data science, data applications, and data sharing and exchange. This is achieved via tools such as:

  • Snowpark allows developers to write code in their language of choice and execute it directly in Snowflake. With User Defined Functions (UDFS), you can execute native Java or Python code in Snowflake, including business logic and trained ML models. Leverage embedded Anaconda repository for effortless access to open-source libraries. With External Functions, it’s easier to access external API services such as geocoders, machine learning models, and other custom code. Stored Procedures operationalize and orchestrate your DataFrame operations and custom code to run on a desired schedule and at scale, all natively in Snowflake.
  • Snowsight is an easy-to-use web interface that provides an easy-to-use experience for performing critical Snowflake operations such as building and running queries, monitoring query performance, etc.
  • Snowpipe allows us to load data from files as soon as they’re available in a stage. This means you can load data in micro-batches, making it available to users within minutes.
  • Snowpipe Streaming enables low-latency streaming data pipelines to support writing data rows directly into Snowflake from business applications, IoT devices, or event sources such as Apache Kafka.
  • SnowSQL is the command line client for connecting to Snowflake to execute SQL queries and perform all DDL and DML operations, including loading data into and unloading data out of database tables.

Snowflake is powered by its managed hybrid shared-disk, shared-nothing database architecture, and virtual warehouses – which are clusters of compute resources in Snowflake.



What Is Databricks?

Databricks offers a comprehensive managed platform for constructing, deploying, sharing, and sustaining enterprise-grade data solutions at scale, harnessing the power of Databricks platform and industry-leading features like Photon.

Databricks is a unified, consistently governed data analytics and AI platform that is built on top of Apache Spark. It provides a robust, collaborative, and scalable cloud-based platform-as-a-service (PaaS) environment to process, transform, and make available huge amounts of data to multiple user personas for many use cases – including data streaming, data warehousing, data engineering, business intelligence, data science, and machine learning. 

An end-to-end product for your data management needs from storage to analysis, Databricks integrates with various data sources and provides built-in tools for data processing, visualization, and machine learning. It allows for the analysis of structured, unstructured, and semi-structured data and works with batch or streaming processing. 
Since Databricks is built on top of open standards and provides open-source-based technologies such as Delta and MLflow, it does not suffer from vendor lock-in. Databricks introduced the concept of the data lakehouse to combine the best of the data lake and the data warehouse solutions. As a result, there is no need for two different platforms.

Databricks’ high-level architecture

The Databricks Lakehouse

The Databricks Lakehouse combines the ACID transactions and data governance of enterprise data warehouses with the flexibility and cost-efficiency of data lakes to enable business intelligence (BI) and machine learning (ML) on all data. 

Built on open source and open standards, Databricks Lakehouse keeps data in your massively scalable cloud object storage, allowing you to use it however and wherever you want. This unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science, and machine learning. At its core is the Delta Lake – a single, open storage layer. It provides reliability and world-record-setting performance directly on data in the data lake in your cloud storage. 

The Databricks Lakehouse is open, supports any downstream data use case, easily shares data, and allows you to build your modern data stack without restrictions and lock-ins.

A variety of tools power Databricks Lakehouse:

  • Delta Lake – an open format storage layer that delivers reliability, security, and performance on your data lake for both streaming and batch processing. By replacing data silos with a single home for structured, semi-structured, and unstructured data, Delta Lake is the foundation of a cost-effective, highly scalable lakehouse. A single source of truth for all of your data, including real-time streams, so you are always working on the most current data. Provides open and secure data sharing via Delta Sharing.
  • Unity Catalog – a unified governance solution for all data and AI assets, including files, tables, machine learning models, and dashboards in the lakehouse on any cloud platform. Provides centralized fine-grained auditing through the audit logs. Includes real-time data lineage across all workloads in SQL, Python, Scala, and R. It works with existing data catalogs, data storage systems, and governance solutions – which can be used to build a future-proof governance model without expensive migration costs.
  • Databricks Workflows – reliable, fully managed orchestration service for data, analytics and AI. Orchestrate any combination of notebooks, SQL, Spark, ML models, and dbt as a Jobs workflow, including calls to other systems. Build ETL pipelines that are automatically managed, including ingestion and lineage, using Delta Live Tables. Available on all Databricks’ clouds, giving you full flexibility and cloud independence.
  • Data Streaming – allows data teams to build streaming data workloads with the languages and tools they already know. Simplify development and operations by automating the production aspects associated with building and maintaining real-time data workloads. Spark Structured Streaming is the core technology that unlocks data streaming on the Databricks Lakehouse Platform, providing a unified API for batch and stream processing. 
  • Databricks SQL (DB SQL) –  a serverless data warehouse on the Databricks Lakehouse Platform that lets you run all your SQL and BI applications at scale with up to 12x better price/performance, a unified governance model, open formats and APIs, and your tools of choice. You can easily ingest data from cloud storage to enterprise applications such as Salesforce and Google Analytics. Manages dependencies, transforms data in place, and works seamlessly with leading BI tools. Empowers every analyst to collaboratively query, find, and share insights with built-in SQL editor, visualizations, and dashboards. 
  • Databricks ML – built on an open lakehouse architecture, empowers ML teams to prepare and process data, streamlines cross-team collaboration and standardizes the full ML lifecycle from experimentation to production. Managed MLflow automatically tracks experiments and logs parameters, metrics versioning off data and code, as well as model artifacts with each training run. Once trained models are registered, you can collaboratively manage them through their lifecycle from data to production and back. You can then deploy your models as REST API endpoints anywhere with enterprise-grade availability. Leverage a range of tools – Managed MLFlow and Model Registry, ML Runtime, Collaborative Notebooks, Feature Store, Auto ML, Model Monitoring, and Model Serving.
  • Photon – the next-gen engine on Databricks Lakehouse Platform provides up to 80% cost savings and up to 12x speedups, especially on CPU-heavy workloads. It supports diverse data operations, including ingestion, ETL, streaming, and analytics, directly on the data lake. Photon is compatible with Apache Spark APIs, requiring no code changes or lock-in. It. With a single set of APIs for batch and streaming workloads, Photon simplifies data processing. It seamlessly integrates with existing code in SQL, Python, R, Scala, and Java. 



Head-to-head comparison

Security, Compliance, Data Ownership – Snowflake vs. Databricks

Both Snowflake and Databricks offer comprehensive security features to safeguard data and ensure adherence to regulatory requirements. Snowflake provides encryption, role-based access control, and auditing functionalities, along with the option for enhanced network security through virtual private cloud peering. Similarly, Databricks delivers comparable security features, encompassing a cloud storage firewall, encryption capabilities, and seamless integration with Microsoft Entra ID (formerly Azure Active Directory) for robust authentication and authorization.

In terms of data ownership, Snowflake retains control over both storage and processing layers, utilizing proprietary storage. Access to this storage is subject to customer charges. Conversely, Databricks adopts a storage-layer agnostic approach, empowering users to store data in any location and format of their choice. This prioritizes open standards, grants users the flexibility to select their preferred processing engines, and facilitates seamless integration with third-party solutions.

Data Governance – Snowflake vs. Databricks

Data governance in Snowflake is designed for Snowflake tables and external staged files and structured and unstructured data with limitations. Snowflake provides the following data governance features:

  • Column-level security
  • Row-level security
  • Object tag-based masking policies
  • Data classification
  • Object dependencies
  • OAuth

On the other hand, Snowflake has some challenges with data governance:

  • Metadata scope limitations on ETL logs, data quality metrics, workflow errors, etc.
  • Governance for data outside of Snowflake. Requires third-party tool for centralized data governance
  • No native lineage (only with third-party tools)
  • Basic search and discovery, limited to tables, requires third-party tools to find all your objects.

Databricks data governance with Unity Catalog provides unified governance for all data and AI assets, including files, tables, machine learning models, and dashboards in your lakehouse on any cloud. It is a centralized data governance for all your data assets, coupled with a data governance framework and an extensive audit log of all actions performed on the data stored in a Databricks account. With Unity Catalog, Databricks provides the following features and capabilities in data governance:

  • Built-in search and discovery for all data, code, notebook, and workflow assets.
  • Data governance framework and an extensive audit log.
  • Automated and real-time data lineage down to column level to track sensitive data and ensure data quality. Includes workflows and notebook lineage as well.
  • Column and row-level security through dynamic views.
  • Fine-grained access control with custom privileges on different levels of the three-level namespace in the metastore. Access Control Lists (ACLs) control access to all the different data assets.
  • Governance for data outside of Databricks through ACLs.

Data Processing – Snowflake vs. Databricks

Snowflake is primarily focused on data warehousing and analytics, offering robust capabilities for data processing workloads. Its cloud-based architecture provides scalability and efficiency in handling large volumes of data. 

With Snowpipe, data can be automatically loaded from files as soon as they are available in a designated stage, while Snowpark allows users to create custom ETL processes and leverage non-SQL languages like Python, Java, and Scala.

  • ETL capabilities are limited to SQL-only pipelines that depend on Snowpipe.
  • Batch data ingestion is supported, requiring basic transformations through COPY INTO <table> operations.
  • Streaming data can only be handled by passing it through Snowpipe and landing it in a stage via a Kafka stream.
  • These limitations often force customers to turn to third-party orchestration tools for more complex ETL operations, resulting in additional and unnecessary costs.
  • Snowpark, for accessing data, cannot utilize leading libraries and functions from the open-source community and instead relies on the proprietary Snowpark DataFrame API.

Databricks excels in efficiently managing and processing vast amounts of data, both in batch and streaming scenarios, leveraging the power of Apache Spark. 

Organizations can effortlessly handle complex data processing tasks, including ETL, data cleansing, and data transformation, on a large scale, thanks to Databricks’ robust capabilities.

  • Provides end-to-end data pipelines, offering a comprehensive solution for managing data workflows.
  • The Auto Loader feature automatically detects and processes new files as they are added to cloud object storage, simplifying data ingestion.
  • Delta Live Tables in Databricks abstract the complexity of managing the ETL lifecycle by automating data dependencies, incorporating quality controls, and providing detailed visibility into pipeline operations.
  • Leverages Spark, Delta Lake, and Photon technologies to ensure efficient data processing for both ELT and ETL workflows.
  • With Delta Live Tables, Databricks can handle low-latency, high-throughput ETL workloads, enabling real-time data processing.
  • Supports both batch and streaming processing, leveraging the power of Spark for scalable and performant data transformations.

Data Engineering – Snowflake vs. Databricks

Snowflake offers a comprehensive managed platform for data warehousing and data engineering workloads. With its scalable architecture, Snowflake provides a robust solution for constructing data pipelines and effectively managing data workflows.

  • Uses the same data warehouses for ETL workloads.
  • Snowpark translates code into SQL for execution or uses Python and Java stored procedures/UDFs. However, it is accompanied by restrictions in terms of code, memory, and performance.
  • Data warehouses lack the ability to auto-scale individual nodes for long-running ETL jobs.
    • One feature that can overcome this deficiency is the query acceleration service, which depends on server availability and has fluctuating performance, making it problematic for critical ETL jobs and serverless tasks. However, serverless tasks primarily focus on resizing warehouses for future runs, and they often encounter a significant failure rate. Additionally, the inclusion of serverless tasks incurs a service fee.
  • Snowflake lacks built-in data quality and monitoring capabilities, relying on external tools to configure and monitor data quality.
  • Spot instances, which can help reduce the total cost of ownership (TCO), cannot be utilized in Snowflake as all compute resources are owned and locked in by the platform.

Databricks offers extensive support for data engineering workloads, encompassing the construction of data pipelines and the management of data workflows. With the power of Apache Spark, Databricks provides a user-friendly platform for developing scalable data engineering solutions.

  • Next-generation engine for ETL with an optimized runtime engine compatible with Apache Spark, delivering high performance and cost reduction for streaming and batch workloads.
  • Unified API with native support for Python, Scala, and ANSI SQL, allowing for streamlined development and deployment of data pipelines.
  • Intelligent and efficient serverless autoscaling optimizes cluster utilization and ensures end-to-end SLAs are met.
  • Automated secondary storage usage prevents out-of-memory failures, enabling the execution of extremely large workloads.
  • Supports fault-tolerant ETL and streaming pipelines that can run on large-scale clusters with resilience to failures.
  • Auto-scaling of pipelines minimizes the risk of failure, and automated retries can be configured at no additional cost for uninterrupted business operations.
  • Built-in data quality validation enables the setting of data quality rules, error-handling policies, and real-time analysis of data quality statistics.
  • Monitoring alerts can be set to ensure data quality meets acceptable thresholds.
  • Support for flexible spot instance configurations allows for efficient ETL computing with reduced total cost of ownership.

Data Warehousing – Snowflake vs. Databricks

Snowflake offers a cloud data warehouse with many traditional features, which is a comprehensive managed solution for data warehousing and analytics. Leveraging its cloud-based architecture, Snowflake provides a highly scalable and efficient platform that can store and analyze extensive amounts of data.

  • Horizontal scalability for BI workloads in the Enterprise edition allows for efficient scaling. Limited maximum concurrency of 8 queries before scaling, potentially leading to over-provisioning.
  • Provides basic SQL orchestration but lacks support for refreshing dashboards or triggering alerts, requiring the use of third-party tools.
  • No ability to query external data sources.
  • Offers basic built-in charging features for executing SQL queries but lacks built-in capabilities for dashboarding or sharing reports.

While Databricks can be utilized for data warehousing purposes, its primary focus lies in facilitating data processing and machine learning workloads. However, the Databricks Lakehouse platform combines data warehouse and data lake, providing an end-to-end data warehousing solution. With Databricks SQL, enterprise data warehousing is built into the Lakehouse Platform, providing a serverless data warehouse with the purpose-built for analytics and business intelligence (BI) use cases.

  • Automatic horizontal scaling is enabled on data warehouses for BI workloads, ensuring efficient processing and improved response times by intelligently scheduling short BI queries.
  • Integration with Databricks Workflows allows users to orchestrate SQL queries, dashboard refreshes, and SQL alerts, providing comprehensive control over data workflows within the Lakehouse architecture.
  • Secure connectivity to external data sources is supported, allowing for querying with views using query federation.
  • Databricks provides a powerful and native SQL editing platform with data visualization and dashboarding capabilities, facilitating the easy creation and sharing of reports across teams.

Real-time Analytics – Snowflake vs. Databricks

Snowflake does not natively offer built-in support for real-time analytics but it can seamlessly integrate with popular real-time streaming platforms such as Kafka or Kinesis. Leveraging these platforms alongside Snowflake, organizations can achieve real-time analytics capabilities and gain valuable insights from streaming data.

  • Data ingested through the streaming API cannot be processed and transformed within the same pipeline, requiring separate micro-batch workloads for data transformation.
  • Limited support for ingesting data from Kafka in real-time, requiring the use of a complex Java SDK and self-managed computing outside of Snowflake. 
  • No support for streaming data to external destinations.
  • No capability to run streaming data pipelines, which means real-time machine learning on data is not possible.
  • Python Dataframe framework limited to batch processing.
  • Compute in Snowflake cannot automatically scale based on the volume of streaming data.

Databricks offers comprehensive support for real-time analytics by leveraging the powerful streaming capabilities of Apache Spark. 

With Databricks, organizations can process and analyze streaming data in real-time, enabling them to make timely and data-driven decisions based on up-to-the-minute insights.

  • Provides a user-friendly environment for developing real-time streaming data pipelines using Scala, Python, or ANSI SQL, with advanced monitoring and observability features.
  • Streaming data can be ingested and transformed on the same compute, simplifying the pipeline, improving latency, and reducing storage and compute costs.
  • An open and flexible platform for streaming, it supports various messaging services and ingestion sources, including cloud storage, Apache Kafka, and managed message services.
  • Streaming data pipelines can be seamlessly integrated with serverless machine learning models deployed on serverless endpoints, with managed auto-scaling based on traffic.
  • Apache Spark, the underlying framework in Databricks, is widely adopted for streaming workloads and has proven its scalability and reliability in processing exabytes of data for mission-critical applications.
  • Offers enhanced autoscaling optimizations designed explicitly for streaming workloads. These optimizations allocate cluster resources based on workload volume, ensuring efficient utilization, reduced resource usage, and lower costs without compromising data processing latency.

Machine Learning – Snowflake vs. Databricks

Although Snowflake lacks native support for machine learning, it can be seamlessly integrated with popular machine learning platforms such as Databricks, Amazon SageMaker, or Dataiku. Snowflake’s robust data warehousing capabilities facilitate the storage and analysis of vast amounts of data, providing a solid foundation for training and deploying machine learning models.

Snowflake supports running non-SQL code through UDFs and Stored Procedures. However, it falls behind Databricks when it comes to ML capabilities due to these shortcomings:

  • Lacks high-performing ML development features such as distributed training, library flexibility, experiment tracking, and distributed hyperparameter optimization.
  • No capabilities for production-grade ML operations, including model tracking, versioning, sharing, endpoints, and monitoring.
  • Requires external tools for data analysis and visualization.
  • Complex process for running Python libraries, limited execution control within a Python sandbox.
  • Limited environment management, only supports Conda package libraries.
  • No native capabilities for managing aggregated ML features, requiring separate tooling.
  • Users need to self-manage open-source or third-party feature stores.
  • No native support for automated ML, relying on third-party connectors and platforms.
  • Limited machine learning capabilities on Snowpark, requiring model training within UDFs or Stored Procedures.
  • No support for distributed training, resulting in slow performance and over-provisioned warehouses.
  • No native visualization, tracing, debugging, versioning, governance, or sharing of models.
  • Limited support for ML batch serving, no support for real-time use cases.
  • No built-in monitoring for models deployed within Snowflake.
  • No built-in support for MLOps, limited automation options.
  • Unable to connect to external resources like remote MLflow tracking servers.
  • No environment separation, testing, or model asset management.
  • No support for real-time model serving, impacting application performance and scalability.

Databricks is purpose-built for machine learning workloads, offering a collaborative environment where data scientists can effectively develop and deploy their machine learning models. It comes with native support for popular machine learning frameworks, including TensorFlow, PyTorch, and Scikit-learn, providing data scientists with a seamless experience for building and training their models.

  • End-to-end ML platform for development and production.
  • Integrated notebook experience with support for ML libraries and frameworks.
  • Pandas code can be executed on single or distributed nodes using the Pandas API on Spark.
  • Native feature store for reusing aggregated ML features in batch and real-time.
  • No-code automated ML functionality for quick model generation and testing.
  • Open format models with accessible code for further exploration and development.
  • Model training on single and multi-node CPU and GPU with parallelism options and library dependencies.
  • Tracing, visualization, and versioning of model trials with a full audit of parameters and performance.
  • One-click deployment for real-time and batch, including A/B testing and multi-model endpoints.
  • Serverless endpoint deployment with auto-scaling based on traffic.
  • Real-time support for high query volumes.
  • Automatic lineage tracking for deployed models and monitoring of data and predictions.
  • Native orchestrator for automating ML training and scoring pipelines with advanced capabilities.
  • Model serving for easy integration of machine learning into existing applications.

Collaboration – Snowflake vs. Databricks

Snowflake provides a collaborative environment for enabling teamwork and accelerating data-driven decision-making with its collaborative environment, data sharing features such as Marketplace and Data Exchange, comprehensive data governance, and integrations with popular collaboration platforms like Slack and Jira.

  • Relies solely on an SQL-based interface for data warehousing (DW) and analytics workloads, posing challenges for building, deploying, and running ETL or machine learning (ML) workloads.
  • Users are responsible for managing authentication credentials when connecting to Snowflake from their integrated development environment (IDE).
  • Implementing database changes through a continuous integration and continuous deployment (CICD) pipeline involves a complex combination of database management tools and source control systems.
  • Data sharing within Snowflake is limited to other Snowflake customers, which restricts the ecosystem and can lead to vendor lock-in.
    • Sharing data across different clouds or regions requires resource-heavy data replication.
    • Data sharing is limited to data files, UDFs, and unstructured data through secure views, excluding assets like notebooks or data models.
  • The marketplace allows public searches for data and apps, but access is only for Snowflake customers.

Databricks offers a flexible, adaptable, collaborative workspace that enhances teamwork and collaboration for data processing and machine learning projects. Within this workspace, teams can leverage shared notebooks and version control capabilities to streamline workflows, foster knowledge sharing, and ensure efficient collaboration. By providing a centralized and collaborative environment, data sharing, and marketplace, Databricks empowers teams to collaborate, iterate, and deliver impactful data-driven solutions effectively.

  • Provides collaborative notebooks with support for multiple languages (R, Python, SQL, Scala) and features like autocomplete, built-in data visualizations, versioning, real-time coauthoring, commenting, variable tracking, and debugging.
  • Notebooks can be scheduled as production pipelines and executed within broader workflows, offering flexibility and automation.
  • Users can connect their VS Code IDE to the remote Databricks environment, allowing the execution of code in Python, R, Scala, and SQL through a native VS Code plugin.
  • Jobs can be programmatically launched, configured, and deployed, and projects can be packaged in a versioned manner using the open-source CLI extension, facilitating integration with CI/CD pipelines.
    • Databricks Terraform resources enable the inclusion of Databricks objects in cloud-agnostic CI/CD pipelines.
  • Data and assets can be shared across a vast ecosystem, including any cloud or region, customers, partners, and various tools and platforms like Excel, PowerBI, and Pandas.
    • Secure data sharing with centralized governance is facilitated with zero copy and zero compute overhead, simplifying the process through UI, API, or SQL.
    • In addition to data files, Databricks supports the sharing of notebooks, dashboards, and ML models.
  • The open marketplace provides access to data analytics and AI assets for both Databricks and non-Databricks users, enabling quick discovery, evaluation, and access to a wide range of resources.

Pricing – Snowflake vs. Databricks

Both platforms follow a pay-as-you-go, consumption-based pricing model and pre-purchased capacity – a specific purchased commitment for a period of time. With commitment purchases, discounts are available from each vendor. Each pricing tier offers additional feature enhancements.

Snowflake has a simple pricing model. You pay for compute, storage, and data transfer resources. Snowflake credits are used to pay for the consumption of resources on Snowflake.

  • Compute resources include Virtual Warehouse (loading data, executing queries, and performing other DML operations), Serverless (features like Search Optimization and Snowpipe), and Cloud Service compute (cloud services layer of the Snowflake architecture).
  • Storage resources include the monthly cost of storing data in Snowflake.
  • Data Transfer resources is a per-byte fee for transferring data from a Snowflake account into a different region on the same cloud platform or into a completely different one.

Databricks costs for computation as a so-called DBU (Databricks Unit) – a normalized unit of processing power. The number of DBUs a workload consumes is specified by processing metrics. It can get fairly complicated, as workloads require different computation and cluster types with different prices/DBUs. This can change between regions. Note that in Databricks, the customer can choose the cluster’s instance type, and different instances have different prices. Databricks doesn’t charge for storage costs currently, it goes directly to the cloud service provider.

Each workload has multiple computation types, with additional feature sets you can choose:

Snowflake vs. Databricks: Which One to Choose?

Comparing the two data platforms, it is evident that both platforms have their strengths and are suitable for different use cases and industries. Snowflake excels in traditional SQL-based data warehousing functions and is a solid choice for standard data transformation and analysis. On the other hand, Databricks stands out in ecosystem integration, real-time data processing, and advanced capabilities for data engineering, machine learning, and artificial intelligence workloads.

As businesses advance in their data maturity and require more sophisticated data processing and analytics, the Databricks Lakehouse Platform emerges as the preferred solution. By combining the best features of data warehouses and data lakes, Databricks provides a unified platform to handle all data, analytics, and AI use cases at scale.

Databricks Lakehouse Architecture offers a comprehensive and unified platform for securely accessing and processing data across various use cases. It provides powerful data analytics capabilities, including machine learning and AI, while simplifying data management and reducing costs. Databricks’ collaborative environment, support for multiple languages, and integration with popular tools make it a versatile choice for organizations with diverse data needs. 

As businesses advance in their data maturity and require more sophisticated data processing and analytics, the Databricks Lakehouse Platform emerges as the preferred solution. By combining the best features of data warehouses and data lakes, Databricks provides a unified platform to handle all data, analytics, and AI use cases at scale.

Ultimately, the choice between Snowflake and Databricks depends on the specific data strategy, usage patterns, data volumes, and workloads of each organization. However, Databricks has gained traction among clients for its advanced capabilities and support for raw unstructured data, making it an attractive option for organizations looking to harness the full potential of their data.