A fast-growing startup hits a wall. Data pipelines break, dashboards lag, and the data science team waits hours to train models. Sounds familiar? Whether you’re running a lean team or managing millions in cloud spend, picking the right platform to manage and analyze data isn’t just a tech choice—it affects the entire business.
Databricks vs Snowflake is a decision many teams face, and it’s not always clear-cut. Databricks says it’s built for big data and AI, while Snowflake focuses on simplicity and fast SQL analytics. But which one is better for your needs?
According to a recent benchmark by GigaOm , Databricks outperformed Snowflake on TPC-DS queries by up to 2.8 times on price-performance. over 50% of the Fortune 500 use Databricks, while Databricks generated $1.6 billion in revenue with 50% growth just last year. Meanwhile, Snowflake continues to dominate the cloud data warehouse space.
If you’re still torn between the two, this post breaks down 7 critical differences that could help you avoid costly mistakes down the road, whether you’re scaling up or just starting out.
What is Databricks? Databricks is a cloud-based platform built to handle large-scale data processing and analytics . Developed by the creators of Apache Spark, it combines data engineering , machine learning, and analytics in a single environment.
Key Features of Databricks 1. Built for Big Data and AI Workloads Databricks can process large volumes of data quickly. It’s designed to run machine learning models and AI pipelines at scale, making it a strong choice for teams working with complex or real-time data.
2. Collaborative Notebooks Teams can work together using interactive notebooks that support multiple languages, including Python, SQL, R, and Scala. This makes collaboration between data engineers, analysts, and data scientists much easier and more efficient.
3. Lakehouse Architecture Databricks uses a Lakehouse approach, which combines the flexibility of data lakes with the performance and structure of data warehouses . This means users can store raw and structured data in one place and still run fast, reliable analytics.
Who Uses Databricks? Databricks is ideal for teams that manage high volumes of data and rely on AI or machine learning. It’s widely used in industries like tech, finance, healthcare, and anywhere real-time insights or advanced analytics are critical.
What is Snowflake? Snowflake is a cloud-based data platform built to store, process, and analyze large amounts of structured and semi-structured data. It runs entirely on public cloud services like AWS, Azure, and Google Cloud. Known for its simplicity and performance, Snowflake helps businesses handle data without managing complex infrastructure.
Key Features of Snowflake 1. Easy and Scalable Data Warehousing Snowflake separates storage and compute, which means users can scale up or down based on workload without affecting performance. This makes it simple to run heavy analytics jobs while keeping costs under control.
2. Fast SQL Performance One of Snowflake’s biggest strengths is its ability to run SQL queries quickly, even on large datasets. It’s optimized for business intelligence tools and is great for teams that need fast dashboards, reports, and insights.
3. Secure Data Sharing and Collaboration Snowflake allows seamless data sharing across teams and even with external partners, all without moving or copying data. Its Data Marketplace and secure sharing features make it easy to collaborate across organizations.
Who Uses Snowflake? Snowflake is ideal for analysts, business intelligence teams, and data engineers who focus on structured data and reporting. It’s widely used in retail, media, finance, and other industries that depend on fast, reliable analytics.
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Azure Databricks vs Snowflake: What Are the Major Differences? 1. Architecture Comparison The fundamental difference between these platforms lies in their architectural philosophy. Databricks follows the lakehouse architecture, which brings data management capabilities like data cataloging to data lakes , while Snowflake replaces legacy data warehouses and supports ELT processing.
Azure Databricks operates on a lakehouse model that combines the flexibility of data lakes with the structure of data warehouses. It uses the open-source Apache Spark framework to create data lakehouses, allowing you to store both structured and unstructured data in one location. This approach eliminates the need for separate systems and reduces data movement.
Snowflake , on the other hand, maintains a traditional data warehouse architecture but with cloud-native enhancements. Snowflake now supports data lakes by allowing data teams to work with a variety of data types, including semi-structured and unstructured data. However, it still requires you to load data into its proprietary format before analysis.
Key Architectural Differences:
Storage approach : Databricks stores data in open formats (Delta Lake), while Snowflake uses proprietary storageProcessing engine : Databricks uses Apache Spark for distributed processing, Snowflake uses its own SQL engineData organization : Databricks maintains raw data in place, Snowflake requires data ingestion and transformation2. Data Processing and Analytics Performance Performance differences between these platforms depend heavily on your workload type and data processing requirements. Databricks can process data up to 12X faster than competitors, particularly for complex data engineering tasks and machine learning workloads.
Azure Databricks excels in batch processing and real-time analytics thanks to its Spark-based architecture. It handles large-scale data transformations efficiently and supports streaming data processing natively. The platform automatically optimizes queries and can scale compute resources based on workload demands.
Snowflake focuses on SQL-based analytics and traditionally performs better for structured data queries and reporting workloads. Its architecture separates storage from compute, allowing independent scaling of each component. Due to its start in data warehousing, Snowflake has a much stronger and more fully featured SQL data warehousing product.
Performance Characteristics:
Batch processing : Databricks typically faster for large-scale ETL operationsReal-time analytics : Databricks native streaming capabilities vs Snowflake’s more limited optionsSQL queries : Snowflake optimized for traditional BI queries, especially on structured dataConcurrency : Snowflake handles high concurrent user loads better for standard reporting3. Machine Learning and AI Capabilities This is where the platforms show their most significant differences. Databricks excels in data engineering, real-time analytics, and machine learning , while Snowflake has traditionally focused on data warehousing with limited native ML capabilities.
Azure Databricks provides a comprehensive machine learning platform with MLflow for experiment tracking, model versioning, and deployment. It offers collaborative notebooks for Python, Scala, R, and SQL, making it ideal for data science teams. The platform supports popular ML frameworks like TensorFlow, PyTorch, and scikit-learn natively.
Snowflake has been expanding its ML capabilities but still lags behind Databricks. It offers Snowpark for Python-based data processing and basic ML model training , but lacks the comprehensive ML ecosystem that Databricks provides. Most organizations using Snowflake for ML still rely on external tools.
ML and AI Differences:
Native ML support : Databricks offers full ML lifecycle management, Snowflake provides basic ML functionsModel development : Databricks supports diverse ML frameworks, Snowflake limited to SnowparkDeployment options : Databricks offers multiple model serving options, Snowflake primarily supports batch scoringData science workflows4. SQL and Business Intelligence Support Both platforms support SQL, but with different strengths and limitations. Snowflake has a much stronger and more fully featured SQL data warehousing product, making it more familiar to traditional BI users and database administrators.
Snowflake uses standard SQL with some proprietary extensions, making it easy for teams familiar with traditional databases to adopt. It integrates seamlessly with popular BI tools like Tableau, Power BI , and Looker. The platform’s SQL engine is optimized for analytical queries and handles complex joins efficiently.
Azure Databricks supports SQL through Databricks SQL, which provides a SQL-first interface for analysts. However, it’s built on Spark SQL, which sometimes requires different syntax or approaches compared to traditional SQL. The platform offers Delta Live Tables for SQL-based ETL pipelines and connects to most BI tools.
SQL and BI Considerations:
SQL compatibility : Snowflake closer to ANSI SQL standards, Databricks uses Spark SQL syntaxBI tool integration : Both support major BI tools, but Snowflake offers more native optimizationsQuery performance : Snowflake optimized for analytical queries, Databricks better for complex data processingLearning curve : Snowflake easier for traditional SQL users, Databricks requires some Spark knowledge
5. Data Integration and ETL/ELT The approaches to data integration reflect each platform’s architectural philosophy. Databricks can reduce ETL costs by 9x compared to traditional approaches, while Snowflake focuses on simplified ELT processes.
Azure Databricks excels at complex ETL operations with its Spark-based processing engine. It can handle diverse data sources, perform complex transformations, and process both batch and streaming data. The platform supports over 300 data connectors and provides native integration with Azure services .
Snowflake simplifies data integration through its ELT approach, where data is loaded first and then transformed within the warehouse. It offers Snowpipe for continuous data ingestion and integrates with popular ETL tools. However, complex transformations might require external processing before loading.
Integration Capabilities:
Data sources : Databricks supports more diverse data formats and sources nativelyTransformation complexity : Databricks handles complex ETL better, Snowflake simpler for ELTReal-time processing : Databricks offers native streaming, Snowflake requires third-party toolsCloud integration 6. Cost and Pricing Analysis Pricing models differ significantly between platforms, making direct comparisons challenging. Databricks charges $0.75 per DBU for serverless preview options, while Snowflake uses a credit-based system that varies by compute and storage usage.
Azure Databrick s uses a Databricks Unit (DBU) pricing model where you pay per hour of compute usage. Costs vary based on workload type, with different rates for data engineering, machine learning , and SQL analytics. Storage costs are separate and based on your chosen Azure storage service.
Snowflake charges based on compute credits and storage consumption. Compute costs depend on warehouse size and usage time, while storage is charged monthly based on data volume. The platform offers automatic scaling but costs can escalate quickly with increased usage.
Cost Considerations:
Pricing predictability : Snowflake’s credit system can be more predictable for steady workloadsScaling costs : Databricks may be more cost-effective for variable workloads due to auto-scalingStorage expenses : Databricks leverages cheaper cloud storage , Snowflake includes storage in pricingHidden costs : Both platforms can have unexpected costs from inefficient query patterns or over-provisioning7. Governance, Security & Ecosystem Both platforms offer enterprise-grade security and governance, but their approach reflects their core focus. Databricks emphasizes flexibility and open-source integration, while Snowflake leans toward ease of use and built-in controls.
Azure Databricks provides strong support for fine-grained access control, data lineage , and audit logging through Unity Catalog. It offers strong integration with Azure Active Directory and supports HIPAA, SOC 2, GDPR, and other standards. Because it supports open file formats and tools, teams can build highly customized governance workflows .
Snowflake focuses on making data sharing and control easier out-of-the-box. It includes native role-based access control, automatic encryption, and data masking . Its secure data sharing capabilities are a major strength, especially in regulated industries. Snowflake also has deep support for compliance frameworks like PCI DSS, FedRAMP, and ISO.
Governance and Security Highlights :
Access control : Databricks offers detailed role and data-level access via Unity Catalog; Snowflake has simpler built-in RBAC.Data sharing : Snowflake leads with secure, zero-copy data sharing.Compliance : Both meet major standards, with Snowflake slightly ahead in built-in certifications.Integration : Databricks supports more open-source tools; Snowflake offers a cleaner, managed experience.Partner ecosystem : Both have strong ecosystems, but Databricks leans open-source, Snowflake leans productized simplicity.Snowflake vs Databricks: Comparison of Key Features Category Azure Databricks Snowflake 1. Architecture Lakehouse model combining data lakes and warehouses using Apache Spark Cloud-native data warehouse with support for semi-structured data Storage Format Stores data in open formats like Delta Lake Uses proprietary storage format Processing Engine Apache Spark-based distributed engine Custom-built SQL engine optimized for analytics 2. Data Processing & Analytics Optimized for ETL, real-time data, and ML-heavy workloads Excels in structured data queries and business reporting Batch Processing Better suited for large-scale ETL operations Efficient for standard batch SQL workloads Real-time Analytics Offers native support for streaming data Requires third-party tools for streaming SQL Query Handling Handles complex transformations, may need tuning Highly optimized for SQL queries with high concurrency 3. Machine Learning & AI Full ML lifecycle support with MLflow and open-source ML frameworks Basic ML via Snowpark; depends on external tools for full ML workflows Model Training Supports popular libraries like TensorFlow and PyTorch Limited to Snowpark and basic Python support Deployment Options Offers various model serving and real-time deployment tools Supports batch scoring but lacks robust native deployment 4. SQL & BI Support SQL through Databricks SQL; better for engineers and data scientists Strong ANSI SQL support; ideal for analysts and BI users BI Integration Connects to BI tools with some tuning Seamlessly integrates with Tableau, Power BI, and more Learning Curve Requires Spark knowledge for full flexibility Easier for SQL and BI users to adopt 5. Data Integration & ETL/ELT Excels at ETL with Spark; supports over 300 data connectors Focuses on ELT with tools like Snowpipe and Fivetran Transformation Complexity Handles complex workflows, batch and streaming Best for structured, post-load transformations Real-time Support Native support for streaming workloads Streaming needs external services 6. Cost & Pricing Pay-per-DBU model based on workload type and compute time Credit-based model with predictable compute and storage costs Cost Efficiency Cost-effective for variable and compute-heavy workloads Predictable pricing for steady, SQL-based workloads Storage Management Uses Azure cloud storage separately Includes storage in pricing but at a premium 7. Governance & Ecosystem Fine-grained control with Unity Catalog and strong Azure integration Simpler governance with native features and zero-copy data sharing Security & Compliance Complies with major standards; highly customizable workflows Built-in certifications and easier setup for compliance Partner Ecosystem Strong open-source focus with flexible integrations Tight product integration and curated partner ecosystem
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Databricks vs Snowflake: Ideal Use Case Scenarios When to Choose Databricks Machine Learning and Data Science Projects Native ML ecosystem : Built-in support for TensorFlow, PyTorch, and scikit-learn with MLflow for experiment trackingCollaborative notebooks : Python, R, Scala, and SQL notebooks for data scientists to explore and model dataModel lifecycle management : End-to-end ML workflows from data preparation to model deployment and monitoringFeature engineering : Advanced data transformation capabilities for creating ML features from raw dataAutoML capabilities : Automated machine learning tools to accelerate model development and testingComplex Data Engineering Pipelines Multi-format data handling : Process structured, semi-structured, and unstructured data in a single platformSpark-based processing : Distributed computing framework handles large-scale data transformations efficientlyDelta Lake integration : ACID transactions and schema evolution for reliable data pipelines Streaming and batch processing : Unified platform for both real-time and batch data processing workflowsData lineage trackingReal-time Analytics Requirements When to Choose Snowflake Traditional Data Warehousing Business Intelligence and Reporting BI tool integration : Optimized connectors for Tableau, Power BI, Looker, and other popular BI platformsConcurrent user support : Handles hundreds of simultaneous users running reports and dashboardsQuery performance : Optimized for typical BI workloads like aggregations, joins, and analytical functionsRole-based access : Granular security controls for different user types and reporting requirementsMaterialized views : Pre-computed results for faster dashboard loading and improved user experienceSQL-heavy Analytics Workloads ANSI SQL compliance : Standard SQL syntax with minimal learning curve for database professionalsWindow functions : Advanced SQL analytical functions for complex reporting and trend analysis Stored procedures : Support for complex business logic implementation within the databaseQuery optimization : Automatic query planning and optimization for analytical workloadsData clustering : Intelligent data organization for faster query performance on large datasetsExplore how Kanerika’s strategic partnership with Databricks is reshaping data intelligence , unlocking smarter solutions and driving innovation for businesses worldwide.
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Kanerika: Driving Business Growth with Smarter Data and AI Solutions Kanerika helps businesses make sense of their data using cutting-edge AI, machine learning, and strong data governance practices. With deep expertise in agentic AI and advanced AI/ML data analytics, we work with organizations to build smarter systems that adapt, learn, and drive decisions with precision.
We support a wide range of industries—manufacturing, retail, finance, and healthcare—in boosting productivity, reducing costs, and making better use of their resources. Whether it’s automating complex processes, improving supply chain visibility, or streamlining customer insights, Kanerika helps clients stay ahead.
Our partnership with Databricks strengthens our offerings by giving clients access to powerful data intelligence tools. Together, we help enterprises handle large data workloads, ensure data quality , and get faster, more actionable insights.
At Kanerika, we believe innovation starts with the right data. Our solutions are built not just to solve today’s problems but to prepare your business for what’s next.
FAQs Is Databricks better than Snowflake? The “Databricks vs. Snowflake” question boils down to your needs. Snowflake excels as a purely cloud-based data warehouse, prioritizing ease of use and scalability for analytical queries. Databricks offers a more versatile, open-source-based platform that’s strong for both data warehousing *and* data engineering, giving you greater control but requiring more technical expertise. Ultimately, the best choice depends on your existing infrastructure and the complexity of your data tasks.
Which is better Snowflake or Azure? The “better” choice between Snowflake and Azure depends entirely on your needs. Snowflake excels as a purely cloud-based data warehouse, offering scalability and ease of use. Azure provides a broader, more integrated platform encompassing data warehousing (Synapse Analytics) alongside many other services. Ultimately, it’s about whether you prioritize a specialized data warehouse or a comprehensive cloud ecosystem.
What are Azure Databricks and Snowflake? Azure Databricks is a managed Apache Spark service on Azure, simplifying big data processing and collaboration. It combines the power of Spark with cloud-scale infrastructure, making it easy to build and deploy data pipelines and analytics. Snowflake, on the other hand, is a cloud-based data warehouse offering scalable and highly performant data warehousing as a service, distinct from Databricks’ broader data processing focus. Essentially, Databricks is more about *processing* large datasets, while Snowflake excels at *querying* and *analyzing* them.
Who is Databricks' biggest competitor? There’s no single “biggest” competitor to Databricks, as the landscape is diverse. Companies like Snowflake excel in data warehousing, while AWS, Google Cloud, and Azure offer competing lakehouse platforms with varying strengths. The “biggest” competitor really depends on the specific customer needs and use case. Ultimately, the competition is more about best-fit than outright dominance.
Is Snowflake good for ETL? Snowflake excels at *parts* of ETL, particularly the “load” and “transform” stages. Its powerful querying and data warehousing capabilities make loading and manipulating large datasets efficient. However, for the “extract” stage, you’ll likely need external tools to pull data from diverse sources. Ultimately, Snowflake shines as part of a broader ETL strategy, not as a complete solution.
Is Databricks a PaaS or SaaS? Databricks blurs the lines between PaaS and SaaS. It’s fundamentally a SaaS offering – you subscribe and use their managed service. However, it provides a platform (PaaS) on which you build and deploy your own data applications and workflows. Think of it as a SaaS-delivered PaaS.
Can Snowflake and Databricks work together? Yes, Snowflake and Databricks are highly compatible. Databricks can act as a powerful data preparation and processing engine, feeding cleaned and transformed data directly into Snowflake for storage, querying, and advanced analytics. This combined approach leverages the strengths of both platforms for a robust data solution. Essentially, they complement each other rather than compete.
Why Databricks is expensive? Databricks’ cost stems from its powerful, unified platform combining compute, storage, and collaboration tools. You’re paying for highly scalable, managed infrastructure optimized for big data workloads, unlike self-managed solutions where you handle infrastructure costs. This ease of use and performance comes at a premium, but potentially saves on overall operational expenses compared to building and maintaining a similar setup yourself. Finally, pricing is usage-based, so costs depend directly on your data volume and compute needs.
Is Databricks Azure or AWS? Databricks isn’t tied exclusively to Azure or AWS. It’s a lakehouse platform that operates across multiple cloud providers, including Azure, AWS, and GCP. You choose your preferred cloud environment when setting up your Databricks workspace. Think of it as a software application, not a cloud provider itself.
Why Databricks is so popular? Databricks’ popularity stems from its seamless unification of data engineering, analytics, and machine learning on a single, scalable platform. It simplifies complex workflows, boosting team collaboration and efficiency significantly. This, coupled with its strong Apache Spark foundation and user-friendly interface, makes it a highly attractive solution for diverse data needs. Ultimately, it accelerates time-to-insight and reduces operational overhead.
Is Databricks owned by Microsoft? No, Databricks is not owned by Microsoft. While they have a strong partnership, Databricks is an independent company. Think of it as a collaborative relationship, not a parent-subsidiary one. They work together on various projects but maintain separate corporate structures.
Which language is best for Databricks? There’s no single “best” language for Databricks; the ideal choice depends on your project’s needs and your team’s expertise. Python is popular for its extensive data science libraries and ease of use, while Scala offers performance advantages for large-scale processing. Ultimately, the best language is the one you and your team are most productive with.
Is Snowflake better than Azure? Snowflake and Azure Synapse Analytics are both powerful cloud data platforms, but cater to different needs. Snowflake excels as a dedicated, scalable data warehouse, prioritizing ease of use and query performance. Azure Synapse offers broader integration within the Microsoft ecosystem and more versatile options, including data lake capabilities. The “better” choice hinges entirely on your specific data architecture and existing infrastructure.
Why Databricks is faster? Databricks’ speed stems from its unified architecture combining compute, storage, and analytics. This eliminates data movement bottlenecks common in traditional systems. Its optimized engine, built on Apache Spark, leverages cluster resources incredibly efficiently for parallel processing. Finally, built-in optimizations and automatic scaling contribute to significantly faster query execution.
