Databricks vs Snowflake

Different use cases when they shine best…

Databricks and Snowflake are two major cloud platforms for managing data, but each has different strong points based on how they’re built. Databricks uses what’s called a “lakehouse” design on top of Apache Spark technology, which allows organizations to do data processing, analysis, and machine learning all in one place. 

Snowflake is a cloud-first data warehouse that focuses on being easy to use, growing automatically as needed, and running SQL queries very quickly. 

Real-world examples demonstrate that Databricks usually works better for complicated projects with many different requirements, while Snowflake excels at standard business reporting and traditional data warehouse tasks.

A Comparative study

Databricks Outperforming Snowflake

Databricks’ strength lies in its ability to handle diverse data workloads and unstructured data. Its native support for machine learning and AI makes it the go-to platform for organizations with advanced analytics needs.

• Machine Learning and AI: A company building a real-time recommendation engine would likely choose Databricks. The platform provides a unified environment for data scientists to ingest raw, unstructured data (like user clicks and product images), transform it using Spark, and then train, track, and deploy machine learning models at scale using integrated tools like MLflow. Snowflake, while expanding its ML capabilities with Snowpark, often requires external tools for a comprehensive MLOps lifecycle, which can add complexity and cost.

• Complex Data Engineering (ETL/ELT): A large enterprise with a variety of data sources — including streaming data from IoT devices, log files, and semi-structured data from web APIs — would benefit from Databricks. The platform’s Spark-based engine is highly efficient at performing complex transformations on massive datasets, especially for batch and streaming data processing. Databricks’ Delta Lake ensures data reliability with ACID transactions, which is crucial for building robust data pipelines.

• Handling Unstructured and Semi-Structured Data: A media company needing to analyze video and audio files for content tagging and sentiment analysis would find Databricks better suited. Databricks can process these raw data types directly in its data lake, allowing data teams to use various languages (Python, R, Scala, SQL) and libraries to extract insights, whereas Snowflake’s primary focus remains on structured data, with more limited support for unstructured formats.

Snowflake Outperforming Databricks

Snowflake excels in environments where simplicity, concurrency, and fast SQL-based analytics are the top priorities. Its architecture, which separates compute and storage, makes it highly scalable and cost-effective for these specific use cases.

• Business Intelligence (BI) and Ad-Hoc Analytics: A retail company with a large sales database would use Snowflake for its BI needs. Business analysts can run fast, concurrent queries on structured sales data without performance degradation, even with hundreds of users. Snowflake’s automatic query optimization, caching, and simple “virtual warehouse” sizing make it easy for non-technical users to get quick insights. A case study with a company like Petco demonstrated that they were able to process data up to 50% faster with Snowflake, enabling data science teams to increase productivity.

• Data Warehousing for Structured Data: For an organization that primarily deals with structured, relational data and needs to consolidate it for reporting and dashboards, Snowflake is an ideal choice. It functions as a fully managed service, which means less administrative overhead. Its architecture is optimized for loading and querying massive volumes of well-structured data, providing consistent and predictable performance. A company like Pfizer used Snowflake to modernize its data platform, achieving a significant reduction in total cost of ownership and 4x faster data processing.

• Simple Data Sharing: A financial institution needing to share live, secure data with its partners without complex ETL processes would leverage Snowflake’s secure data sharing capabilities. Snowflake allows different accounts to access the same underlying data without copying or moving it, which is a powerful feature for collaboration and data marketplaces. Databricks can also share data via Delta Sharing, but Snowflake’s native data sharing is a core feature that is often cited as a key advantage for companies that rely on inter-organizational data exchange.

A compelling example of a company choosing Databricks for machine learning is Shell.

Shell — Case Study

Shell, a global energy and petrochemical company, has vast amounts of data from various sources, including oil rigs, sensors, and operational logs. They needed to move from a traditional, siloed approach to a unified platform that could handle the entire machine learning lifecycle, from data ingestion to model deployment and monitoring. Their existing environment was a mix of different tools, which led to data fragmentation and hindered collaboration between data engineers and data scientists. They needed a solution to:

• Ingest and process massive, diverse datasets: Including structured, semi-structured, and unstructured data from IoT sensors, geological surveys, and more.

• Enable collaboration: Allow data engineers to prepare data and data scientists to build models on the same platform.

• Streamline MLOps (Machine Learning Operations): Automate the process of training, tracking, deploying, and managing machine learning models at scale.

• Improve model performance: Build more accurate predictive models to optimize operations and predict equipment failure.

Why Databricks Was the Right Choice

Shell chose Databricks for its unified, end-to-end platform for data and AI. The key reasons were:

1. The Lakehouse Architecture: Databricks’ Delta Lake provides a reliable, open-source storage layer that brings data warehouse-like reliability (ACID transactions) to a data lake. This allowed Shell to store all their raw data in one central location while still enabling high-performance, concurrent access for data science workloads. This eliminated data silos and the need for separate data lakes and data warehouses.

2. Native MLOps Capabilities: Databricks is built on Apache Spark and integrates directly with MLflow, an open-source platform for managing the end-to-end machine learning lifecycle. This was a critical factor for Shell. MLflow allowed their teams to:

• Track experiments: Log parameters, metrics, and code versions for every model trained, ensuring reproducibility.

• Package models: Use the MLflow Model Registry to centralize, version, and manage models, making them ready for production.

• Deploy and serve models: Easily deploy models as REST API endpoints for real-time inference.

3. Unified Workspace for Collaboration: The Databricks notebooks and collaborative workspace allowed data engineers using SQL and data scientists using Python to work together on the same platform and data. This streamlined the workflow, from data preparation to model development, and significantly reduced the handoff time between teams.

4. Support for a Variety of ML Workloads: Databricks provides a runtime optimized for machine learning, with pre-installed libraries like TensorFlow, PyTorch, and scikit-learn. This flexibility allowed Shell’s data science teams to use their preferred tools and frameworks to build a wide range of models, from predictive maintenance to demand forecasting.

By using Databricks, Shell was able to consolidate its data and AI initiatives onto a single platform, leading to faster model development cycles and improved operational efficiency. The unified nature of the platform was the key differentiator that allowed them to scale their ML capabilities effectively.

Here are specific examples of companies that chose Snowflake over Databricks and why.

1. Petco: Case Study

Petco, a leading pet retail company, had a legacy on-premises data warehouse that was slow, expensive, and struggled to keep up with the volume of data from its stores, e-commerce site, and customer loyalty programs. The company needed to create a 360-degree view of its customers by consolidating all this data. The goal was to provide business analysts with a platform for quick, ad-hoc analysis to improve marketing, customer service, and store operations. The legacy system led to long query times and poor concurrency, meaning analysts often had to wait in line to get insights.

Why Snowflake Was the Better Choice:

Petco chose Snowflake primarily for its simplicity and superior performance for BI workloads.

• Massive Concurrency: Snowflake’s multi-cluster shared data architecture allows multiple virtual warehouses to access the same data simultaneously without impacting performance. This was crucial for Petco, as it enabled hundreds of business users and analysts to run complex reports and dashboards concurrently without contention. In contrast, Databricks’ Spark clusters, while powerful, can sometimes require more manual tuning to achieve the same level of concurrent BI performance, especially for a large number of users.

• Ease of Use: Snowflake’s SQL-based interface and fully managed, cloud-native architecture make it incredibly easy for data analysts to use. Petco’s data team didn’t have to spend time on cluster management, performance tuning, or infrastructure maintenance, which is often a requirement with a Databricks environment. This enabled them to focus on delivering business value faster.

• Faster Time to Insight: Petco was able to speed up data processing for marketing and sales data by up to 50%. This translated to a 20% increase in productivity for their analytics teams. For a business that runs on daily sales reports and real-time customer behavior analysis, this speed was a game-changer.

2. Pfizer: Data Democratization and Secure Data Sharing

The Challenge:

Pfizer, a global pharmaceutical and biotechnology corporation, faced a different challenge. Due to a history of mergers and acquisitions, their data was highly siloed across disparate systems, regions, and business units. This made it difficult to share data securely and efficiently, hindering collaboration on research and development, clinical trials, and supply chain management. They needed a single, compliant, and easy-to-use platform to break down these data silos.

Pfizer’s core requirement was data democratization and secure data sharing at a global scale.

• Secure Data Sharing: Snowflake’s unique and powerful secure data sharing feature was a major differentiator. It allows Pfizer’s various business units, and even external partners, to access live, governed data without ever copying or moving it. This streamlined collaboration and ensured everyone was working with a single source of truth, a key strategic goal for the company.

• Simplified Data Platform: Snowflake’s fully managed, zero-maintenance model allowed Pfizer’s data teams to focus on value-added projects instead of infrastructure maintenance. The platform’s ease of use and SQL interface meant they could open up data access to a wider range of users across the organization, accelerating decision-making and innovation.

• Cost Efficiency: By consolidating their data on Snowflake, Pfizer was able to achieve a 57% reduction in total cost of ownership compared to their legacy systems. Snowflake’s separated compute and storage architecture provided granular control over costs, allowing them to scale compute resources up or down on demand, ensuring they only paid for what they used.

In both of these cases, while Databricks could have theoretically handled the data, Snowflake’s native architecture and feature set were better suited for the specific business problems of BI-driven insights and secure, enterprise-wide data democratization. The ease of use, predictable performance for SQL queries, and unique data sharing capabilities were the key reasons these companies chose Snowflake.

Healthcare Domain — 

For organizations in the healthcare domain, which platform is a better choice between Snowflake and Databricks depends on the specific use cases. Both are robust and offer key security and compliance certifications, such as HIPAA and HITRUST, and will sign a Business Associate Agreement (BAA), which is essential for handling Protected Health Information (PHI). However, their architectural differences make them excel at different tasks.

Snowflake: The Better Choice for Analytics and Business Intelligence

Snowflake is generally the better choice for healthcare organizations whose primary needs are:

• Structured Data Analytics and Reporting: For processing and analyzing well-structured data like patient records (EHRs), claims data, and billing information. Snowflake’s architecture is optimized for high-performance SQL queries, making it ideal for business analysts and data teams who need to run reports and create dashboards.

• Data Democratization: Snowflake’s ease of use and simple, scalable design make it easier to get data into the hands of a broader range of users across the organization, from business analysts to administrators, without requiring deep technical expertise.

• Secure Data Sharing: Snowflake’s native, secure data sharing feature is a major advantage. It allows healthcare providers, payers, and partners to share live, governed data without copying or moving it, which is critical for collaboration while maintaining compliance.

Example: A hospital network needs to analyze claims data and patient demographics to identify trends, optimize billing cycles, and track key performance indicators (KPIs) for administration. Snowflake’s platform allows them to consolidate data from disparate systems into a single, clean source and run ad-hoc queries with high concurrency, empowering business analysts to make fast, data-driven decisions.

Databricks: The Better Choice for Machine Learning and Unstructured Data

Databricks, with its lakehouse architecture, is the superior choice for organizations focused on advanced analytics and AI, especially when dealing with unstructured data.

• Machine Learning and AI Workloads: Databricks is built on Apache Spark and provides a unified environment for data engineering, data science, and machine learning. This is critical for building complex models for predictive analytics, such as predicting patient readmission rates or diagnosing diseases from medical images. The integration with MLflow provides a complete MLOps lifecycle for tracking, deploying, and managing models.

• Handling Unstructured Data: Healthcare data is not just structured; it includes medical images (DICOM), clinical notes, and genomic sequences.Databricks can process these unstructured and semi-structured data types natively in the same platform, which is a major advantage for building AI models for tasks like tumor detection from MRIs or analyzing physician’s notes for insights.

• Complex Data Engineering (ETL/ELT): For organizations with a variety of data sources and formats, Databricks is better suited for building robust, scalable data pipelines. Its ability to handle large-scale data ingestion and complex transformations on raw data is a key differentiator.

Example: A life sciences company needs to analyze vast amounts of genomic data and unstructured clinical trial notes to accelerate drug discovery.They would choose Databricks to handle the massive data processing required for genomic analysis and to use its advanced machine learning capabilities to build models that can identify new drug targets from unstructured text and image data.

Game Changer — DML Operations with Delta format

How Databricks and Snowflake Handle DML on Delta Lake?

Databricks: The Databricks platform is built on top of Apache Spark, and its foundational storage layer is Delta Lake. Databricks provides native, full support for all DML operations on Delta tables. This means you can run standard SQL commands like UPDATE my_delta_table SET col = 'new_val' WHERE id = 123 or DELETE FROM my_delta_table WHERE status = 'old'. Databricks handles the underlying changes to the data files and updates the Delta transaction log, ensuring ACID compliance. This is a core part of the "lakehouse" architecture and a key reason why it is used for ETL and data engineering.

Snowflake: Snowflake’s architecture is different. When you interact with a Delta table from Snowflake, you are typically creating an external table that points to the data files and transaction log in your cloud storage. Because Snowflake is only reading the files and not managing the underlying data, these external tables are read-only. You can query and join the data, but you cannot modify it directly with DML commands. If you need to update or delete data in a Delta table, you would have to use a different tool (like Databricks or a Spark job) to perform the DML operation on the source data, and then Snowflake’s external table would be refreshed to reflect the changes. Snowflake has made advancements in its interoperability with Delta Lake, but the core limitation of read-only access for DML remains.

Snowflake’s DML Approach and Alternatives

Snowflake’s core design principle is to separate compute from storage and manage its own internal storage for optimal performance. This means DML is a core function for tables stored within Snowflake, but not for external data.

1. DML on Internal Tables: Snowflake fully supports standard DML operations (INSERT, UPDATE, DELETE, MERGE) on tables that have been ingested into its internal storage. Data is typically loaded into these tables from files in an external stage using the COPY INTO command. This is Snowflake's intended workflow for managing a relational data warehouse.

2. External Tables for Read-Only Access: As discussed, external tables in Snowflake are read-only. They are a way to query files directly from a data lake (like a Delta Lake), but they don’t allow for in-place modifications. This is by design to maintain a clear separation between the data in your lake and the data in your warehouse.

3. Alternative Mechanisms for DML on File Systems: For use cases that require DML-like behavior on files, Snowflake uses a “copy-and-replace” strategy. If you need to update a few records in a file, you would typically:

• Load the data from the external file into a temporary internal table in Snowflake.

• Perform your UPDATE or DELETE DML operations on that temporary table.

• Overwrite the original file on the file system with the modified data from the temporary table using the COPY INTO <location> command.

This is a multi-step process that is much more cumbersome and less efficient than the single UPDATE or DELETE command available in Databricks' Delta Lake.

Key Takeaways in Conclusion:

1. Databricks is the Champion of the “Lakehouse” for AI and Complex Data.

• Best For: Companies with a strong focus on advanced analytics, machine learning, and data engineering. Databricks’ unified platform is designed to handle the entire lifecycle of AI and data projects, from raw data ingestion to model deployment.

• Core Strengths:

• Native handling of all data types (structured, semi-structured, and unstructured).

• Seamless integration with a full MLOps lifecycle via MLflow.

• Powerful and efficient DML operations directly on files in a data lake (Delta Lake), which is crucial for complex data transformations and compliance.

• Weaknesses: Can be complex for a broad base of business users and may require more expertise to manage and optimize for high-concurrency BI queries.

 2. Snowflake is the Leader in “Data Warehousing as a Service” for BI and Analytics.

• Best For: Organizations that need a simple, scalable, and high-performance solution for traditional data warehousing, business intelligence, and ad-hoc reporting.

• Core Strengths:

• Massive concurrency for a large number of business analysts.

• Exceptional ease of use with a user-friendly, SQL-native interface and minimal administration.

• Unique and powerful secure data sharing capabilities.

• Weaknesses: Less suitable for complex machine learning pipelines, native processing of unstructured data, and is not designed for the same DML capabilities on external files as Databricks.

The Final Verdict:

The decision comes down to the primary pain point a company is trying to solve.

• If your organization’s goal is to empower business users with fast, concurrent access to clean, structured data for reporting and analysis, Snowflake is the clear choice.

• If your organization is building advanced analytics capabilities, such as predictive models on diverse datasets, or needs a unified platform for data engineers and data scientists, Databricks is the superior solution.

In many large enterprises, it’s common to see both platforms in use, with Databricks handling the data engineering and ML workloads in the data lake, and Snowflake serving as the high-performance data warehouse for business intelligence and reporting. The interoperability between the two platforms allows for this “best-of-breed” approach, where they can work together to meet all of an organization’s data needs.