Snowflake vs Databricks: The Ultimate Comparison

Data storage is the foundation of every digital transformation, cloud computing and data science application today. Any company embarking on their infrastructure data roadmap or continuing their digital transformation needs a secure, flexible and convenient place to store their data. Snowflake and Databricks are at the forefront of the race to provide cloud computing services, and despite being competitors, they differ in several ways. Analytics India Magazine has created the ultimate comparison that every business needs to decide on the best provider for their applications.

What is Snowflake?

Snowflake is a cloud computing based data warehouse company founded in 2012 by Benoit Dageville, Thierry Cruanes and Marcin Zucowski. It is a fully managed service that provides near-infinite scalability of concurrent workloads, enabling customers to integrate, load, analyze, and securely share their data. Snowflake’s popular offerings include Data Lakes, Data Engineering, Data Application Development, Data Science, and Secure Use of Shared Data. Moreover, it is known for its separate compute and storage facilities, allowing customers to access only the single necessary copy of the data with efficient performance.

Snowflake is known for its innovation in the data warehouse. This relational database is designed for analytical rather than transactional work and serves as a federated repository for all particular data sets. It is led by a businessman, CEO Frank Slootman.

What is Databricks?

Databricks is a cloud-based data platform powered by Apache Spark and found in 2013 by the original creators of Apache Spark, Delta Lake and MLflow. It has become a total solution for the entire analytics team instead of giant suppliers. Databricks’ unique offering includes Machine Learning Runtime, managed ML Flow, Collaborative Notebooks, Dataframes, and Spark SQL libraries. The unified analytics platform allows the team of data engineers, data analysts, data scientists, and machine learning engineers to work together on a project. The Data Engineers can build advanced data pipelines by realizing data architectures such as Lambda Architecture and Delta Architecture.

Databricks is known for its patented innovation, the Data Lake, where users can dump all their data in any format and still be able to use it to generate insights. The company, led by an academic himself, CEO Ali Ghodsi, is focused on technology and led by engineering.

Data Ownership

Snowflake has decoupled processing and storage layers that can scale independently in the cloud. In addition, ownership is retained for both tiers. Snowflake uses the Role-based Access Control (RBAC) method to secure access to data and machine resources.

Unlike the decoupled layers in Snowflake, the data processing and storage layers are completely decoupled in Databricks. Since its main purpose is data application, users can leave their data anywhere in any format, and Databricks will handle it efficiently.

data structure

Snowflake supports structured and unstructured data without the need for an ETL tool to organize it. The data is stored in database tables, logically structured as collections of columns and rows using micropartitions and data clustering methods. Snowflake automatically converts the data into the internal structured format when uploaded.

Databricks is compatible with all data types in their native format and even allows users to add structure to their unstructured data. The Databricks database is a collection of structured data tables. The user can cache, filter, and execute all Apache Spark dataframes on these tables.


Both Databricks and Snowflake offer strong scalability, but scaling up and down is easier with Snowflake. In Snowflake, the processing and storage layers scale independently of each other. This allows for in-time scaling without interfering with queries in the process. In addition, it provides near-infinite scalability by isolating concurrent workloads on dedicated resources.

Databricks automatically scales depending on workload where it can be scaled down when the platform is 100% idle long enough. It then removes inactive workers on underutilized clusters.


Snowflake offers individual client keys, including encryption at rest, role-based access control, and Virtual Private Snowflake. Management of this key to protect customer data is done automatically using AES-256 strong encryption. In addition, it offers Time Travel and Fail-safe. Snowflake’s Time Travel features to keep the original data state before updating, with a period from one day to 90 days.

Databricks provides protection through Delta Lake, which serves a similar function to Snowflake’s Time Travel. It also enables compliance with data laws, given Delta Lake’s additional transaction layer that provides structured data management on top of the data lake. This allows users to simplify the process and quickly find and delete personal information. In addition, Databricks offers separate client keys and RBAC for data clusters. Because Databricks runs on Spark and its object-level storage, the platform doesn’t actually store any data, allowing it to address on-premises use cases.


Snowflake’s architecture is a hybrid of traditional shared-disk and shared-nothing database architectures. It uses a central data store for persistent data accessible from all compute nodes in the platform and provides a serverless solution based on ANSI SQL that separates storage and compute processing layers. Snowflake’s architecture is based on parallel processing where some of the data is stored locally at each individual virtual warehouse. Snowflake uses micropartitions to organize and internally optimize data in a compressed column format that can be stored in cloud storage. The architecture consists of three layers; Database storage, query processing, and cloud services. Snowflake automatically manages various aspects such as file size, compression, structure, metadata, statistics and other data objects that are only accessible through SQL queries. Snowflake, a SaaS solution, manages the user request backend, infrastructure management, metadata, authentication, query parsing, access control and optimization. It runs on three major clouds, AWS, GCP and Azure.

Databricks architecture is built on Spark around a few nodes that can be deployed in the cloud. It currently runs on AWS, GCP and Azure like Snowflake. Databricks operate from a control plane and a data plane. The control plane includes the backend services in Databricks’ AWS account, storing notebook commands and workspace configurations, and encryption at rest. The data plane is where the data is processed. It further provides serverless computing for users to create serverless SQL endpoints fully managed by Databricks, enabling instant computing. These resources are shared in a serverless data plane and allow users to connect to external data sources to ingest data outside the AWS account and external data streaming sources.

Use cases

Both Databricks and Snowflake strongly support BI and SQL use cases.

Snowflake provides JDBC and ODBC drivers that integrate easily with third-party applications. It is best known for its use cases in BI and for companies opting for a simplistic platform for analytics as users do not need to manage the software.

Meanwhile, Databricks has introduced an open-source Delta Lake that acts as an additional layer of reliability on their Data Lake. Delta Lake allows customers to submit SQL queries with high-level performance. Databricks is widely known for their use cases that prevent minimal vendor lock-in, are better suited to ML workloads, and support tech giants for their versatility and superior technology.

Leave a Comment