Snowflake Vs. Databricks: The Ultimate Comparison

Excerpt: As the amount of data to be analyzed grows, organizations must find a way to entrap all of their data in one place. Data storage is now a requirement for digital transformation, cloud computing, and data science applications. However, data analytics and data management have become critical business functions. 

Table of contents:

  • Introduction
  • What exactly do we understand by Snowflake?
  • What exactly do we understand by Databricks?
  • Snowflake Characteristics:
  • Databricks Characteristics:
  • Key distinctions between Snowflake and Databricks:
  • Final thoughts


Snowflake and Databricks, two well-known cloud-based data platforms, are clear rulers in this field. Databricks and Snowflake also seem to be two data management and analytics platforms on the market that, among other things, consolidate data infrastructure, make collaboration easier, and automate data pipeline tasks.

For today’s business, a comparison of data platforms Snowflake and Databricks is critical. There has been a lot of debate about whether Snowflake or Databricks is the better-designed cloud analytics solution. However, because both solutions were designed to handle different tasks, they should not be compared on an apples-to-apples basis. Both models are distinct from one another, but they share many similarities.

Today we will provide the ultimate comparison between these cloud computing service leaders.

What exactly do we understand by Snowflake?

Snowflake is a centrally managed service that allows consumers to incorporate, load, analyze, and securely share their data with near-infinite virtualization of simultaneous workloads. Benoit Dageville, Thierry Cruanes, and Marcin Zucowski established Snowflake, a cloud-based data warehouse company, in 2012. It’s also recognized for its completely separate compute and storage facilities, which allow customers to access only the one copy of data they require while maintaining high performance.

It is now one of the most valuable companies in the cloud data warehousing industry, with a market capitalization of billions of dollars. Snowflake is a self-serve platform designed to support Business Intelligence use cases at its core. Users can use SQL to query data and generate reports and dashboards to help them make better business decisions. Such a relational database is intended for analytical instead of transactional work, and it exists to serve as a confederal repository for all of the data sets in question.

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What exactly do we understand by Databricks?

Databricks is just a cloud-based data platform based on Apache Spark that was founded in 2013 by the Apache Spark, Delta Lake, and MLflow creators. You can get a full Data Science workspace for Business Analysts, Data Scientists, and Data Engineers to work collaboratively with Databricks’ Machine Learning Runtime did manage ML Flow and Collaborative Notebooks. Its strong and united analytics platform enables a project’s team of Data Engineers, Data Analysts, Data Scientists, and Machine Learning Engineers to collaborate on the same project. By implementing data architectures such as Lambda Architecture and Delta Architecture, Data Engineers can create cutting-edge data pipelines.

Databricks are used by a variety of enterprise customers to run large-scale production operations across a wide range of use cases and businesses, including healthcare, media, and many others. Databricks is renowned for its proprietary Data Lake, which allows users to dump the whole of their data in any format while still generating insights.

Snowflake Characteristics:

Some of the advantages of using Snowflake as a Software as a Service (SaaS) solution are as follows:

  1. Improve Analytics Quality and Speed: By switching from overnight batch loads to real-time data streams, Snowflake makes it possible for you to embolden your Analytics Pipeline. By awarding secure, concurrent, and ruled access to your Data Warehouse across your organization, you can improve the quality of analytics at your company. This enables businesses to optimize resource distribution in order to maximize revenue while reducing costs and manual effort.
  2. User Experiences and Innovative Products Have Been Improved: You can best explain user behaviour and product usage with Snowflake in place. You can also use the full scope of data to ensure customer satisfaction, greatly enhance product offerings, and foster Data Science innovation.
  3. Improved Decision-Making Based on Data: Snowflake helps to break down data silos and give everyone in your organization access to actionable insights. This is a crucial first step toward bettering partner relationships, pricing optimization, operational cost reduction, sales effectiveness, and much more.
  4. Strong Security: Snowflake Data Lakes ensures quick incident response times. This allows you to get a full picture of an incident by combining large amounts of log data into a single location and quickly analyzing years of log data. You can use a secure Data Lake to store all conformance, and data security data can be stored in one place using a secure Data Lake. Together in a single Data Lake, semi-structured logs and organized enterprise data could now be merged. Snowflake lets you get your feet on the ground without needing to index your data first and then try to influence and transform it.
  5. Data Exchange with Customization: Snowflake enables you to create a Data Exchange that allows you to securely share live, governed data. It also encourages you to improve data relationships all over your business units, as well as with your partners and clients. This is accomplished by obtaining a 360-degree view of each customer, which provides information on key customer characteristics such as interests, work opportunities, and many others.

Databricks Characteristics:

  1. Delta Lake: Databricks is an open-source transaction-oriented storage layer intended for use throughout the data lifecycle. This layer can be used to add Data Performance and Reliability to an existing Data Lake.
  2. Spark Engine has been improved: You could indeed simply set up clusters and build a managed service Apache Spark environment using the flexibility and reliability of multiple Cloud service providers. Databricks enable you to configure, set up, and fine-tune clusters with no need to monitor them for maximum performance and reliability.
  3. Notebooks for Collaboration: You can instantly analyze and access your data, jointly build models, and explore and introduce various actionable insights if you have the right tools and language. You can use Databricks to code in any language you want, including Scala, R, SQL, and Python.
  4. Machine Learning: With the assistance of cutting-edge architectures like Tensorflow, Scikit-Learn, and Pytorch, Databricks provides one-click access to preconfigured Machine Learning environments. You could indeed share and track experiments, collaborate on model management, and replicate runs all from a single location.

Here are some key distinctions between Snowflake and Databricks:

  1. Data Ownership


The processing and storage layers in Snowflake have been separated. This means they can scale independently in the Cloud-based on your requirements. You will be able to save money as a result of this. As you can see, you’re only processing about half of the data you store. Likewise, both layers retain their ownership. To protect data and machine resources, Snowflake employs the Role-based Access Control (RBAC) method.


Databricks’ data processing and storage layers are fully decoupled, unlike Snowflake’s decoupled layers. Because Databricks’ primary goal is data application, users could really leave their data in any format, and Databricks would then process it quickly.

  1. Data Structure


Snowflake, unlike EDW 1.0 and similar to a Data Lake, enables you to save and upload both semi-structured and structured files before even organizing the data with an ETL tool prior to actually loading it into the EDW. The information is stored in database tables, which are logically organized as repositories of columns and rows utilizing micro-partitions and data clustering techniques. When you upload data to Snowflake, it is automatically transformed into its internal structured format.


Databricks, on the other hand, can work with any data type in its original format. Databricks can also be used as an ETL tool to structure unstructured data so that other tools, such as Snowflake, can work with it. As a result, in the Databricks vs. Snowflake debate, Databricks trumps Snowflake in terms of Data Structure. On these tables, the user could perhaps cache, filter, and run any Apache Spark DataFrames.

  1. Scalability


Both Databricks and Snowflake have a lot of scalabilities, but Snowflake makes scaling up and down a lot easier. Processing and storage layers scale independently in Snowflake. This allows for in-the-moment scaling without interfering with the queries. It also offers near-infinite throughput by isolating concurrent workloads on resources allocated.


Databricks auto-scales based on workload, and it can scale down if the platform is completely idle for an extended period of time. The system then eliminates idle workers from underutilized clusters.

  1. Security


Snowflake offers customer-specific keys, as well as cryptography at rest, role-based access control, and Virtual Private Snowflake. This key is managed automatically to protect customer data using AES-256 strong encryption. It also has Time Travel and Fail-safe features. Snowflake’s Time Travel features allow you to save the original state of your data before it is updated for a period of one day to 90 days.


Databricks offers protection via Delta Lake, which is similar to Snowflake’s Time Travel feature. Delta Lake’s additional transactional layer, in which primary data is collected data management on top of the data lake, also enables compliance with data laws. Databricks does not actually store any data because it runs on Spark and its object-level storage, enabling it to confront on-premises use cases.

  1. Architecture


The Snowflake database architecture is a mix of shared-disk and shared-nothing database architectures. It offers a cloud hosting solution based on ANSI SQL that differentiates computing and storage processing layers and uses a centralized data repository for reoccurred data accessible from any and all compute nodes inside the platform. Snowflake organizes and internally optimizes data into a compact columnar format that can be stored in the cloud using micro partitions. Database Storage, Query Processing, and Cloud Services are the three layers that make up its architecture. Snowflake manages file size, compression, structure, metadata, statistics, and other data objects, which could only be accessed via SQL queries automatically.


The architecture of Databricks is based on Spark and is built around single nodes that can still be implemented in the cloud. Snowflake is currently available on AWS, GCP, and Azure. A control plane and a data plane are used by databricks. The control plane includes Databricks’ AWS backend services, which include storing notebook instructions and workspace configurations as well as encrypting data at rest. The data is processed in the data plane. It also includes serverless computing, which allows users to create serverless SQL endpoints that are entirely managed by Databricks and enable instant computation. These resources are shared in a serverless data plane, allowing users to connect to external data sources and external data streaming sources to ingest data from outside their AWS accounts.

  1. Use Cases


Snowflake provides JDBC and ODBC drivers for third-party applications that are simple to integrate. It’s best known for its BI applications and for companies looking for a simple analysis platform that doesn’t require users to manage the software.


In the meantime, Databricks has released an open-source Delta Lake that adds a layer of reliability to their Data Lake. Customers can use Delta Lake to submit SQL queries with high performance. Databricks is known for its use-cases that prevent vendor lock-in, are better suited for machine learning workloads, and support tech giants due to its wide range and superior technology.

  1. Pricing

Snowflake offers four enterprise-level perspectives to its customers. Premium, Basic, Enterprise, and Professional are the four editions available. Databricks, on the other hand, has three business pricing options for its subscribers: Business Analytics workloads, Data Science work overload, and corporate plans.

Final thoughts:

In conclusion, Databricks is the winner for a technical audience. Snowflake is easy to use for both technical and non-technical users. Databricks has almost all of the data management features that Snowflake has, plus a lot more. Both Snowflake and Databricks are fantastic data platforms for analysis. Each has advantages and disadvantages. Usage patterns, data volumes, workloads, and data strategies all play a role in determining the best platform for your company. Of course, price plays a role in the decision-making process. Because Databricks allows users to manage their own storage, it can be significantly less expensive at times. However, this is not always the case. Snowflakes can sometimes be found for a lower price.

Author Bio

Meravath Raju is a Digital Marketer, and a passionate writer, who is working with MindMajix, a top global online training provider. He also holds in-depth knowledge of IT and demanding technologies such as Business Intelligence, Salesforce, Cybersecurity, Software Testing, QA, Data analytics, Project Management and ERP tools, etc.