Gartner Magic Quadrant for Cloud Database Management Systems: In-Depth Comparison of AWS, Snowflake and Databricks 

Gartner Magic Quadrant for Cloud Database Management Systems: In-Depth Comparison of AWS, Snowflake and Databricks 

By Marta Teneva

March 7, 2024

The cloud database management system (DBMS) landscape is rapidly evolving, with innovation and functionality expanding at an unprecedented pace.  

According to Gartner: 

  • By 2025, data ecosystems will be embraced by 55% of IT organizations, resulting in a 40% consolidation of the vendor landscape. This will likely lower costs but also limit the range of choices available. 
  • By 2027, the decision-makers for 75% of Database Management System (DBMS) purchases will be leaders in specific business domains, a notable increase from 55% in 2022. 
  • By 2027, Relational Database Management System (RDBMS) solutions will incorporate 80% of the functional capabilities found in their NoSQL counterparts, rising from 60% in 2022. 

As organizations seek to leverage data for competitive advantage, the choice of a cloud DBMS has become critical.  

The Gartner Magic Quadrant for Cloud Database Management Systems (DBMS) provides a valuable framework for evaluating these technologies.  

This article compares three leaders in the space: AWS, Snowflake, and Databricks, exploring their strengths, weaknesses, and unique offerings in detail. 

Table of Contents

AWS in Gartner Magic Quadrant for Cloud DBMS: A Comprehensive Analysis

Snowflake in Gartner Magic Quadrant for Cloud DBMS: A Detailed Overview

Databricks in Gartner Magic Quadrant for Cloud DBMS: An Analysis

Gartner Magic Quadrant for Cloud Database Management Systems FAQs

Comparing AWS, Snowflake, and Databricks: Final Thoughts on Gartner Magic Quadrant for Cloud DBMS

AWS in Gartner Magic Quadrant for Cloud DBMS: A Comprehensive Analysis 

Amazon Web Services (AWS) emerges as a leader in the Gartner Magic Quadrant for Cloud Database Management Systems, underscoring its robust suite of database services. AWS offers more than 15 purpose-built engines catering to operational, transactional, analytical, and streaming applications, showcasing unparalleled scalability, flexibility, and a broad spectrum of options for enterprises of all sizes. With a global footprint and a comprehensive ecosystem of partners and third-party tools, AWS stands out for its ability to deliver complete, secure, and compliant database solutions across diverse organizational needs. 

Infographic detailing the strengths and cautions of AWS. Strengths include its global market presence, diverse database engines, and integrated data ecosystem. Cautions mention the challenges of hybrid and multicloud flexibility, the complexity of service selection, and concerns over vendor lock-in.

AWS Strengths 

Market Presence: AWS’s global presence and extensive offerings, supported by a vibrant community of partners and services, ensure a comprehensive database solution landscape. 

Flexibility and Diversity: The platform’s array of purpose-built database engines supports a wide range of data models, enhancing user choice and application fit. 

Integrated Data Ecosystem: AWS is moving towards a more integrated solution set, exemplified by initiatives like Amazon DataZone, facilitating seamless service integration. 

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AWS Cautions 

Hybrid and Multicloud Flexibility: Despite offering connectors through services like Amazon Athena, AWS Glue, and Amazon AppFlow for integration across on-premises and other cloud environments, AWS users may still find themselves dependent on third-party solutions for comprehensive data management and orchestration in hybrid and multicloud scenarios. 

Service Selection Complexity: The sheer breadth of AWS’s service offerings, while beneficial for flexibility and interoperability, can also present challenges. Navigating and integrating the wide array of services demands considerable effort, and while AWS has made strides in simplifying this process, the complexity of managing its extensive cloud platform persists. 

Concerns Over Vendor Lock-in: Organizations considering AWS must carefully evaluate their long-term strategic needs against potential lock-in risks. While concerns about cloud vendor lock-in are often overstated, AWS’s extensive ecosystem and comprehensive service suite could lead to a certain level of dependency. However, AWS promotes open standards and formats to facilitate data portability, and the perceived disadvantages of switching cloud environments are frequently outweighed by the benefits of staying within AWS’s robust and complex ecosystem. 

Snowflake in Gartner Magic Quadrant for Cloud DBMS: A Detailed Overview 

Snowflake stands out as a leader, particularly for its innovative approach to data warehousing, data lakes, and AI/ML processing. Its platform facilitates data sharing within and across clouds, supporting a wide range of industries globally. Snowflake’s emphasis on ease of use and its integrated solution approach, coupled with a robust ecosystem, positions it as a highly attractive option for organizations prioritizing analytics and data sharing. 

Infographic showing Snowflake's strengths and cautions. Strengths include its user-friendly interface, increasing market popularity, and cohesive ecosystem. Cautions point out the challenges with cost predictability, the slow release of new features, and the absence of on-premises solutions.

Snowflake Strengths 

User-Friendly: Snowflake leads the market in user-friendliness, making its analytical systems exceptionally easy to implement and manage. This emphasis on simplicity has become a significant market trend, largely influenced by Snowflake’s initial approach, positioning it as a prominent beneficiary of this movement. 

Increasing Popularity: Snowflake’s market presence is rapidly expanding, with its revenue growth outstripping the overall market expansion. Gartner notes a high level of interest in Snowflake from potential clients. 

Cohesive and Comprehensive Ecosystem: Snowflake advocates for an integrated, user-friendly solution, bolstered by a solid framework for data sharing and a vibrant data marketplace. This is further enhanced by its adoption of Kubernetes and co-processor technologies. 

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Snowflake Cautions 

Cost Predictability: Despite improvements, Gartner notes that clients still find forecasting expenses with Snowflake challenging. Nonetheless, a niche market of financial operations (FinOps) solutions has emerged to assist with managing costs for Snowflake and other cloud services. Snowflake is working to bolster its offerings for better financial management. 

Feature Release Cadence: Snowflake is known for frequently introducing new features, which is generally positive. However, the transition of these features from private to public preview and then to production can seem slow. Users should seek clarity on the availability of new functionalities for production use. 

On-Premises Solution Absence: There remains a demand for on-premises solutions among certain sectors of the market, particularly those under strict regulatory requirements that necessitate on-premises data processing. Snowflake’s lack of an on-premises offering could be a limitation for organizations in such regulatory environments. 

Databricks in Gartner Magic Quadrant for Cloud DBMS: An Analysis 

Databricks is recognized for its Lakehouse Platform, offering a unique blend of data lake and data warehouse capabilities. With strong foundations in data science and AI, Databricks excels in analytical use cases, supported by innovations like Unity Catalog and Delta Live Tables. Its approach to data management, emphasizing open formats and structures, positions Databricks as a leader for organizations focused on data science and analytics at scale. 

Infographic presenting the strengths and cautions of Databricks. Strengths focus on its data science and AI capabilities, the Unity Catalog for managing data assets, and scalable data lake foundation. Cautions highlight its nascent relational capabilities, potentially overwhelming product breadth, and complex pricing models.

Databricks Strengths 

Data Science and AI: Databricks has deep roots in data science, aiming to revolutionize future analytics by incorporating Databricks-developed large language models. These models are designed to be trained on the customer’s data, enhancing other models and enabling natural language queries on the Lakehouse Platform by non-technical users. 

Unity Catalog: The platform’s comprehensive support for the data ecosystem is embodied in the Unity Catalog, which offers detailed access control and manages metadata and lineage for a wide array of data assets, including tables, files, notebooks, and machine learning models. It supports a variety of data sources through standard storage objects like Parquet and Delta Lake, extending to databases such as MySQL, Microsoft SQL Server, and Snowflake. 

Scalability: Built on a scalable data lake foundation, Databricks successfully delivers significant scalability for suitable tasks, with customer systems capable of processing from a few gigabytes to petabytes of data daily. 

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Databricks Cautions 

Relational Capabilities: The relational capabilities within the Databricks Lakehouse, though promising, are relatively nascent and lack the maturity seen in solutions from other market leaders, particularly for traditional relational workloads. 

Product Breadth: The wide range of capabilities offered by Databricks’ unified platform may overwhelm users, making it challenging to fully comprehend its extensive functionalities or identify the most appropriate components for specific use cases. 

Pricing Models: As Databricks expands its pricing flexibility, including a serverless option alongside its standard consumption-based model, users might find it complex to select and leverage the most cost-effective model for their needs. The serverless option is specifically available for SQL warehouses, data science, and AI model serving, but not for other types of operations. 

Gartner Magic Quadrant for Cloud Database Management Systems FAQs 

Comparing AWS, Snowflake, and Databricks: Final Thoughts on Gartner Magic Quadrant for Cloud DBMS 

Comparative summary table highlighting the offerings of AWS, Snowflake, and Databricks. AWS is praised for its comprehensive suite of services, global reach, and robust ecosystem, catering to various use cases. Snowflake is recognized for its ease of use, data sharing capabilities, and market presence, focusing on analytical use cases. Databricks is noted for its strong focus on data science and AI, offering a unified platform that combines data lakes and warehouses for advanced analytics.

When comparing AWS, Snowflake, and Databricks within the context of the Gartner Magic Quadrant for Cloud Database Management Systems, it’s clear that each provider brings distinct strengths to the table. 

AWS excels in offering a comprehensive suite of services with global reach, flexibility, and a robust ecosystem. Its wide array of database engines and models caters to a diverse set of use cases, making it a versatile choice for organizations with varied data management needs. 

Snowflake distinguishes itself with its ease of use, innovative data sharing capabilities, and strong market presence. Its focus on analytical use cases and the ability to scale effortlessly without the complexity of traditional data warehousing solutions make it an attractive option for businesses prioritizing analytics and data sharing. 

Databricks stands out for its strong focus on data science and AI, offering a unified platform that supports a wide range of data analytics and ML projects. Its lakehouse architecture combines the flexibility of data lakes with the management features of data warehouses, appealing to organizations looking for advanced analytics capabilities. 

Overall, the choice between AWS, Snowflake, and Databricks should be guided by your organization’s specific requirements, strategic goals, and the specific use cases you need to support. The Gartner Magic Quadrant for Cloud DBMS is a valuable resource for understanding the market landscape, but it should be complemented with thorough research and direct engagement with the vendors to find the best fit for your data management needs. If you’re wondering which DBMS to choose, our consultants can help you with expert advice and seamless integration.  

Contact us, call us at +1 888 564 1235 or fill out the form below to tell us about your project and unique DBMS requirements. 

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