Cloud Database Management Systems
The cloud DBMS market is undergoing significant changes, including the adoption of generative AI, real-time processing and analysis, and new methods of interaction between DBMSs and other data management components. This research guides data and analytics leaders in making informed decisions about evaluating and selecting cloud DBMS solutions.
Market Definition
Gartner defines the market for cloud database management systems (DBMSs) as software products that store and manipulate data and are primarily delivered as platform as a service (PaaS) in the cloud. Cloud DBMSs may optionally be capable of running on-premises or in hybrid, multicloud or intercloud configurations. They can be used for transactional and/or analytical work. They typically persist data using a combination of proprietary and open components in a durable manner, enabling a full range of create, read, update and delete operations. They are used by application end users, designers, developers and operators of large database systems.
Cloud DBMSs provide a means for businesses to store and process data in support of business applications and processes. They support transactional and/or analytical processing by supplying the data to run the business and analyzing it to improve overall business benefits. They address the needs of the following use cases:
Online transaction processing (OLTP) transactions: Support a centralized transaction focus, with a fixed, stable schema, while delivering high speed; high volumes; concurrency controls; data insert/update; atomicity, consistency, isolation and durability (ACID) properties; transaction isolation; and security.
Lightweight transactions: Support very high volumes of simple transactions with high concurrency, low latency and potentially relaxed consistency. This use case covers processing of fast-moving events captured from the edge.
Application state management: Supports modern end-user experiences by managing session state at scale, providing rich user profiles and offering variable consistency mechanisms across the database. It supports variable and complex schemas across multiple applications and developer teams.
Enterprise data warehouse: Manages data from multiple sources in a highly structured schema to meet analytical demands. It provides predictable performance for both batch and interactive queries.
Lakehouse: Manages the variety and volume of data of variable structures across a wide range of analytical query workloads, ranging from traditional analytics to data science. Data may be physically distributed.
Event analytics: Manages data that is written at high frequency and volume. Queries are made in real time to both evaluate data against models and summarize events. The same data is also queried at later times for ad hoc investigation, discovery and model training. In all cases, data is mixed in structure and size. Predictable performance and availability are critical for both ingestion and querying.
Report 2025
Here is a summary of the vendors featured in the Gartner magic quadrant 2025 report.
For the full analysis and detailed insights, you can read the report
here
and view the magic quadrant graphic
here.
| Market Status | Market Vendor |
|---|---|
Leader |
Amazon Web Services |
Leader |
|
Leader |
Microsoft |
Leader |
Oracle |
Leader |
Databricks |
Leader |
Snowflake |
Leader |
MongoDB |
Leader |
IBM |
Leader |
Alibaba Cloud |
Visionary |
SAP |
Visionary |
Cloudera |
Visionary |
Teradata |
Niche Player |
Couchbase |
Niche Player |
SingleStore |
Niche Player |
EDB |
Niche Player |
Redis |
Niche Player |
Neo4j |
Niche Player |
Cockroach Labs |
Challenger |
InterSystems |
Challenger |
Huawei Cloud |
Report 2024
Here is a summary of the vendors featured in the Gartner magic quadrant 2024 report.
For the full analysis and detailed insights, you can read the report
here
and view the magic quadrant graphic
here.
| Market Status | Market Vendor |
|---|---|
Leader |
Amazon Web Services |
Leader |
|
Leader |
Oracle |
Leader |
Microsoft |
Leader |
Databricks |
Leader |
MongoDB |
Leader |
Snowflake |
Leader |
Alibaba Cloud |
Leader |
IBM |
Visionary |
SAP |
Visionary |
Teradata |
Visionary |
Cloudera |
Visionary |
Redis |
Visionary |
Neo4j |
Niche Player |
SingleStore |
Niche Player |
Couchbase |
Niche Player |
Cockroach Labs |
Niche Player |
EDB |
Challenger |
InterSystems |
Challenger |
Huawei Cloud |