Gartner recently released its Magic Quadrant for cloud data base management system (DBMS). This Magic Quadrant will help data and analytics leaders choose the right cloud DBMS in a complex and fast-evolving market.
The Cloud DBMS market is defined by Gartner as follows: vendors who supply provider-managed public or private cloud software systems that manage data stored in a cloud storage tier. These systems may cater to multiple data models and data types, including relational, nonrelational, geospatial, time series, and others.
20 leading Cloud DBMS market leaders as per Gartner
Alibaba Cloud is a Leader in the Gartner Magic Quadrant. Its DBMS offerings include PolarDB which is compatible with MySQL, PostgreSQL, and Oracle, and PolarDB-X for operational use cases. For analytical use cases, Alibaba Cloud offers AnalyticDB and MaxCompute. For nonrelational and real-time use cases, it provides Lindorm, Graph Database (GDB), and Tair. Alibaba Cloud has a wide range of DBMSs for operational, analytical, multimodal, and real-time use cases. Along with its cloud infrastructure, artificial intelligence (AI), and Software-as-a-Service (SaaS) solutions, Alibaba Cloud has helped industries including finance, retail, logistics, gaming, automobile, and others.
Amazon Web Services (AWS) is a Leader in the Gartner Magic Quadrant. It offers a range of database management services. Some services like Amazon Relational Database Service (RDS), Amazon Aurora, and Amazon DynamoDB are aimed at operational use cases. For analytics use cases, there are services like Amazon Redshift, Amazon Athena and Amazon EMR. AWS is the largest cloud service provider in the world by revenue and has an international presence and global client base across all major industries. Its main focus is on making sure that both transactional and analytical workloads run smoothly for all customers, regardless of size.
3. Cloudera
Cloudera is a Leader in the Gartner Magic Quadrant. The Cloudera Data Platform (CDP) is a software platform that helps manage data. It is available on-premises and as managed services in AWS, Azure, and Google Cloud Platform (GCP). CDP includes CDP Data Hub, CDP DataFlow, CDP Data Engineering, CDP Operational Database, CDP Data Warehouse, and CDP Machine Learning for operational and analytics use cases.
The Cloudera Shared Data Experience (SDX) offers hybrid, intercloud, and multicloud unified security, governance, and metadata management. Cloudera’s focus is on providing a platform that can manage and analyze data anywhere, whether it is on one cloud or multiple clouds.
Cockroach Labs, a Niche player in this Magic Quadrant, offers CockroachDB, a database management system that can be used in public and private clouds, as well as on-premises. It also offers two deployment options for DBaaS: CockroachDB dedicated (for single tenant) and CockroachDB serverless (for multiple tenants). Cockroach Labs is constantly improving its capabilities for cloud-based systems, such as consumption-based pricing and resource optimization. This makes it a good option to consider when choosing a transactional database for cloud-agnostic transactional databases in complicated infrastructure environments.
5. Couchbase
Couchbase is listed as a Niche Player in this Magic Quadrant. The Couchbase Capella multimodel DBMS is designed for high-performance nonrelational operational databases. It also supports relational database capabilities such as SQL, schema, and transactions. Additionally, it can be used for hybrid analytical use cases and is compatible with various cloud providers. Most of its operations are in North America, but it also has some presence in Europe and is growing in the Asia/Pacific region. It is used by major organizations in many different sectors. Developers like using Couchbase because it offers a flexible document-based approach. Even though this limits its overall market, it is one of the few competitors that focus on this sector.
6. Databricks
Databricks is a Leader in the Gartner Magic Quadrant and offers Databricks Lakehouse Platform on Microsoft Azure (Azure Databricks), AWS, Alibaba, and GCP. Databricks SQL provides a serverless data warehouse for data analysts for running SQL and BI applications at scale directly on the data lake.
Lakehouse Platform is a data storage system that includes open-source formats. The data lake can be used through Delta Lake, which adds metadata and structures to the underlying data to deliver some of the capabilities of a traditional data warehouse. Databricks focuses on analytical use cases, worldwide mainly in North America and Europe.
7. Google
Google, Leader in the Gartner Magic Quadrant, operates globally and addresses both transactional and analytics use cases. Google has customers worldwide, in a wide spread of industries and of all sizes. Google Cloud Platform supports many database platforms as a service (dbPaaS) products. Google recently added AlloyDB, which offers a PostgreSQL front-ended hyperscale cloud-native database. Google uses a serverless approach, which enables more flexible pricing for customers and positions Google well for AI-driven optimization.
8. IBM
A Leader in this Magic Quadrant, IBM provides a unified integration layer for containerized DBMS called Cloud Pak for Data. The IBM Cloud Database family also provides a variety of other managed data technologies, including PostgreSQL, MongoDB, Elasticsearch, Redis, RabbitMQ, DataStax, and EnterpriseDB. IBM operates globally across all industries and organization sizes. It addresses both operational and analytical use cases.
IBM is good at coming up with new technologies, particularly in the areas of database optimization, portability, and distributed access to data through data virtualization. IBM also invests in primary research into advanced technologies such as quantum computing.
9. InterSystems
InterSystems, a Visionary in this Magic Quadrant, provides a multimodel hybrid DBMS called InterSystems IRIS. It has a global presence in healthcare and other industries, such as financial services and supply chains. InterSystems supports both operational and analytical use cases.
InterSystems has enhanced its capabilities for in-database processing of machine learning models with its embedded Python and AutoML capabilities and support for Predictive Markup Modeling Language (PMML) for the exchange of models.
10. MarkLogic
MarkLogic is a Visionary in this Magic Quadrant. It helps people solve difficult data problems. The MarkLogic Data Hub service is available on AWS and Azure clouds, Openshift, GCP, Docker Hub, and Kubernetes. MarkLogic helps manage data and has a multimodel data platform and an integration hub. These both allow users to access data stored in different places through a universal index. This reduces the amount of movement needed to get to the data remotely.
11. Microsoft
Microsoft is a leading provider of cloud-based database management systems (DBMS). It is a Leader in this Magic Quadrant. Its offerings include Azure SQL Edge, on-premises SQL Server, containerized SQL for Linux and Kubernetes, virtual machines, and Azure Arc. Additionally, Microsoft offers SQL Server on Alibaba Cloud, AWS, Google Cloud Platform, and Oracle Cloud Infrastructure. The company’s operations are geographically diversified, with customers in a wide range of industries and deployment sizes worldwide.
12. MongoDB
MongoDB is a Leader in the Magic Quadrant and provides a document-based nonrelational database called MongoDB Atlas on AWS, Azure, and Google Cloud Platform. It also offers the MongoDB Enterprise Server for on-premises use. In addition, MongoDB provides MongoDB Charts, Atlas Data Federation, Atlas Search, Atlas Application Services, and Realm, a mobile object database. Its operations are global, and MongoDB is used across all industry segments and by enterprises of all sizes.
13. Neo4j
Neo4j is a Niche Player in this Magic Quadrant. It offers the AuraDB managed service which is available on AWS and GCP. It also offers the Neo4j graph database on-premises and for private clouds. It offers AuraDS “data science as a service” library for integrated AI/ML. Neo4j has customers all over the world in different industries, including financial services, transportation and warehousing, and professional, technical, and scientific services.
14. Oracle
Oracle is a Leader in this Magic Quadrant. The Oracle Autonomous Database, which includes the Autonomous Transaction Processing and Autonomous Data Warehouse services, can be found in the Oracle Cloud Infrastructure (OCI) and on the Oracle Exadata Cloud@Customer (ExaCC) private cloud. Oracle also offers a variety of services, including Autonomous JSON Database, Oracle Graph with Autonomous Database, Oracle MySQL Database Service, Oracle NoSQL Database Cloud Service, and a service that supports its rapid development tool APEX.
The Oracle Database Management System (DBMS) has always had a lot of features and capabilities. In the cloud, it has added even more features, like autonomous tuning and extended management capabilities. This reduces the amount of work needed to keep the system running smoothly.
15. Redis
Redis is a Challenger in this Magic Quadrant. It offers Redis Enterprise Cloud which is a commercial offering based on the popular open-source caching database Redis and is available on AWS, GCP, Azure, Alibaba Cloud, IBM, and on-premises deployment. It is a multimodel data platform with a focus on real-time use cases and is capable of transactions and augmented transaction processing.
16. SAP
SAP is a Leader in this Magic Quadrant. SAP’s products include SAP HANA Cloud, SAP Data Warehouse Cloud (DWC), SAP Adaptive Server Enterprise, SAP IQ, and SAP SQL Anywhere. SAP Products work for both operational and analytical DBMS use cases. SAP HANA Cloud is a service that manages databases and supports transactional and analytical workloads in one solution including multimodel support.
17. Snowflake
Snowflake is a Leader in this Magic Quadrant. The Snowflake Data Cloud helps companies with their analytics, data warehousing, and data lake requirements. Its operations are in different locations around the world, and it works with companies of all sizes. It works with many different types of businesses, including finance, healthcare, retail, telecommunications, and manufacturing.
The company is investing in its Snowpark feature to provide AI/ML support. Python support was recently added, and the company also announced its intention to also provide transactional capabilities.
18. Tencent Cloud
Tencent Cloud is a Niche Player in this Magic Quadrant. It provides TDSQL, which is an operational DBMS available on both Tencent Cloud and the private cloud. Tencent Big Data Suite (TBDS), Tencent’s analytical DBMS, is a cloud solution that focuses on data warehouses and data lakes. Tencent Cloud’s product portfolio includes cloud platforms, DBMS, AI, and analytics. Its DBMS can be used for a lot of different things by companies of all sizes. Most of Tencent Cloud’s DBMS customers are in China, but it also has customers in the Asia/Pacific region, Japan and Europe.
19. Teradata
Teradata is a leader in this Magic Quadrant. It provides services related to data analysis, storage, and retrieval. It operates globally and works with clients of all sizes, but especially large and complex organizations. Teradata’s clients come from many different industries, such as retail, manufacturing, telecommunications, healthcare, and financial services. Teradata offers distributed capability with its Teradata QueryGrid feature. It also provides vertical industry offerings such as the Teradata Industry Data Models (iDMs) for industries like finance, retail, telecommunications, manufacturing, and healthcare.
20. TigerGraph
TigerGraph is a Niche Player in this Magic Quadrant. It offers TigerGraph Cloud, a native graph DBMS solution, on AWS, Microsoft Azure, and Google Cloud Platform (GCP). TigerGraph Cloud is designed to cover both operational and analytical workloads.
TigerGraph has high-performance capability based on its distributed architecture and advanced massively parallel processing (MPP)-native graph engine. It is suitable for data-intensive graph use cases because of its innovation in fast data processing and strong scalability makes.
TigerGraph is present in many different areas around the world, including North America and Europe, as well as in the Asia/Pacific region which includes China, India, and ASEAN. TigerGraph provides industrial graph applications that can be used for retail, finance, and manufacturing purposes.
Source: Gartner
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