Report:

Magic Quadrant for Cloud Database Management Systems

Top Vendor Products for Cloud Database Management Systems in 2023

• Alibaba Cloud
• Amazon Web Services
• Databricks
• Google
• Microsoft
• MongoDB
• Oracle
• Snowflake

Cloud Database Management Systems Vendor Product Comparisons 2023

• Alibaba Cloud - Leader
  

  Pros:

  • Leading cloud-native DBMS innovations: Alibaba Cloud has been generously investing in cloud-native DBMS capabilities such as more flexible resource elasticity, augmented FinOps and hybrid cloud capabilities. These continuous innovations have shifted the vendor from a cloud DBMS follower to a provider of original native cloud solutions.
  • Broad and differentiated vertical presence: Alibaba Cloud has delivered DBMS success stories in a wide range of industries including finance, public sector, retail, gaming and automotive. At the same time, its deep engagement in some forefront industrial applications (such as autopilot) makes it highly competitive in specific data-intensive use cases.
  • Ecosystem enhancement: Alibaba Cloud's recent enhanced partnership with independent software vendors (ISVs), such as MongoDB, ClickHouse and PingCAP, has demonstrated improvement in its cloud ecosystem. This provides more choice in third-party D&A solutions with less integration effort on Alibaba Cloud.

  Cons:

  • Less coherent product portfolio: Alibaba Cloud has made some improvements to unify its diverse D&A products including DBMS from different open-source engines. However, the market recognition of its D&A products as a coherent brand is still lagging compared to its global peers.
  • Organizational restructuring uncertainty: Alibaba Group has announced the decision to split into six business entities with respective business independence, including Alibaba Cloud. The transitioning spin-off process as well as potential IPO bring a certain extent of uncertainty to its business strategy for the next one to two years.
  • Geopolitical headwinds: Although clearly not something it can control, overall geopolitical tensions remain high, which impacts Alibaba Cloud's global business expansion, especially in North America and Europe. This usually manifests as data security, data sovereignty or other local digital compliance concerns.


• Amazon Web Services - Leader
  

  Pros:

  • Continued market presence leadership: AWS's global presence, extensive offerings and services, paired with the community of partners, third-party tools and services, deliver complete, compliant and secured database applications and solutions across a wide variety of organizations.
  • Flexible database engines and models: AWS provides purpose-built database engines to support diverse data models, including relational, key-value, document, in-memory, graph, time series, wide column and ledger databases. This flexibility, and interoperability between the databases, helps users choose the best-fit databases for building their applications.
  • Resources to integrate data ecosystem: AWS has the resources to move toward a more integrated set of solutions, building on the work started with Amazon DataZone, a common metadata framework and integration between services.

  Cons:

  • Limited offerings beyond AWS cloud: AWS offers data and query connectors through native services such as Amazon Athena, AWS Glue and Amazon AppFlow across on-premises and other clouds. However, AWS customers continue to rely upon third-party solutions for unified and complex data management and orchestration for data spanning across hybrid and multicloud.
  • Breadth of choice of services adds to complexity: AWS's breadth of choices and extensive services offerings provide flexibility and interoperability, as noted in the Strengths section above. However, it can be overwhelming for some as it requires a significant effort to integrate data across its broad range of service offerings. AWS has continued to progress in this area but hasn't fully mitigated the complexity of managing the overall cloud platform.
  • Alleviating lock-in concerns: Clients considering AWS must ensure alignment with specific long-term needs and constraints, particularly with leading cloud providers like AWS, as they offer comprehensive services for a complete end-to-end data ecosystem deployment that bring stickiness. This may create a degree of dependence and lock-in; however, the general myths about lock-in don't reflect the reality of current cloud environments. AWS adopts and supports open formats and standards for ease of portability. Also, the cost and complexity of switching to a different environment often outweighs the risks of remaining in the incumbent environment, however complex it may be.


• Cloudera - Visionary
  

  Pros:

  • Hybrid and multicloud with scale: Cloudera's value proposition is to deliver a scalable data platform in both hybrid and multicloud deployments. Cloudera is a major driver of and contributes a significant portion of its R&D to open-source projects to drive enterprise extensions. Cloudera customers have petabytes of data under management.
  • Portable deployment strategy: Cloudera's portable deployment strategy lets organizations run workloads across hybrid and multicloud environments without redevelopment, eliminating significant effort, potential errors and increased costs. It is primarily helpful for organizations where privacy, security and other regulations force them to refrain from moving or replacing their workloads. It provides the flexibility to run governed workloads between on-premises and clouds.
  • Flexible and frictionless billing: Cloudera's frictionless application of customer investment across any deployment model allows the free exchange of credits and subscriptions based on a common unit, regardless of whether it is purchased directly from Cloudera or through marketplaces.

  Cons:

  • Core offerings and complexity: CDP customers continue to have challenges in navigating the complexity of its core offerings with its breadth of capabilities. However, the integrated security, metadata and governance with applied observability with SDX experience simplifies the user experience and cloud, hybrid and intercloud deployments.
  • More competing choices available: All cloud service providers (CSPs) offer alternatives that compete with Cloudera's offerings, frequently in a delivery form factor that is more tightly integrated with the CSP ecosystem and easier to use. However, Cloudera's strong focus on multicloud and hybrid operations with ease of portability on workloads is an effective counterbalance to this competitive pressure that will require sustained effort to remain effective.
  • Operational use cases: Cloudera is well-known for its portfolio of analytics capabilities and not as well-known for its operational capabilities with Apache HBase and the SQL interface supported by Apache Phoenix. Cloudera is typically not used as a general-purpose operational DBMS. It is more prevalent in high-volume, lightweight transaction lookups and large-scale batch operations supporting operational intelligence paired with its shared unified SDX experience across analytical and operational applications.


• Cockroach Labs - Niche Player
  

  Pros:

  • Distributed transactions for mission-critical systems: CockroachDB is well-suited for organizations' core systems, which have extremely high requirements for availability, scale, data consistency, data residency and security. The perception that distributed transactional DBMS are not suitable for organizations' most mission-critical applications has changed, and CockroachDB is well-positioned to take advantage of this trend.
  • Full multicloud, hybrid cloud and intercloud operations: Cockroach Labs is continuously evolving its capabilities for hybrid environments, such as intercloud resource efficiency and intercloud data security. These efforts are enhancing its position as a cloud-agnostic transactional database in complex infrastructure environments.
  • Easier to operate: Cockroach Labs has enhanced its out-of-box features to more easily realize expected resilience, performance, correctness and elasticity benefits. The majority of feedback from Gartner clients regarding its configuration complexity has been positive this year.

  Cons:

  • Competition from other vendors: Elasticity and high availability are not unique benefits of distributed transactional DBMSs today. New cloud DBMS products offered by CSPs, independent DBMS vendors and traditional vendors are also innovating in these areas. Cockroach Labs needs to respond to this aggressive competition to remain competitive in this market.
  • Lack of vertical ecosystem: Vertical industry solutions built on CockroachDB are rare, despite efforts from the vendor to establish vertical industry partnerships. As a result, most successful deployments require custom-made approaches to solve industry challenges.
  • Dedicated operational DBMS use cases: Cockroach Labs continues to prioritize its innovation in online transaction processing (OLTP) use cases for relational data. A concentrated business strategy is good for a specialized technology vendor, but it also means additional integration efforts are needed for clients looking for a more comprehensive use of different data models or query patterns.


• Couchbase - Niche Player
  

  Pros:

  • Mobile capabilities: Couchbase has positioned itself as a leading player in the mobile and edge space, allowing for synchronization with edge data stores even if they are periodically not connected to a central service. This capability is available from server to server as well as between mobile devices running Android or iOS and servers. Couchbase is unique in its peer-to-peer sync offerings for this space.
  • Strong capabilities for user engagement: Couchbase Capella offers rich capabilities not typically offered in a single product that address large-scale customer interactions. The union of a strong cache, SQL++ query language, document, key value and search models in a single product, deployable at scale in the cloud and on mobile devices, is powerful and leveraged effectively by many enterprises. Couchbase remains one of the most popular database systems globally.
  • Distributed intercloud approach across multiple clouds: Capella offers sophisticated replication and high availability configurations that span multiple clouds, which make it a good choice for systems that need a high level of scalability and availability.

  Cons:

  • Market awareness: Couchbase's market awareness has improved, but there is opportunity to do better. Its new focus has yet to generate significant market awareness or capture the growth in usage captured by some of its other open-source peers, as indicated by the number of incoming inquiries to Gartner about the product.
  • Lack of ecosystem support: Couchbase does not have a full-fledged capability to access data nor provide data to engines outside of the Couchbase world, although they are on its roadmap. The ability to broadly interact with multiple engines across an ecosystem is driving increasing flexibility and efficiency in multiple use cases, most prominently analytics and AI. Other nonrelational products also have this limitation.
  • Competition from other multimodel databases: A selection of other databases now offer significant multimodel support including JSON document model support and in-memory, real-time features. This has increased the competition in this area.


• Databricks - Leader
  

  Pros:

  • Data science and AI: Databricks has strong roots in data science. Its vision for the future includes the ability to use Databricks-developed large language models (LLM) to train on a customer's own data and use the results of this training to customize other models as well as allow nontechnical users to query the Lakehouse Platform with natural language.
  • Unity Catalog: Databricks embraces data ecosystem support via its Unity Catalog, which provides fine-grained access control and metadata management and lineage for Databricks tables, files, notebooks, machine learning models and data in federated databases. Unity Catalog can access any data source that uses standard storage objects, like Parquet and Delta Lake, as well as other data sources such as MySQL, Microsoft SQL Server and Snowflake.
  • Scalability: With its platform built on a highly scalable data lake foundation, Databricks has provided its customers with an extreme level of scalability for appropriate tasks. Databricks customers have systems that can process petabytes of data a day or as little as a few gigabytes.

  Cons:

  • Less mature relational capabilities: The relational capabilities of the Databricks Lakehouse are still relatively new and not as well-established as other leaders in this area for traditional relational workloads.
  • Product breadth can complicate focus: Because Databricks provides such a broad set of capabilities in a single, unified platform, users may find it challenging to understand its full range of capabilities or to find the right components to use for a particular use case.
  • Pricing models: As Databricks increases its pricing flexibility with additional options, such as a serverless option to go along with its standard consumption-based model, discovering and using the most optimal model for a particular set of use cases can be challenging. The serverless option is available for SQL warehouses, data science, machine learning and AI model serving, but not for other operations.


• EDB - Niche Player
  

  Pros:

  • Leading vendor for PostgreSQL: EDB is a go-to vendor for PostgreSQL solutions, and interest in PostgreSQL and PostgreSQL-compatible databases is growing. While characterized as a niche vendor within the Magic Quadrant, this is relative to the overall size of the cloud DBMS market, which is very broad and deep in its features and its overall value. EDB specializes in providing fully supported and managed cloud solutions for the PostgreSQL databases, enabling many deployment choices and migration paths.
  • Support and community: EDB is well-regarded for the support that it provides. In addition, it is valued for the contributions it makes to PostgreSQL and the way that it balances open-source community involvement and the provision of products and services.
  • Ease of migration: EDB has successfully put in a great deal of effort to extend PostgreSQL with optional compatibility features for other DBMS, most notably Oracle. This makes migration to EDB easier by reducing the amount of code conversion needed to support a migration.

  Cons:

  • PostgreSQL-compatible competitors: Many customers are adopting PostgreSQL and PostgreSQL-compatible DBMSs, which opens up many more migration paths. Other vendors also offer PostgreSQL-compatible DBMS, which provides competition in this space for customers who want the migration options but not necessarily PostgreSQL itself.
  • Late to cloud: EDB was relatively late in providing a cloud-based offering and thus has had a shorter amount of time to grow this side of the business and establish its presence.
  • Suited to organizations with an open-source strategy: EDB is best suited to organizations that have and are comfortable with an open-source software strategy.


• Google - Leader
  

  Pros:

  • Openness: Google has redefined the lakehouse by breaking down the data silos with BigLake, unifying data warehouses, data lakes and multicloud data across other cloud service providers with integrated access within Google Cloud. This is enhanced by a strategy of open support for PostgreSQL across relational databases such as AlloyDB and a common storage layer that supports unified transactional and analytical workloads.
  • Unified data management and governance: Google Cloud unifies data management and governance use cases with its Dataplex offering. Dataplex's unified and centralized metadata storage management allows the governed access of metadata and data for multiple data management workloads across Google's proprietary and open-source tools.
  • Industry-leading cloud infrastructure: Google has established a reputation through its AI research and innovation, partnerships, contribution to open-source data technologies and differentiation in its cloud infrastructure. Google Cloud offers an industry-leading 99.999% uptime SLA for products including Cloud Spanner, Cloud Bigtable and Firestore.

  Cons:

  • Market perception: While Google Cloud continues to expand its capabilities through native services and through partner integrations, some still do not view GCP as a choice for complex enterprise-scale applications. This is no longer the case, and evaluators should ensure that they correctly assess Google Cloud against their actual requirements.
  • Focused range of services: Google Cloud has a narrower range of services than other CSPs and partners with third parties to provide integrated capabilities for specialized needs, such as Graph DBMS. GCP has a concise set of clearly defined products with little overlap compared to other vendors. For evaluators, this can be a benefit and should be judged on its merits.
  • Simplicity of pricing: Google Cloud offers a simple pricing and deployment scheme with serverless and consumption models. It also offers capacity-based pricing for those who prefer predictable costs. Google's focus on simplicity should not be seen as a lack in capability, but is often preferred.


• IBM - Visionary
  

  Pros:

  • Technical depth and expertise: IBM remains one of the most technically accomplished vendors in the world and is able to bring to bear significant lab research on fundamental research for innovation.
  • Industry knowledge: IBM operates in a wide variety of industries and in many cases can provide specialist offerings to assist in building business solutions using IBM technology. The business data models from IBM remain among the best and most comprehensive in the industry and can be used by multiple database technologies.
  • Portability: IBM Cloud Pak for Data is an innovative offering that provides a complete and portable platform that can be run in public and private clouds, on-premises and in virtualized environments.

  Cons:

  • Market awareness: IBM maintains a considerable presence in the overall DBMS market. Nevertheless, newer entrants, including large cloud-native service providers, are growing their cloud DBMS presence more rapidly and at above-market rates. While IBM strives to keep pace with the expansion, there is still room to improve and adjust to the growing market demands of the DBMS in the cloud.
  • Product choice and selection: IBM offers a wide variety of cloud DBMS systems — both its own and from third parties — and the diversity of database engines allows customers more choices. IBM must do more to help its customers understand how multiple database offerings can work to solve varying customer needs with the best fit and right product selection.
  • Regaining momentum: IBM data strategy is now moving much more clearly into alignment with the direction of the cloud DBMS market. However, it needs to do more to set the pace in regaining the mind share and momentum it deserves by expanding its market influence and direction in data management technology deployments.


• InterSystems - Challenger
  

  Pros:

  • Solutions: InterSystems is offering a fairly unique, modular approach to solutions. It offers frameworks and data models for healthcare, supply chain and financial asset management that all run on IRIS. Additionally, it offers cloud services such as supply-chain forecasting, health record exchange and others that work over those same frameworks. The combination allows for significant flexibility versus packaged applications while leveraging the value of prebuilt solutions.
  • Continuing and enhanced strength in healthcare: Gartner's client inquiries continue to show customer reliance on the support and functionality InterSystems offers for key healthcare concerns, including graph-based support for the Fast Healthcare Interoperability Resources (FHIR) standard API for sharing healthcare data. This includes providing these capabilities to other DBMS vendors. InterSystems has a growing recognition in financial services and supply chain.
  • Simpler development: InterSystems customers are leveraging embedded Python and SQL at an increasing rate, allowing development with less IRIS-specific skills and syntax. This flexibility has also been supported by significant increases in performance for IRIS.

  Cons:

  • Limited availability of skills: Customers may struggle to find personnel skilled with InterSystems IRIS and other InterSystems products. This continues to be a challenge that potential customers must consider. While Python and SQL support provides familiar access for many developers, the architecture of IRIS is unique and very different from alternatives.
  • Analytics considerations: While InterSystems has made improvements in analytics, including data lakehouse capabilities, Gartner clients still report a need to bring data outside of InterSystems for some analytic use cases. InterSystems has partnered with others for data lake and lakehouse capabilities, such as AWS HealthLake.
  • Learning curve, documentation and complexity: Reviewers on Gartner's Peer Insights platform report some issues around quality of documentation and initial support. Coupled with a relatively steep learning curve on a complex offering, customers report more effort is required to implement successfully. Once implemented, customers generally report a very high level of satisfaction.


• Microsoft - Leader
  

  Pros:

  • A trustworthy global partner: Microsoft's broad global presence with highly localized business operations all over the world makes it one of the limited trustworthy global DBMS partners in today's world with digital geopolitics in play. Its broad partnership with ISVs/service providers has also enhanced its leadership in the DBMS market for traditional industries like financial services, healthcare and manufacturing.
  • A more open and unified data ecosystem: Microsoft continues to enhance the compatibility and interoperability between its DBMSs and other Microsoft products such as Microsoft 365, Power BI and Purview. This enables a more consistent experience for its clients. At the same time, the "one lake" direction in Microsoft Fabric brings more openness to data in non-Microsoft systems, which has the potential to reduce its clients' vendor lock-in concerns.
  • Innovation in AI-augmented data management: Microsoft is one of the pioneers innovating AI-augmented data management capabilities in its DBMS and D&A solutions. Microsoft Fabric (in preview) will use generative-AI-based automation and other copilot-style technology to automate data management tasks from simple DBMS administration to complex data integration.

  Cons:

  • Pace of brand upgrading: In the past few years, Microsoft has announced many new data and analytics offerings, such as Azure Synapse Analytics (December 2020), Microsoft Intelligent Data Platform (May 2022), and Microsoft Fabric (May 2023), which include overlapping DBMS products. This frequent rebranding and evolution has resulted in concerns around roadmap and delivery for customers who have or are building on existing versions of Microsoft D&A solutions.
  • Performance and cost challenges: In feedback from Gartner clients, performance and cost challenges continue to be the most frequently voiced concerns. Less predictable cost and performance for scaled data consumption spanning its DBMS and data management products still have room to improve compared to its peers.
  • Deployment challenges: Feedback from Gartner inquiries and Peer Insights highlights the complexity of configuration and tool utilization to fully realize its claimed integrated ecosystem capabilities. Some clients who lack deep configuration knowledge are demanding more enhanced performance, security and cost control from its out-of-box solutions.


• MongoDB - Leader
  

  Pros:

  • Market presence and momentum: MongoDB continues to occupy a significant presence in the marketplace and grow at above-market rates. It has established itself as a standard for nonrelational databases in the application development stack. MongoDB has a strong following, and this engenders good availability of skills.
  • Hybrid, multicloud and intercloud deployment choice: MongoDB can be run on a wide variety of cloud platforms including AWS, Azure, GCP, IBM, Alibaba, and several geosovereign and industry-focused clouds, as well as on-premises in private clouds and using containerized and virtualized environments. This provides a lot of flexibility in placing development and production instances and the connections between them.
  • Expanded product vision: MongoDB has expanded its data platform with new capabilities such as queryable encryption and support for time-series workloads. The MongoDB Atlas SQL Interface, Connectors and Drivers enable query, analysis and visualization of Atlas data in SQL-based BI tools, thus spanning the relational and nonrelational worlds. Roadmap items include vector search, stream processing and additional edge capabilities.

  Cons:

  • The document model is different: MongoDB adopts the nonrelational JSON document approach to data storage and representation; it eliminates the impedance mismatch between the database and programming languages. This can seem strange to developers used to the relational model. However, MongoDB provides tools and assists with training and intellectual capital dealing with a relational approach to document model mapping and migration of associated application code.
  • Increased competition: MongoDB has become a de facto standard for document database-oriented application development. That has caused other vendors in the market to offer MongoDB-compatible interfaces to their products with a view to allowing customers to be able to port MongoDB-based applications to their competitive products. This has increased competition, but evaluators should examine just how compatible and complete these interfaces are.
  • The rise of multimodel DBMS: It is becoming more common for DBMS to support multiple models, not just relational and not just document. MongoDB itself now offers SQL for analytics. As most major DBMS products now support document models, MongoDB will need to rely on articulating its particular advantages in implementation and approach rather than the advantages of the document model itself.


• Neo4j - Visionary
  

  Pros:

  • Graph market leadership: Neo4j has a broad community of users and loyal customers. Its Cypher and openCypher property graph query language is used by a dozen other vendors in their own graph offerings. While the graph DBMS market is still relatively small, Neo4j's broad presence spanning the open-source community, enterprise users, third-party developers and cloud service providers is unmatched by other independent vendors in the graph DBMS market.
  • Graph data science productivity: Neo4j provides a robust set of algorithms for analytics and data science over graph data, providing significant productivity and performance gains versus alternative approaches for analyzing graph data. It has robust connectors supporting integration with Spark as well as common ML platforms including Amazon SageMaker, Databricks and Google Cloud Vertex AI.
  • Strong customer satisfaction: Peer Insights reviewers of the on-premises offering consistently praise Neo4j for its integration and deployment services, and for its service and support. The graphical representation and speed to develop were cited as useful features.

  Cons:

  • Business model concerns: Gartner clients express dissatisfaction with AuraDB's pricing. AuraDB's self-service, pay-as-you-go options are restricted to a version with limited capabilities. Their more capable enterprise version is only available through traditional enterprise channels or as a one-year reservation through cloud marketplaces. AuraDS is only available for self-service in a single cloud, GCP. While AuraDB Enterprise is available in AWS, Azure and GCP, it is sold only through traditional enterprise channels. Further, Alibaba's hosted Neo4j offering is a different product than AuraDB. Customers need to be aware of the varying offerings and pricing approaches as they consider AuraDB.
  • Growing competitive landscape: Numerous graph DBMS competitors have entered the market, and increased graph analytics functionality is available within Spark. While Neo4j has partnered with all major CSPs, Google Cloud has chosen Neo4j as its main graph offering. Customers still prefer the native graph DBMS products when offered by a CSP before looking at independent software vendors. In addition, there are several open-source graph DBMS and analytics products gaining substantial user bases.
  • Lack of turnkey solution focus: While Neo4j has shown substantial success across some key use cases, such as fraud detection and recommendation engines, there is still significant work left for the customer to apply their technology and realize the potential benefits. Also, graph databases tend not to expand outside of the use cases for which they are especially suited.


• Oracle - Leader
  

  Pros:

  • Proven feature breadth and depth: The Oracle DBMS has one of the richest sets of technologies in the market, an advantage that continues in its cloud offering. Oracle's rich feature set has been proven over decades of production operation, including most of the world's largest OLTP, operational intelligence, data warehouse and JSON systems.
  • Pricing model: The Oracle Autonomous Database allows customers to get the price and predictability of a resource-based model with the automatic scalability of a consumption-based model, delivering excellent price and performance. On top of this flexible blended pricing model, Oracle offers a credit for on-premises support costs in relation to a customer's spend on the Oracle Database Cloud offerings, and there are no initial charges for data egress.
  • Hybrid and multicloud capabilities: Oracle has a number of database offerings that run in a customer's data center or any public cloud data center. These range from a standard client-managed model to Oracle Exadata Cloud@Customer, which implements a fully managed solution within a customer data center, to its strong partnership with Microsoft Azure. Additionally, MySQL HeatWave runs in Amazon Web Services, Microsoft Azure and Oracle Cloud Infrastructure.

  Cons:

  • Legacy perception of cost: Oracle continues to refine and modernize its pricing model for cloud and provides competitive pricing. However, Gartner clients and reviews on Gartner's Peer Insights platform regularly cite cost and expense as a primary negative factor. Oracle needs to improve marketing communications and account management to counter this legacy perception.
  • Not top of mind for cloud: Oracle was later to move to the cloud than other providers. The three largest cloud service providers by DBMS revenue and more recent entrants as well as others have captured much of the mind share in this market. This hurts Oracle since it results in its absence on default vendor shortlists. The recent Oracle Database@Azure announcement should help for those cases where Azure is the strategic CSP for a client.
  • Slower growth than some hyperscaler competitors: Oracle's growth has been respectable, slightly above overall cloud market growth, but some high-profile competitors have been growing faster. Oracle's ability to transition a very large on-premises customer base has the potential to accelerate the future growth of OCI.


• Redis - Visionary
  

  Pros:

  • Popularity: Redis is one of the most widely deployed databases worldwide. Offered by all cloud providers, it's used by millions of programmers, and is a key part of many large and small applications. This popularity is served by Redis, Inc. to drive enterprise extensions, modern data type and processing engine capabilities on top of open-source Redis, and enrich them with various frameworks, integrations and tools.
  • Real-time capability: Redis is well-known for its in-memory data processing capability, which is widely used to sustain modern real-time use cases in most industries. Amplified by its ease-of-use cloud offering and AI capabilities, Redis Cloud is a significant tool for building responsive applications. This strength is furthered by widespread support within application frameworks, enabling integration of Redis Cloud on top of other databases.
  • Popularity in AI/ML use cases: Redis Cloud has become popular for usage in AI/ML, driven by real-time capabilities for storing values as well as its early availability of vector search capabilities as compared to many other products.

  Cons:

  • Unchanged market recognition: Despite the innovations to cover broader workloads, the popularity of Redis is based on requirements for cache, real-time and in-memory. While Redis has expanded the functionality of Redis Enterprise beyond these core capabilities, this broader footprint has not been recognized by the market.
  • Limited vertical engagement: The vertical partnership for the enterprise use of Redis is still limited in the market. Despite the popularity of its open-source version, many clients are lacking vertical service providers or ISVs to help them build industrial applications.
  • Significant competition: Despite its popularity and high-standard SLA, many customers turn to other providers when deploying Redis in the cloud. These other vendors, most prominently the major CSPs, are developing increased efficiency and functionality on Redis or their own API-compatible products in customers' implementations.


• SAP - Visionary
  

  Pros:

  • Multicloud, intercloud and hybrid: SAP HANA-based database systems are available for deployment on a wide variety of cloud service providers including AWS, Azure, GCP and Alibaba as both a managed database service via SAP HANA Cloud and bring-your-own software model. They can also be deployed on-premises and in containerized or virtualized environments. The ability to work across clouds is valued by organizations with global supply chains.
  • Market presence: SAP has greatly increased its presence in the data management market through the adoption of SAP HANA Cloud as the underlying engine for SAP business applications. SAP has a very large customer base for its applications, and it is natural for SAP customers to adopt SAP HANA Cloud to run those applications.
  • SAP data ecosystem: The SAP Datasphere strategy is built upon and powered by SAP HANA Cloud. Thus, SAP is now much more open in its ability to import and export data between SAP and non-SAP environments via file exchange, replication and federation. SAP Datasphere is most applicable to organizations with a significant SAP technology presence.

  Cons:

  • Limited adoption outside of SAP ecosystem: SAP has been less successful in penetrating the general market because organizations that do not already use SAP are unlikely to adopt SAP for data management. However, this does not detract from the value of SAP data management for those organizations that are or are not SAP customers.
  • Marketing and sales messaging: SAP clients continue to tell Gartner that they are not being fully informed about SAP's data management offerings. Customers of SAP applications should ensure that they properly assess the SAP data management offerings, as some variants allow simpler solutions, especially for data integration.
  • Unfamiliarity with SAP HANA: SAP HANA has been in the market for over a decade, and it is a general-purpose ANSI SQL database. However, there are still several concepts that may be unfamiliar to developers — in particular, the use of the native multimodel engines, multitier data storage and processing architecture, and augmented transactional capabilities. Developers that are not familiar with these capabilities may not be taking full advantage of the platform. Adopters should familiarize themselves with these and other concepts with SAP HANA Cloud, which provides a simplified cloud-native environment to leverage these capabilities.


• Snowflake - Leader
  

  Pros:

  • Ease of use: Snowflake continues to lead in the ease with which its analytical systems can be implemented and operated. Ease of use is now a major theme in this marketplace, due largely to the initiative that Snowflake took when it entered the market. Snowflake will continue to benefit from this trend as one of its main exponents.
  • Growing market mind share: Interest in Snowflake continues to grow at a rapid pace, with revenue growth significantly outpacing the general growth in the market. Gartner receives many inquiries from clients that are considering adopting Snowflake.
  • Integrated solution with robust ecosystem: Snowflake promotes the philosophy of an easy-to-use integrated solution complemented by a robust data-sharing and data marketplace story. The Kubernetes and co-processor approach enhances this.

  Cons:

  • Predicting spend: While improvements have been made to the Snowflake DBMS, Gartner still gets reports that it can be challenging to predict spend. However, some clients are doing this successfully. There is now a market for financial operations (FinOps) vendors emerging to address this for Snowflake and other cloud vendors. Snowflake has partners in this space and is enhancing its product for improved support.
  • Progression of new features to production: Snowflake regularly introduces many new features. While this is a good thing, the pace of the features through private preview, public preview and into production can appear slow. Evaluators should be clear on when functionality will be ready to use in production.
  • Lack of an on-premises offering: A section of the market still requires some kind of on-premises presence to assist in the transition to cloud, and cannot move to cloud without it. This type of organization operates in a regulatory regime that requires on-premises for at least some of its processing.


• Teradata - Visionary
  

  Pros:

  • Optimized ecosystem: Teradata's QueryGrid implements access to data outside of Teradata efficiently by intelligently pushing down processing where appropriate, offloading cycles from the Teradata machine and reducing the amount of data that has to be returned to Teradata. Teradata is now extending this for optimal placement of workloads inside and outside of Teradata using its global planner. This capability provides Teradata with the ability to challenge the leaders who are shaping the market.
  • Depth and breadth of analytic capabilities: Teradata has long been the leader in providing the most advanced analytic capabilities to its customer base, including integrated AI/ML, robust mixed workload management and distributed access. Its customers have tended to be very sophisticated in their use of analytics, setting up a synergistic virtuous circle of product enhancement and deployment.
  • Workload management: Based on its architectural history, Teradata has the best workload management for analytic workloads in the market. This strength allows it to handle very demanding workloads with efficient use of resources as well as control costs in the cloud.

  Cons:

  • Lack of buyer awareness: Gartner inquiries show that Teradata is rarely at the top of a shortlist for cloud vendors, and the number of Gartner inquiries concerning this vendor has declined.
  • Lateness to the cloud: Teradata was relatively late in offering a full cloud version of its product. This meant that if its customers wanted to move to the cloud early in the cycle, they looked elsewhere. This continues to have an effect on Teradata's market awareness. Buyers need to ensure they are evaluating Teradata's most recent cloud-native offering to get a full understanding of its capabilities.
  • Product sophistication requires educated buyers: The strengths listed above are significant. But those strengths are not necessarily apparent in simplistic comparisons, so buyers need to take the time to understand the differentiation and the demands of enterprise workloads.


• Yugabyte - Niche Player
  

  Pros:

  • PostgreSQL compatibility: YugabyteDB is runtime compatible with PostgreSQL, including stored procedures, user-defined functions and triggers, which makes it compatible with many PostgreSQL tools. PostgreSQL compatibility is itself a trend in the cloud DBMS market.
  • Distributed database functionality: Distributed database management systems (DDBMS) are now accepted for mainstream applications. Not so long ago, DDBMS were seen as specialist and esoteric, but now they are considered proven and suitable for large mission-critical applications. YugabyteDB is well-established in this market.
  • Hybrid, multicloud and intercloud: Inherent in the distributed model is the ability to spread a single application database over multiple clouds and on-premises. This provides an opportunity to move workloads between these different locations, which can be advantageous for optimizing resources and for having procurement choice.

  Cons:

  • Size: Yugabyte is still a relatively small company in the cloud DBMS market, and buyers should check that they are happy with its support and roadmap.
  • Competition for large, high-concurrency workloads: The DDBMS approach is undoubtedly very effective for very large transactional workloads. However, improvements in nondistributed transactional systems, particularly from the hyperscalers, means that these competitors can cover many of these large opportunities too.
  • Ensure configuration for distributed systems: While YugabyteDB implements a proven distributed solution, it is still necessary for system designers to properly understand the principles of distributed systems, especially in the areas of performance, data sovereignty, high availability and recovery. This is not a concern, simply an area to attend to when using these types of systems.

Reference

• Gartner, Magic Quadrant for Cloud Database Management Systems, 18 December 2023, ID G00784704

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