A multi-model database is a database management system that supports multiple data models within a single, unified backend. It allows users to store and query data in various formats, such as relational, document, graph, key-value, or columnar, without needing separate databases for each model. This flexibility enables developers to use the best data structure for different types of applications while maintaining consistency and efficiency. Multi-model databases typically provide a single query interface that can handle diverse data models, reducing complexity in data integration. They are designed to improve scalability, performance, and adaptability for a wide range of use cases. By consolidating multiple data models, these databases simplify architecture, lower maintenance costs, and enhance overall system interoperability.
-
1
SingleStore
SingleStore
Maximize insights with scalable, high-performance SQL database solutions.SingleStore, formerly known as MemSQL, is an advanced SQL database that boasts impressive scalability and distribution capabilities, making it adaptable to any environment. It is engineered to deliver outstanding performance for both transactional and analytical workloads using familiar relational structures. This database facilitates continuous data ingestion, which is essential for operational analytics that drive critical business functions. With the ability to process millions of events per second, SingleStore guarantees ACID compliance while enabling the concurrent examination of extensive datasets in various formats such as relational SQL, JSON, geospatial data, and full-text searches. It stands out for its exceptional performance in data ingestion at scale and features integrated batch loading alongside real-time data pipelines. Utilizing ANSI SQL, SingleStore provides swift query responses for both real-time and historical data, thus supporting ad hoc analysis via business intelligence applications. Moreover, it allows users to run machine learning algorithms for instant scoring and perform geoanalytic queries in real-time, significantly improving the decision-making process. Its adaptability and efficiency make it an ideal solution for organizations seeking to extract valuable insights from a wide range of data types, ultimately enhancing their strategic capabilities. Additionally, SingleStore's ability to seamlessly integrate with existing systems further amplifies its appeal for enterprises aiming to innovate and optimize their data handling. -
2
ChaosSearch
ChaosSearch
Transform your log analytics with cost-effective, scalable solutions.Log analytics doesn't need to be excessively costly. Numerous logging solutions depend on technologies such as Elasticsearch databases or Lucene indexes, which can drive up operational expenses significantly. ChaosSearch provides an innovative solution by rethinking the indexing approach, allowing us to pass on substantial savings to our customers. You can investigate our competitive pricing benefits using our comparison calculator. As a fully managed SaaS platform, ChaosSearch empowers users to focus on searching and analyzing data stored in AWS S3, eliminating the hassle of database maintenance and adjustments. By leveraging your existing AWS S3 infrastructure, we manage everything else for you. To grasp how our unique methodology and architecture can cater to the needs of modern data and analytics, make sure to check out this short video. ChaosSearch processes your data in its original state, enabling log, SQL, and machine learning analytics without requiring transformation, while also automatically identifying native schemas. This positions ChaosSearch as an excellent alternative to traditional Elasticsearch solutions. Moreover, the efficiency of our platform allows for seamless scalability of your analytics capabilities as your data requirements expand, ensuring that you are always equipped to handle growing workloads effectively. -
3
Hackolade
Hackolade
Empowering data modeling for NoSQL: innovate, visualize, succeed!Hackolade stands at the forefront of data modeling for NoSQL and multi-model databases, offering an extensive array of tools suitable for a wide range of NoSQL databases and APIs. As the sole data modeling solution for platforms such as MongoDB, Neo4j, Cassandra, ArangoDB, BigQuery, Couchbase, Cosmos DB, Databricks, DocumentDB, DynamoDB, Elasticsearch, EventBridge Schema Registry, Glue Data Catalog, HBase, Hive, Firebase/Firestore, JanusGraph, MariaDB, MarkLogic, MySQL, Oracle, PostgreSQL, Redshift, ScyllaDB, Snowflake, SQL Server, Synapse, TinkerPop, YugabyteDB, and others, Hackolade clearly dominates the market. Additionally, it extends its visual modeling capabilities to Avro, JSON Schema, Parquet, Protobuf, Swagger, and OpenAPI, and is continuously expanding its offerings for its physical data modeling engine. Designed with user-friendliness in mind, the software combines simplicity with powerful visualizations, facilitating the adoption of NoSQL technology for users. Its tools empower functional analysts, designers, architects, and DBAs working with NoSQL technology to achieve enhanced transparency and control, which ultimately leads to shorter development cycles, improved application quality, and diminished risks in execution throughout the organization. Furthermore, Hackolade's commitment to innovation ensures that users stay ahead in the rapidly evolving landscape of data management. -
4
Macrometa
Macrometa
"Empower your applications with global, real-time data solutions."We offer a globally distributed, real-time database paired with stream processing and computational capabilities tailored for event-driven applications, leveraging an extensive network of up to 175 edge data centers worldwide. Our platform is highly valued by developers and API creators as it effectively resolves the intricate issues associated with managing shared mutable state across numerous locations, ensuring both strong consistency and low latency. Macrometa enables you to effortlessly enhance your current infrastructure by relocating parts of your application or the entire system closer to your users, thereby significantly improving performance, enriching user experiences, and ensuring compliance with international data governance standards. As a serverless, streaming NoSQL database, Macrometa includes built-in pub/sub features, stream data processing, and a robust compute engine. Users can establish a stateful data infrastructure, develop stateful functions and containers optimized for long-term workloads, and manage real-time data streams with ease. While you concentrate on your coding projects, we take care of all operational tasks and orchestration, allowing you to innovate without limitations. Consequently, our platform not only streamlines development but also enhances resource utilization across global networks, fostering an environment where creativity thrives. This combination of capabilities positions Macrometa as a pivotal solution for modern application demands. -
5
CrateDB
CrateDB
Transform your data journey with rapid, scalable efficiency.An enterprise-grade database designed for handling time series, documents, and vectors. It allows for the storage of diverse data types while merging the ease and scalability of NoSQL with the capabilities of SQL. CrateDB stands out as a distributed database that executes queries in mere milliseconds, no matter the complexity, data volume, or speed of incoming data. This makes it an ideal solution for organizations that require rapid and efficient data processing. -
6
SAP HANA Cloud
SAP
Unlock real-time insights with adaptable, powerful cloud solutions.SAP HANA Cloud functions as a comprehensive managed in-memory database as a service (DBaaS) available in the cloud. It serves as the crucial data backbone for the SAP Business Technology Platform, enabling the integration of information from diverse organizational areas, which accelerates decision-making through real-time insights. This platform allows users to create data solutions utilizing modern architectures, delivering actionable insights almost immediately. As the cloud version of SAP HANA, it retains the same powerful features while being adaptable to meet specific requirements, thus facilitating the processing of a wide range of business data and enabling sophisticated analytics on live transactions without extensive optimization. Users can easily connect to distributed data with built-in integrations, develop applications and tools for both cloud and on-premises environments, and manage transient data effectively. By creating a unified source of truth, organizations can obtain reliable information while maintaining security, privacy, and data anonymization, all supported by a foundation of enterprise-grade reliability. Additionally, SAP HANA Cloud is designed to meet the changing demands of businesses as they navigate evolving market scenarios, ensuring that they remain competitive and responsive to new challenges. This adaptability positions the platform as a vital asset for organizations looking to harness the power of their data. -
7
ArcadeDB
ArcadeDB
Seamlessly integrate diverse data types with unmatched performance.Easily manage complex models with ArcadeDB without compromising on performance. There's no need to rely on Polyglot Persistence; you can store various data types without using multiple databases. In one ArcadeDB Multi-Model database, you can efficiently integrate graphs, documents, key-value pairs, and time series data seamlessly. Since each model is built directly into the database engine, worries about translation delays are a thing of the past. Designed with cutting-edge technology, ArcadeDB can handle millions of records per second effortlessly. A remarkable feature of ArcadeDB is its consistent traversal speed, which remains stable regardless of the database's size, whether it contains a handful of entries or billions. Additionally, ArcadeDB can function as an embedded database on a single server but is also capable of scaling across multiple servers with Kubernetes. Its adaptable nature allows it to run on any platform while using minimal resources. The security of your data is critical; our robust, fully transactional engine ensures durability for essential production databases. To maintain consistency across different servers, ArcadeDB utilizes a Raft Consensus Algorithm, guaranteeing that your data stays reliable and synchronized even in distributed settings. With ArcadeDB, you can effectively manage all your data requirements without the complications of handling multiple systems, ensuring a streamlined and efficient experience. Whether for small projects or large-scale applications, ArcadeDB provides the flexibility and performance needed to meet diverse data challenges. -
8
SAP HANA
SAP
Transform your business with real-time insights and intelligence.SAP HANA is a cutting-edge in-memory database that efficiently manages both transactional and analytical workloads by utilizing a single data copy, regardless of its nature. It effectively eliminates the divide between transactional and analytical functions within businesses, allowing for quick decision-making whether used in a traditional data center or through cloud services. This advanced database management system grants users the ability to develop intelligent, real-time applications, which promotes fast decision-making from a consolidated data repository. By integrating sophisticated analytics, it bolsters the performance of modern transaction processing systems. Organizations can leverage cloud-native features such as enhanced scalability, speed, and performance to create comprehensive data solutions. With SAP HANA Cloud, businesses gain access to dependable and actionable insights from a unified platform while maintaining stringent security, privacy, and data anonymization that align with established enterprise standards. In the rapidly evolving market landscape, the intelligent enterprise increasingly depends on prompt insights generated from data, highlighting the necessity for real-time access to critical information. As organizations face rising expectations for immediate insights, adopting a powerful database solution like SAP HANA is essential for maintaining a competitive edge. The ability to make informed decisions based on real-time data is becoming a key differentiator in business success. -
9
Couchbase
Couchbase
Unleash unparalleled scalability and reliability for modern applications.Couchbase sets itself apart from other NoSQL databases by providing an enterprise-level, multicloud to edge solution that is packed with essential features for mission-critical applications, built on a platform known for its exceptional scalability and reliability. This distributed cloud-native database functions effortlessly within modern, dynamic environments, supporting any cloud setup, from customer-managed to fully managed services. By utilizing open standards, Couchbase effectively combines the strengths of NoSQL with the familiar aspects of SQL, which aids organizations in transitioning smoothly from traditional mainframe and relational databases. Couchbase Server acts as a flexible, distributed database that merges the relational database advantages, such as SQL and ACID transactions, with the flexibility of JSON, all while maintaining high-speed performance and scalability. Its wide-ranging applications serve various sectors, addressing requirements like user profiles, dynamic product catalogs, generative AI applications, vector search, rapid caching, and much more, thus proving to be an indispensable resource for organizations aiming for enhanced efficiency and innovation. Additionally, its ability to adapt to evolving technologies ensures that users remain at the forefront of their industries. -
10
MarkLogic
Progress Software
Empower your business with seamless data integration and insights.Harness the capabilities of your data to streamline informed decision-making and achieve agile data management safely through the MarkLogic data platform. This innovative platform enables the integration of your data with pertinent metadata into a cohesive service, which in turn accelerates the decision-making process and enhances its quality. Discover a dependable and effective method for securely connecting data and metadata, extracting valuable insights, and obtaining high-quality, contextual information throughout your enterprise with the MarkLogic data platform. Obtain real-time insights into customer behavior to provide relevant and fluid interactions, identify new avenues for innovation, and ensure compliant access within a unified data framework. By utilizing MarkLogic, you establish a robust foundation that aligns with your critical business and technical objectives, both in the present and as you navigate future challenges, guaranteeing your competitive edge in an ever-evolving market landscape. Additionally, the platform's flexibility allows for ongoing adaptation, empowering your organization to continually refine its strategies in response to emerging trends. -
11
Azure Cosmos DB
Microsoft
Experience unmatched performance and reliability in cloud databases.Azure Cosmos DB is a fully managed NoSQL database solution tailored for modern application development, delivering guaranteed response times in just a few milliseconds and boasting an impressive availability rate of 99.999%, as outlined in its service level agreements (SLAs). It offers automatic scaling and is compatible with popular open-source APIs such as MongoDB and Cassandra, allowing developers to utilize familiar tools with ease. With its turnkey multi-master global distribution, users benefit from swift read and write operations from virtually anywhere across the globe. Additionally, it empowers organizations to reduce the time needed for insights by enabling near-real-time analytics and artificial intelligence on the operational data stored within Azure Cosmos DB. The integration with Azure Synapse Link also streamlines the connection to Azure Synapse Analytics, facilitating efficient data analysis without requiring data movement or affecting the operational data store's performance. This robust set of features positions Azure Cosmos DB as an exceptional choice for developers seeking both high performance and reliability in their applications, making it an invaluable resource in the realm of cloud databases. Ultimately, organizations leveraging this technology can enhance their operational efficiency and drive innovation more effectively. -
12
FoundationDB
FoundationDB
Empower your data with a versatile, reliable database solution.FoundationDB functions as a versatile multi-model database, allowing for the integration of diverse data formats within a unified platform. Its Key-Value Store feature guarantees that data is stored securely, distributed efficiently, and replicated reliably across the system. The processes of installation, scaling, and management are user-friendly, leveraging a distributed architecture that adeptly adapts to growth and mitigates failures, while still upholding the characteristics of a cohesive ACID-compliant database. Notably, it provides remarkable performance on everyday hardware, making it well-equipped to tackle extensive workloads without incurring high expenses. With a proven track record of years in production environments, FoundationDB has been strengthened by valuable real-world experiences and lessons learned. Its backup functionality is exceptional, employing a deterministic simulation engine for rigorous testing. We encourage you to join our thriving open-source community, where you can participate in both technical and user-centered discussions on our forums and explore various ways to contribute to the ongoing development of the project. By getting involved, you can play a pivotal role in shaping the evolution of FoundationDB for future users! -
13
SurrealDB
SurrealDB
Transform your data journey with innovative, real-time solutions.SurrealDB offers a dynamic and adaptable platform specifically designed for businesses seeking innovative solutions. It comes equipped with a wide range of advanced database tools and services that help teams discover creative approaches tailored to their unique requirements. The query language of SurrealDB, reminiscent of standard SQL, excels in managing both time-series data and complex interconnected graph structures. SurrealQL, an advanced query language, integrates features typical of programming languages, enabling developers and data analysts to interact with SurrealDB in a way that aligns with their individual workflows. Users can easily connect to SurrealDB from any client device, allowing them to run SurrealQL queries directly within web browsers, ensuring that data access is both secure and permission-based. The platform is designed with efficient WebSocket connections that support smooth bi-directional communication for queries, responses, and real-time alerts, significantly improving the user experience. This continuous connectivity and responsiveness distinguish SurrealDB in the competitive landscape of database solutions, making it an appealing choice for modern enterprises. Additionally, its user-friendly interface and robust functionality invite users to explore and harness the full potential of their data in innovative ways. -
14
Oracle Database
Oracle
Revolutionize your data management with flexible, efficient solutions.Oracle offers a range of database solutions designed to be both cost-effective and highly efficient, featuring prominent options such as a multi-model database management system, in-memory databases, NoSQL, and MySQL. The Oracle Autonomous Database enhances the user experience by enabling streamlined management of relational database systems, accessible both on-premises via Oracle Cloud@Customer and through Oracle Cloud Infrastructure, thereby reducing administrative burdens. By simplifying the complexities involved in operating and securing Oracle Database, the Autonomous Database provides users with outstanding performance, scalability, and reliability. For organizations focused on data residency and minimizing network latency, the option for on-premises deployment of Oracle Database is available. Moreover, clients using specific versions of Oracle databases retain complete control over their operational versions, as well as the timing of updates. This level of flexibility not only empowers businesses to customize their database environments but also ensures they can adapt to evolving requirements seamlessly. Ultimately, Oracle's diverse database offerings are tailored to meet the varied needs of clients across different sectors. -
15
ArangoDB
ArangoDB
Seamlessly store and access diverse data with confidence.Store data natively for various requirements such as graphs, documents, and search functionalities. A single query language facilitates rich access to features. You can seamlessly map your data to the database and retrieve it using optimal patterns suited for your tasks, including traversals, joins, searches, rankings, geospatial queries, and aggregations—whatever you need. Enjoy polyglot persistence without incurring high costs. The architecture is easily designed, scaled, and adapted to accommodate evolving needs with minimal effort. By merging the versatility and strength of JSON with graph technology, you can derive advanced features even from extensive datasets, ensuring your solutions remain cutting-edge. This integration not only maximizes efficiency but also empowers you to tackle complex data challenges with confidence. -
16
Apache Ignite
Apache Ignite
Unlock data power with lightning-fast SQL and analytics.Leverage Ignite as a traditional SQL database by utilizing JDBC and ODBC drivers, or by accessing the native SQL APIs available for programming languages like Java, C#, C++, and Python. Seamlessly conduct operations such as joining, grouping, aggregating, and ordering your data, which can be stored both in-memory and on-disk. Boost the efficiency of your existing applications up to 100 times by incorporating Ignite as an in-memory cache or data grid that connects with one or several external databases. Imagine a caching framework that supports SQL queries, transactional processes, and complex computational tasks. Build innovative applications that can manage both transactional and analytical operations by using Ignite as a database that surpasses the constraints of available memory. Ignite adeptly handles memory for frequently accessed information while offloading less commonly queried data to disk storage. Execute custom code snippets, even as small as a kilobyte, over extensive datasets that can reach petabyte scales. Transform your Ignite database into a robust distributed supercomputer engineered for rapid computations, sophisticated analytics, and advanced machine learning initiatives. Furthermore, Ignite not only streamlines data management but also empowers organizations to unlock the full potential of their data, paving the way for groundbreaking solutions and insights. By harnessing its capabilities, teams can drive innovation and improve decision-making processes across various sectors. -
17
Dremio
Dremio
Empower your data with seamless access and collaboration.Dremio offers rapid query capabilities along with a self-service semantic layer that interacts directly with your data lake storage, eliminating the need to transfer data into exclusive data warehouses, and avoiding the use of cubes, aggregation tables, or extracts. This empowers data architects with both flexibility and control while providing data consumers with a self-service experience. By leveraging technologies such as Apache Arrow, Data Reflections, Columnar Cloud Cache (C3), and Predictive Pipelining, Dremio simplifies the process of querying data stored in your lake. An abstraction layer facilitates the application of security and business context by IT, enabling analysts and data scientists to access and explore data freely, thus allowing for the creation of new virtual datasets. Additionally, Dremio's semantic layer acts as an integrated, searchable catalog that indexes all metadata, making it easier for business users to interpret their data effectively. This semantic layer comprises virtual datasets and spaces that are both indexed and searchable, ensuring a seamless experience for users looking to derive insights from their data. Overall, Dremio not only streamlines data access but also enhances collaboration among various stakeholders within an organization.
Multi-Model Databases Buyers Guide
In the modern data landscape, businesses are handling an ever-growing variety of data types. Traditional database management systems often struggle to accommodate this diversity, leading to inefficiencies and increased operational costs. Multi-model databases have emerged as a transformative solution, allowing organizations to manage different data structures within a single, cohesive system. By supporting various formats—including relational, document-based, graph, and key-value—these databases provide the flexibility needed to handle complex business requirements with greater efficiency.
Unlike single-model databases that force organizations to work within a specific framework, multi-model databases empower businesses to leverage the best-suited data model for different applications, all while maintaining a unified storage and management environment. This adaptability is crucial for enterprises that require high levels of scalability, integration, and performance optimization across multiple data types.
Essential Features of Multi-Model Databases
Organizations evaluating multi-model databases should consider the following essential features:
- Support for Multiple Data Models
- Enables businesses to store and process different types of data within a single database engine.
- Supports relational tables, document storage (JSON, BSON), key-value pairs, and graph-based structures.
- Reduces the need for multiple database systems, simplifying architecture and reducing costs.
- Unified Query Language
- Allows seamless interaction with various data models using a single query language or interface.
- Reduces the learning curve for developers and analysts, improving productivity.
- Minimizes the complexity of managing multiple database technologies.
- Schema Flexibility
- Provides the ability to define and modify data structures dynamically.
- Supports both structured and semi-structured data, ensuring adaptability to changing business needs.
- Eliminates the need for extensive schema migrations when evolving data models.
- Data Integration and Interoperability
- Facilitates integration with diverse data sources, including real-time and batch processing applications.
- Combines structured and unstructured data for comprehensive analytics and reporting.
- Enhances connectivity with third-party tools and platforms for business intelligence and decision-making.
- Scalability and Performance Optimization
- Designed to scale horizontally, accommodating increasing data loads without compromising efficiency.
- Optimizes query execution based on the underlying data model, improving response times.
- Supports distributed architectures, enabling businesses to expand infrastructure as needed.
- Transactional Consistency and Data Integrity
- Provides strong or eventual consistency, depending on the use case.
- Ensures accurate and reliable data transactions across different models.
- Helps maintain compliance with industry standards and regulatory requirements.
Key Advantages of Multi-Model Databases
The adoption of multi-model databases offers substantial benefits that can transform how organizations manage their data. These advantages include:
- Enhanced Versatility: With the ability to support multiple data models within a single environment, businesses can handle a broad range of applications, from transactional workloads to analytical processing, without the need for separate database solutions.
- Operational Efficiency and Cost Savings: Consolidating different data management functionalities into a single database reduces administrative overhead, maintenance efforts, and licensing expenses. This streamlining also simplifies deployment and reduces the need for specialized IT expertise in managing disparate systems.
- Deeper Business Insights: By integrating and analyzing different data types, organizations can uncover valuable insights that were previously hidden in siloed databases. This holistic view enables better decision-making and enhances business intelligence strategies.
- Faster Development and Deployment: Developers benefit from a unified framework that simplifies coding, reduces redundancies, and accelerates application development cycles. The ability to work within a single platform minimizes compatibility issues and enhances agility.
- Future-Proofing Technology Investments: As data needs continue to evolve, businesses can adapt their data strategies without having to migrate to entirely new database systems. This flexibility ensures long-term viability and scalability.
Real-World Use Cases
Multi-model databases are particularly well-suited for industries and applications that demand complex data management capabilities. Some common use cases include:
- eCommerce & Retail: Handling product catalogs, customer profiles, transactional data, and recommendation engines within a single system.
- Financial Services: Managing risk assessments, fraud detection, real-time transactions, and regulatory compliance data seamlessly.
- Healthcare & Life Sciences: Storing patient records, medical research, and treatment plans while ensuring data security and accessibility.
- Social Media & Networking: Supporting user interactions, relationships, and content storage using graph and document-based structures.
- Internet of Things (IoT): Aggregating and analyzing massive streams of data generated by connected devices for real-time insights.
Challenges in Implementing Multi-Model Databases
Despite their many benefits, implementing a multi-model database comes with its own set of challenges. Organizations should be aware of potential hurdles and prepare strategies to mitigate them.
- Complex Data Management: Managing multiple data models within a single database requires careful planning and expertise. Improperly designed schemas or inefficient queries can lead to performance bottlenecks and data inconsistencies.
- Skill and Training Requirements: Employees may need additional training to fully leverage the capabilities of a multi-model database. Understanding different query structures and optimization techniques is essential to ensure smooth adoption.
- Data Governance and Compliance: Ensuring data security, compliance, and governance across different data models can be challenging. Businesses must implement robust policies to maintain data integrity and protect sensitive information.
- Vendor Lock-In Risks: Choosing a proprietary multi-model database may limit future integration options or increase migration complexity if switching to another solution. Organizations should evaluate long-term compatibility before committing to a specific vendor.
Final Thoughts
Multi-model databases are revolutionizing the way businesses handle diverse data requirements. By consolidating different data models within a single system, organizations can improve efficiency, scalability, and insight generation while reducing operational costs. While the transition to a multi-model approach requires careful planning and investment, the long-term benefits make it a compelling choice for businesses seeking to stay competitive in an increasingly data-driven world.
As the demand for flexible, high-performance data solutions continues to rise, organizations that embrace multi-model databases will be well-positioned to navigate the complexities of modern data management with confidence.