Alternatives to Neo4j

MongoDB Atlas stands out as the leading cloud database service available, offering unparalleled data distribution and seamless mobility across all major platforms, including AWS, Azure, and Google Cloud. Its built-in automation tools enhance resource management and workload optimization, making it the go-to choice for modern application deployment. As a fully managed service, it ensures best-in-class automation and adheres to established practices that support high availability, scalability, and compliance with stringent data security and privacy regulations. Furthermore, MongoDB Atlas provides robust security controls tailored for your data needs, allowing for the integration of enterprise-grade features that align with existing security protocols and compliance measures. With preconfigured elements for authentication, authorization, and encryption, you can rest assured that your data remains secure and protected at all times. Ultimately, MongoDB Atlas not only simplifies deployment and scaling in the cloud but also fortifies your data with comprehensive security features that adapt to evolving requirements.

Redis Labs is the home of Redis. Redis Enterprise is the best Redis version. Redis Enterprise is more than a cache. Redis Enterprise can be free in the cloud with NoSQL and data caching using the fastest in-memory database. Redis can be scaled, enterprise-grade resilience, massive scaling, ease of administration, and operational simplicity. Redis in the Cloud is a favorite of DevOps. Developers have access to enhanced data structures and a variety modules. This allows them to innovate faster and has a faster time-to-market. CIOs love the security and expert support of Redis, which provides 99.999% uptime. Use relational databases for active-active, geodistribution, conflict distribution, reads/writes in multiple regions to the same data set. Redis Enterprise offers flexible deployment options. Redis Labs is the home of Redis. Redis JSON, Redis Java, Python Redis, Redis on Kubernetes & Redis gui best practices.

Amazon DynamoDB is a versatile key-value and document database that provides exceptional single-digit millisecond performance, regardless of scale. As a fully managed service, it offers multi-region, multimaster durability along with integrated security features, backup and restore capabilities, and in-memory caching designed for internet-scale applications. With the ability to handle over 10 trillion requests daily and support peak loads exceeding 20 million requests per second, it serves a wide range of businesses. Prominent companies like Lyft, Airbnb, and Redfin, alongside major enterprises such as Samsung, Toyota, and Capital One, rely on DynamoDB for their critical operations, leveraging its scalability and performance. This allows organizations to concentrate on fostering innovation without the burden of operational management. You can create an immersive gaming platform that manages player data, session histories, and leaderboards for millions of users simultaneously. Additionally, it facilitates the implementation of design patterns for various applications like shopping carts, workflow engines, inventory management, and customer profiles. DynamoDB is well-equipped to handle high-traffic, large-scale events seamlessly, making it an ideal choice for modern applications.

TIMi allows companies to use their corporate data to generate new ideas and make crucial business decisions more quickly and easily than ever before. The heart of TIMi’s Integrated Platform. TIMi's ultimate real time AUTO-ML engine. 3D VR segmentation, visualization. Unlimited self service business Intelligence. TIMi is a faster solution than any other to perform the 2 most critical analytical tasks: data cleaning, feature engineering, creation KPIs, and predictive modeling. TIMi is an ethical solution. There is no lock-in, just excellence. We guarantee you work in complete serenity, without unexpected costs. TIMi's unique software infrastructure allows for maximum flexibility during the exploration phase, and high reliability during the production phase. TIMi allows your analysts to test even the most crazy ideas.

RavenDB is a pioneering NoSQL Document Database. It is fully transactional (ACID across your database and within your cluster). Our open-source distributed database has high availability and high performance, with minimal administration. It is an all-in-one database that is easy to use. This reduces the need to add on tools or support for developers to increase developer productivity and speed up your project's production. In minutes, you can create and secure a data cluster and deploy it in the cloud, on-premise, or in a hybrid environment. RavenDB offers a Database as a Service, which allows you to delegate all database operations to us, so you can concentrate on your application. RavenDB's built-in storage engine Voron can perform at speeds of up to 1,000,000 reads per second and 150,000 write per second on a single node. This allows you to improve your application's performance by using simple commodity hardware.

MongoDB is a versatile, document-oriented, distributed database designed specifically for contemporary application developers and the cloud landscape. It offers unparalleled productivity, enabling teams to ship and iterate products 3 to 5 times faster thanks to its adaptable document data model and a single query interface that caters to diverse needs. Regardless of whether you're serving your very first customer or managing 20 million users globally, you'll be able to meet your performance service level agreements in any setting. The platform simplifies high availability, safeguards data integrity, and adheres to the security and compliance requirements for your critical workloads. Additionally, it features a comprehensive suite of cloud database services that support a broad array of use cases, including transactional processing, analytics, search functionality, and data visualizations. Furthermore, you can easily deploy secure mobile applications with built-in edge-to-cloud synchronization and automatic resolution of conflicts. MongoDB's flexibility allows you to operate it in various environments, from personal laptops to extensive data centers, making it a highly adaptable solution for modern data management challenges.

Objectivity/DB (or Object Database Management System) is a distributed, highly scalable, high-performance, and highly scalable Object Database (ODBMS). It excels at complex data handling, including many types of connections between objects as well as many variants. Objectivity/DB can also be used as a graph database that is highly scalable and high-performance. Its DO query language allows for standard data retrieval queries and high-performance path-based navigational inquiries. Objectivity/DB is a distributed data base that presents a single logical view of its managed data. Data can be hosted on one machine or distributed over up to 65,000 machines. Machines can be connected to one another. Objectivity/DB can be used on 32- or 64-bit processors that run Windows, Linux, and Mac OS X. APIs are C++, C# Java, Python, and Java. All platforms and languages are interoperable. A C++ program on Linux can store objects and a Java program on Mac OS X can read them.

OrientDB stands out as the fastest graph database globally. An independent benchmarking analysis conducted by IBM alongside the Tokyo Institute of Technology revealed that OrientDB outperforms Neo4j by a factor of ten in graph operations across various workloads. This exceptional speed can help organizations gain a competitive edge and foster innovation, ultimately leading to the development of new revenue opportunities. By leveraging OrientDB, businesses can enhance their operational efficiency and stay ahead in a rapidly evolving market.

InfiniteGraph is a massively scalable graph database specifically designed to excel at high-speed ingest of massive volumes of data (billions of nodes and edges per hour) while supporting complex queries. InfiniteGraph can seamlessly distribute connected graph data across a global enterprise. InfiniteGraph is a schema-based graph database that supports highly complex data models. It also has an advanced schema evolution capability that allows you to modify and evolve the schema of an existing database. InfiniteGraph’s Placement Management Capability allows you to optimize the placement of data items resulting in tremendous performance improvements in both query and ingest. InfiniteGraph has client-side caching which caches frequently used node and edges. This can allow InfiniteGraph to perform like an in-memory graph database. InfiniteGraph's DO query language enables complex "beyond graph" queries not supported by other graph databases.

Simplifying data collaboration is our mission. Whether you're a developer eager to push the boundaries of innovation or a data specialist seeking effective version control, our platform caters to all needs. TerminusDB is an open-source knowledge graph database designed to facilitate secure, efficient, and private revision control and teamwork. By using TerminusDB, you can enhance your productivity when collaborating with teammates or developing data-heavy applications. It offers a comprehensive set of revision control functionalities. With TerminusHub, users can oversee database access and work together on shared resources seamlessly. Our platform boasts versatile capabilities for data storage, sharing, and versioning, making it suitable for both team-based collaboration and integration into applications. You can operate locally and sync changes when you're ready. Our tools also simplify querying, data cleaning, and visualization tasks. By incorporating robust version control and collaborative features, we empower both enterprise and individual users, enabling remote data teams to collaborate efficiently on projects. Together, we can drive innovation through effective data teamwork.

Unlock the potential of your data, expedite insightful decision-making, and attain agility in data management securely with the MarkLogic data platform. This platform allows you to merge your data with all relevant metadata within a unified service, leading to quicker and more informed decision-making. Experience a reliable and efficient approach to securely link data and metadata, derive meaningful insights, and access high-quality contextualized information throughout your organization using the MarkLogic data platform. Gain real-time insights into your customers to deliver relevant and seamless interactions, uncover new opportunities to drive innovation, and facilitate compliant access within a single data framework. With MarkLogic, you have a solid foundation that supports your essential business and technical goals both now and in the future, ensuring you stay ahead in a dynamic market.

HyperGraphDB serves as a versatile, open-source data storage solution founded on the sophisticated knowledge management framework of directed hypergraphs. Primarily created for persistent memory applications in knowledge management, artificial intelligence, and semantic web initiatives, it can also function as an embedded object-oriented database suitable for Java applications of varying scales, in addition to serving as a graph database or a non-SQL relational database. Built upon a foundation of generalized hypergraphs, HyperGraphDB utilizes tuples as its fundamental storage units, which can consist of zero or more other tuples; these individual tuples are referred to as atoms. The data model can be perceived as relational, permitting higher-order, n-ary relationships, or as graph-based, where edges can connect to an arbitrary assortment of nodes and other edges. Each atom is associated with a strongly-typed value that can be customized extensively, as the type system that governs these values is inherently embedded within the hypergraph structure. This flexibility allows developers to tailor the database according to specific project requirements, making it a robust choice for a wide range of applications.

Anzo is an innovative platform for data discovery and integration that empowers users to locate, connect, and blend various enterprise data into datasets that are ready for analysis. With its distinctive application of semantics and graph data models, Anzo enables individuals across the organization—from expert data scientists to inexperienced business users—to actively participate in the data discovery and integration journey, crafting their own analytics-ready datasets in the process. The graph data models offered by Anzo create a visual representation of enterprise data, simplifying the navigation and understanding of complex and siloed information. By incorporating semantics, Anzo enriches the data with business context, allowing users to unify data according to shared definitions and create blended datasets that are tailored for immediate business needs. This democratization of data access not only fosters collaboration but also accelerates decision-making across various levels of the organization.

Memgraph offers a light and powerful graph platform comprising the Memgraph Graph Database, MAGE Library, and Memgraph Lab Visualization. Memgraph is a dynamic, lightweight graph database optimized for analyzing data, relationships, and dependencies quickly and efficiently. It comes with a rich suite of pre-built deep path traversal algorithms and a library of traditional, dynamic, and ML algorithms tailored for advanced graph analysis, making Memgraph an excellent choice in critical decision-making scenarios such as risk assessment (fraud detection, cybersecurity threat analysis, and criminal risk assessment), 360-degree data and network exploration (Identity and Access Management (IAM), Master Data Management (MDM), Bill of Materials (BOM)), and logistics and network optimization. Memgraph's vibrant open-source community brings together over 150,000 developers in more than 100 countries to exchange ideas and optimize the next generation of in-memory data-driven applications across GenAI/ LLMs and real-time analytics performed with streaming data.

Atlas serves as a versatile and scalable suite of essential governance services, empowering organizations to efficiently comply with regulations within the Hadoop ecosystem while facilitating integration across the enterprise's data landscape. Apache Atlas offers comprehensive metadata management and governance tools that assist businesses in creating a detailed catalog of their data assets, effectively classifying and managing these assets, and fostering collaboration among data scientists, analysts, and governance teams. It comes equipped with pre-defined types for a variety of both Hadoop and non-Hadoop metadata, alongside the capability to establish new metadata types tailored to specific needs. These types can incorporate primitive attributes, complex attributes, and object references, and they can also inherit characteristics from other types. Entities, which are instances of these types, encapsulate the specifics of metadata objects and their interconnections. Additionally, REST APIs enable seamless interaction with types and instances, promoting easier integration and enhancing overall functionality. This robust framework not only streamlines governance processes but also supports a culture of data-driven collaboration across the organization.

AnzoGraph DB boasts an extensive array of analytical features that can significantly improve your analytical framework. Check out this video to discover how AnzoGraph DB operates as a Massively Parallel Processing (MPP) native graph database specifically designed for data harmonization and analytics. This horizontally scalable graph database is optimized for online analytics and tackling data harmonization issues. Addressing challenges related to linked data, AnzoGraph DB stands out as a leading analytical graph database in the market. It offers robust online performance suitable for enterprise-scale graph applications, ensuring efficiency and speed. AnzoGraph DB employs familiar SPARQL*/OWL for semantic graphs, while also accommodating Labeled Property Graphs (LPGs). Its vast array of analytical, machine learning, and data science tools empowers users to uncover new insights at remarkable speed and scale. By prioritizing context and relationships among data, you can enhance your analysis significantly. Additionally, the database enables ultra-fast data loading and execution of analytical queries, making it an invaluable asset for any data-driven organization.

Azure Cosmos DB offers a fully managed NoSQL database solution tailored for contemporary application development, ensuring single-digit millisecond response times and an impressive availability rate of 99.999 percent, all supported by service level agreements. This service provides automatic, instantaneous scalability and supports open-source APIs for MongoDB and Cassandra, allowing for rapid data operations. With its turnkey multi-master global distribution, users can experience swift read and write operations from any location around the globe. Additionally, Azure Cosmos DB enables organizations to accelerate their decision-making processes by facilitating near-real-time analytics and AI capabilities on the operational data housed within the database. Furthermore, Azure Synapse Link for Azure Cosmos DB integrates effortlessly with Azure Synapse Analytics, ensuring smooth performance without necessitating data movement or compromising the efficiency of the operational data store, enhancing the overall functionality of your data strategy. This integration not only streamlines workflows but also empowers users to derive insights more efficiently.

Apache Giraph is a scalable iterative graph processing framework designed to handle large datasets efficiently. It has gained prominence at Facebook, where it is employed to analyze the intricate social graph created by user interactions and relationships. Developed as an open-source alternative to Google's Pregel, which was introduced in a seminal 2010 paper, Giraph draws inspiration from the Bulk Synchronous Parallel model of distributed computing proposed by Leslie Valiant. Beyond the foundational Pregel model, Giraph incorporates numerous enhancements such as master computation, sharded aggregators, edge-focused input methods, and capabilities for out-of-core processing. The ongoing enhancements and active support from a growing global community make Giraph an ideal solution for maximizing the analytical potential of structured datasets on a grand scale. Additionally, built upon the robust infrastructure of Apache Hadoop, Giraph is well-equipped to tackle complex graph processing challenges efficiently.

Store data in its native format for graph, document, and search purposes. Leverage a comprehensive query language that allows for rich access to this data. Map the data directly to the database and interact with it through optimal methods tailored for specific tasks, such as traversals, joins, searches, rankings, geospatial queries, and aggregations. Experience the benefits of polyglot persistence without incurring additional costs. Design, scale, and modify your architectures with ease to accommodate evolving requirements, all while minimizing effort. Merge the adaptability of JSON with advanced semantic search and graph technologies, enabling the extraction of features even from extensive datasets, thereby enhancing data analysis capabilities. This combination opens up new possibilities for handling complex data scenarios efficiently.

Amazon Neptune is an efficient and dependable graph database service that is fully managed, facilitating the development and operation of applications that handle intricate, interconnected datasets. At its heart, Amazon Neptune features a specialized, high-performance database engine tailored for the storage of billions of relationships while enabling rapid querying with latency measured in milliseconds. It accommodates widely-used graph models, including Property Graph and W3C's RDF, along with their associated query languages, Apache TinkerPop Gremlin and SPARQL, which simplifies the process of crafting queries for navigating complex datasets. This service supports various graph-based applications, including recommendation systems, fraud detection mechanisms, knowledge graphs, drug discovery initiatives, and enhanced network security protocols. With a proactive approach, it enables the detection and analysis of IT infrastructure threats through a multi-layered security framework. Furthermore, it allows users to visualize their entire infrastructure to effectively plan, forecast, and address potential risks, while also enabling the creation of graph queries for the near-real-time identification of fraudulent patterns in financial and purchasing activities, thereby enhancing overall security and efficiency.

Cayley is an open-source database tailored for Linked Data, drawing inspiration from the graph database that supports Google's Knowledge Graph, previously known as Freebase. This graph database is crafted for user-friendliness and adept at handling intricate data structures, featuring an integrated query editor, a visualizer, and a Read-Eval-Print Loop (REPL). It supports various query languages, including Gizmo, which is influenced by Gremlin, a GraphQL-like query language, and MQL, a streamlined version catering to Freebase enthusiasts. Cayley's modular architecture allows seamless integration with preferred programming languages and backend storage solutions, making it production-ready, thoroughly tested, and utilized by numerous companies for their operational tasks. Additionally, it is optimized for application use, demonstrating impressive performance metrics; for instance, testing has shown that it can effortlessly manage 134 million quads in LevelDB on consumer-grade hardware from 2014, with multi-hop intersection queries—such as finding films featuring both X and Y—executing in about 150 milliseconds. By default, Cayley is set up to operate in-memory, which is what the backend memstore refers to, thereby enhancing its speed and efficiency for data retrieval and manipulation. Overall, Cayley offers a powerful solution for those looking to leverage linked data in their applications.

AllegroGraph represents a revolutionary advancement that facilitates limitless data integration through a proprietary methodology that merges all types of data and isolated knowledge into a cohesive Entity-Event Knowledge Graph, which is capable of handling extensive big data analytics. It employs distinctive federated sharding features that promote comprehensive insights and allow for intricate reasoning across a decentralized Knowledge Graph. Additionally, AllegroGraph offers an integrated version of Gruff, an innovative browser-based tool designed for visualizing graphs, helping users to explore and uncover relationships within their enterprise Knowledge Graphs. Furthermore, Franz's Knowledge Graph Solution encompasses both cutting-edge technology and expert services aimed at constructing robust Entity-Event Knowledge Graphs, leveraging top-tier tools, products, and extensive expertise to ensure optimal performance. This comprehensive approach not only enhances data utility but also empowers organizations to derive deeper insights and drive informed decision-making.

Create adaptable, decentralized systems that can utilize the complete history of your vital data rather than just its latest version. You can either build these systems on your current infrastructure or opt to transition directly to cloud solutions. Gaining critical insights requires understanding the entire narrative of your data, not merely its most recent status. Datomic maintains a repository of unchangeable facts, offering your applications a robust consistency while facilitating horizontal read scalability along with integrated caching features. Since facts are never modified directly and all data is preserved by default, you benefit from inherent auditing capabilities and the option to query historical information. Additionally, this system supports fully ACID-compliant transactions. The information model of Datomic is designed to accommodate a diverse range of use cases. With the Datomic Peer library, you can disseminate immutable data across your application nodes, ensuring in-memory access to your information. Alternatively, leverage the client library to establish lightweight nodes tailored for microservice architectures, enabling seamless integration and enhanced performance. By utilizing these capabilities, you can achieve a comprehensive understanding of your data landscape.

Introducing a versatile, open-source multi-cloud platform for contemporary data applications, built on Apache Cassandra™. Achieve global-scale performance with guaranteed 100% uptime while avoiding vendor lock-in. You have the flexibility to deploy on multi-cloud environments, on-premises infrastructures, or use Kubernetes. The platform is designed to be elastic and offers a pay-as-you-go pricing model to enhance total cost of ownership. Accelerate your development process with Stargate APIs, which support NoSQL, real-time interactions, reactive programming, as well as JSON, REST, and GraphQL formats. Bypass the difficulties associated with managing numerous open-source projects and APIs that lack scalability. This solution is perfect for various sectors including e-commerce, mobile applications, AI/ML, IoT, microservices, social networking, gaming, and other highly interactive applications that require dynamic scaling based on demand. Start your journey of creating modern data applications with Astra, a database-as-a-service powered by Apache Cassandra™. Leverage REST, GraphQL, and JSON alongside your preferred full-stack framework. This platform ensures that your richly interactive applications are not only elastic but also ready to gain traction from the very first day, all while offering a cost-effective Apache Cassandra DBaaS that scales seamlessly and affordably as your needs evolve. With this innovative approach, developers can focus on building rather than managing infrastructure.

*GraphDB allows the creation of large knowledge graphs by linking diverse data and indexing it for semantic search. * GraphDB is a robust and efficient graph database that supports RDF and SPARQL. The GraphDB database supports a highly accessible replication cluster. This has been demonstrated in a variety of enterprise use cases that required resilience for data loading and query answering. Visit the GraphDB product page for a quick overview and a link to download the latest releases. GraphDB uses RDF4J to store and query data. It also supports a wide range of query languages (e.g. SPARQL and SeRQL), and RDF syntaxes such as RDF/XML and Turtle.

FalkorDB is an exceptionally rapid, multi-tenant graph database that is finely tuned for GraphRAG, ensuring accurate and relevant AI/ML outcomes while minimizing hallucinations and boosting efficiency. By utilizing sparse matrix representations alongside linear algebra, it adeptly processes intricate, interconnected datasets in real-time, leading to a reduction in hallucinations and an increase in the precision of responses generated by large language models. The database is compatible with the OpenCypher query language, enhanced by proprietary features that facilitate expressive and efficient graph data querying. Additionally, it incorporates built-in vector indexing and full-text search functions, which allow for intricate search operations and similarity assessments within a unified database framework. FalkorDB's architecture is designed to support multiple graphs, permitting the existence of several isolated graphs within a single instance, which enhances both security and performance for different tenants. Furthermore, it guarantees high availability through live replication, ensuring that data remains perpetually accessible, even in high-demand scenarios. This combination of features positions FalkorDB as a robust solution for organizations seeking to manage complex graph data effectively.

This robust enterprise platform, built on Apache Ignite, delivers lightning-fast in-memory performance and extensive scalability for data-heavy applications, ensuring real-time access across various datastores and applications. Transitioning from Ignite to GridGain requires no code modifications, allowing for secure deployment of clusters on a global scale without experiencing any downtime. You can conduct rolling upgrades on your production clusters without affecting application availability, and replicate data across geographically dispersed data centers to balance workloads and mitigate the risk of outages in specific regions. Your data remains secure both at rest and in transit, while compliance with security and privacy regulations is guaranteed. Seamless integration with your organization’s existing authentication and authorization frameworks is straightforward, and comprehensive auditing of data and user activities can be enabled. Additionally, you can establish automated schedules for both full and incremental backups, ensuring that restoring your cluster to its most stable state is achievable through snapshots and point-in-time recovery. This platform not only promotes efficiency but also enhances resilience and security for all data operations.

Graph Engine (GE) is a powerful distributed in-memory data processing platform that relies on a strongly-typed RAM storage system paired with a versatile distributed computation engine. This RAM store functions as a high-performance key-value store that is accessible globally across a cluster of machines. By leveraging this RAM store, GE facilitates rapid random data access over extensive distributed datasets. Its ability to perform swift data exploration and execute distributed parallel computations positions GE as an ideal solution for processing large graphs. The engine effectively accommodates both low-latency online query processing and high-throughput offline analytics for graphs containing billions of nodes. Efficient data processing emphasizes the importance of schema, as strongly-typed data models are vital for optimizing storage, accelerating data retrieval, and ensuring clear data semantics. GE excels in the management of billions of runtime objects, regardless of their size, demonstrating remarkable efficiency. Even minor variations in object count can significantly impact performance, underscoring the importance of every byte. Moreover, GE offers rapid memory allocation and reallocation, achieving impressive memory utilization ratios that further enhance its capabilities. This makes GE not only efficient but also an invaluable tool for developers and data scientists working with large-scale data environments.

Couchbase distinguishes itself from other NoSQL databases by delivering an enterprise-grade, multicloud to edge solution that is equipped with the powerful features essential for mission-critical applications on a platform that is both highly scalable and reliable. This distributed cloud-native database operates seamlessly in contemporary dynamic settings, accommodating any cloud environment, whether it be customer-managed or a fully managed service. Leveraging open standards, Couchbase merges the advantages of NoSQL with the familiar structure of SQL, thereby facilitating a smoother transition from traditional mainframe and relational databases. Couchbase Server serves as a versatile, distributed database that integrates the benefits of relational database capabilities, including SQL and ACID transactions, with the adaptability of JSON, all built on a foundation that is remarkably fast and scalable. Its applications span various industries, catering to needs such as user profiles, dynamic product catalogs, generative AI applications, vector search, high-speed caching, and much more, making it an invaluable asset for organizations seeking efficiency and innovation.

Dgraph is an open-source, low-latency, high throughput native and distributed graph database. DGraph is designed to scale easily to meet the needs for small startups and large companies with huge amounts of data. It can handle terabytes structured data on commodity hardware with low latency to respond to user queries. It addresses business needs and can be used in cases that involve diverse social and knowledge networks, real-time recommendation engines and semantic search, pattern matching, fraud detection, serving relationship information, and serving web applications.

Google Cloud Bigtable provides a fully managed, scalable NoSQL data service that can handle large operational and analytical workloads. Cloud Bigtable is fast and performant. It's the storage engine that grows with your data, from your first gigabyte up to a petabyte-scale for low latency applications and high-throughput data analysis. Seamless scaling and replicating: You can start with one cluster node and scale up to hundreds of nodes to support peak demand. Replication adds high availability and workload isolation to live-serving apps. Integrated and simple: Fully managed service that easily integrates with big data tools such as Dataflow, Hadoop, and Dataproc. Development teams will find it easy to get started with the support for the open-source HBase API standard.

Fauna is a data API that supports rich clients with serverless backends. It provides a web-native interface that supports GraphQL, custom business logic, frictionless integration to the serverless ecosystem, and a multi-cloud architecture that you can trust and grow with.

SAP HANA is an in-memory database designed to handle both transactional and analytical workloads using a single copy of data, regardless of type. It effectively dissolves the barriers between transactional and analytical processes within organizations, facilitating rapid decision-making whether deployed on-premises or in the cloud. This innovative database management system empowers users to create intelligent, real-time solutions, enabling swift decision-making from a unified data source. By incorporating advanced analytics, it enhances the capabilities of next-generation transaction processing. Organizations can build data solutions that capitalize on cloud-native attributes such as scalability, speed, and performance. With SAP HANA Cloud, businesses can access reliable, actionable information from one cohesive platform while ensuring robust security, privacy, and data anonymization, reflecting proven enterprise standards. In today's fast-paced environment, an intelligent enterprise relies on timely insights derived from data, emphasizing the need for real-time delivery of such valuable information. As the demand for immediate access to insights grows, leveraging an efficient database like SAP HANA becomes increasingly critical for organizations aiming to stay competitive.

IBM Cloudant® is a robust distributed database tailored for managing the demanding workloads commonly associated with large, rapidly expanding web and mobile applications. Offered as a fully managed service on IBM Cloud™, backed by an SLA, Cloudant allows for the independent scaling of both throughput and storage. You can quickly deploy an instance, set up databases, and adjust throughput capacity and data storage as needed to align with your application’s demands. Furthermore, it ensures data security through encryption, providing optional user-defined key management via IBM Key Protect, while also allowing integration with IBM Identity and Access Management. With a focus on performance and disaster recovery, Cloudant guarantees continuous availability by distributing data across multiple availability zones and six regions, making it an ideal choice for critical applications. This distribution not only enhances app performance but also safeguards against potential data loss, ensuring your applications run smoothly and reliably.

Designed specifically for handling super large-scale graphs with latency measured in milliseconds, this graph database continues to engage with the community for its preparation, promotion, and popularization. Nebula Graph ensures that access is secured through role-based access control, allowing only authenticated users. The database supports various types of storage engines and its query language is adaptable, enabling the integration of new algorithms. By providing low latency for both read and write operations, Nebula Graph maintains high throughput, effectively simplifying even the most intricate data sets. Its shared-nothing distributed architecture allows for linear scalability, making it an efficient choice for expanding businesses. The SQL-like query language is not only user-friendly but also sufficiently robust to address complex business requirements. With features like horizontal scalability and a snapshot capability, Nebula Graph assures high availability, even during failures. Notably, major internet companies such as JD, Meituan, and Xiaohongshu have successfully implemented Nebula Graph in their production environments, showcasing its reliability and performance in real-world applications. This widespread adoption highlights the database's effectiveness in meeting the demands of large-scale data management.

Effortlessly handle intricate models with ArcadeDB while ensuring no compromises are made. Say goodbye to the concept of Polyglot Persistence; there's no need to juggle multiple databases. With ArcadeDB's Multi-Model database, you can seamlessly store graphs, documents, key values, and time series data in one unified solution. As each model is inherently compatible with the database engine, you can avoid the delays caused by translation processes. Powered by advanced Alien Technology, ArcadeDB's engine can process millions of records every second. Notably, the speed of data traversal remains constant regardless of the database's size, whether it houses a handful of records or billions. ArcadeDB is versatile enough to function as an embedded database on a single server and can easily scale across multiple servers using Kubernetes. Its compact design allows it to operate on any platform while maintaining a minimal footprint. Your data's security is paramount; our robust, fully transactional engine guarantees durability for mission-critical production databases. Additionally, ArcadeDB employs a Raft Consensus Algorithm to ensure consistency and reliability across multiple servers, making it a top choice for data management. In an era where efficiency and reliability are crucial, ArcadeDB stands out as a comprehensive solution for diverse data storage needs.

What makes eXtremeDB platform independent? - Hybrid storage of data. Unlike other IMDS databases, eXtremeDB databases are all-in-memory or all-persistent. They can also have a mix between persistent tables and in-memory table. eXtremeDB's Active Replication Fabric™, which is unique to eXtremeDB, offers bidirectional replication and multi-tier replication (e.g. edge-to-gateway-to-gateway-to-cloud), compression to maximize limited bandwidth networks and more. - Row and columnar flexibility for time series data. eXtremeDB supports database designs which combine column-based and row-based layouts in order to maximize the CPU cache speed. - Client/Server and embedded. eXtremeDB provides data management that is fast and flexible wherever you need it. It can be deployed as an embedded system and/or as a clients/server database system. eXtremeDB was designed for use in resource-constrained, mission-critical embedded systems. Found in over 30,000,000 deployments, from routers to satellites and trains to stock market world-wide.

When seeking a database that ensures both scalability and high availability without sacrificing performance, Apache Cassandra stands out as an ideal option. Its linear scalability paired with proven fault tolerance on standard hardware or cloud services positions it as an excellent choice for handling mission-critical data effectively. Additionally, Cassandra's superior capability to replicate data across several datacenters not only enhances user experience by reducing latency but also offers reassurance in the event of regional failures. This combination of features makes it a robust solution for organizations that prioritize data resilience and efficiency.

VelocityDB stands out as a unique database engine, renowned for its ability to store data more quickly and efficiently than any competing solutions while being significantly more affordable. Unlike traditional databases that rely on mapping to tables, JSON, or XML, it retains .NET objects in their native format. Complementing this is VelocityGraph, an open-source property graph database that seamlessly integrates with the VelocityDB object database. As a C# .NET noSQL object database, VelocityDB is enhanced by VelocityGraph, making it one of the fastest, most scalable, and flexible database options available today. Bugs that are reported along with reproducible test cases are typically resolved within a week, showcasing the responsiveness of the development team. The primary advantage of using this database system lies in its remarkable flexibility, enabling developers to tailor their applications to meet even the most specific requirements. With VelocityDB, you have the freedom to select the most suitable data structures for your needs, granting you control over the persistent placement of data, as well as how it is indexed and accessed. This level of customization sets VelocityDB apart in the realm of database technology.

ScyllaDB serves as an ideal database solution for applications that demand high performance and minimal latency, catering specifically to data-intensive needs. It empowers teams to fully utilize the growing computing capabilities of modern infrastructures, effectively removing obstacles to scaling as data volumes expand. Distinct from other database systems, ScyllaDB stands out as a distributed NoSQL database that is completely compatible with both Apache Cassandra and Amazon DynamoDB, while incorporating significant architectural innovations that deliver outstanding user experiences at significantly reduced costs. Over 400 transformative companies, including Disney+ Hotstar, Expedia, FireEye, Discord, Zillow, Starbucks, Comcast, and Samsung, rely on ScyllaDB to tackle their most challenging database requirements. Furthermore, ScyllaDB is offered in various formats, including a free open-source version, a fully-supported enterprise solution, and a fully managed database-as-a-service (DBaaS) available across multiple cloud platforms, ensuring flexibility for diverse user needs. This versatility makes it an attractive choice for organizations looking to optimize their database performance.

Couchbase Capella serves as a cloud-based database solution that enhances the storage and processing of both operational and analytical data in a JSON-friendly environment. It stands out as a superior DBaaS for transactions, search functionalities, AI applications, and edge computing, all while providing real-time analytics capabilities. Getting started with Capella is straightforward; you can utilize SQL to interact with your JSON data, and benefit from the natural language coding support offered by Capella iQ. This versatile database is engineered to handle both operational and analytic tasks, accommodating use cases such as JSON analytics, vector searches, and mobile data synchronization. Capella ensures rapid response times measured in milliseconds, all while offering exceptional price-performance metrics for any document-based Database-as-a-Service. You can easily sign up for a free trial and be operational within minutes. In addition to its impressive features, Capella is designed to address various challenges in development, architecture, and deployment, making it a smart choice for anyone looking to secure their technological future with a robust database solution.

The foundation of creating intelligent systems lies in the database, and Grakn serves as a sophisticated knowledge graph database. It features an incredibly user-friendly and expressive data schema that allows for the definition of hierarchies, hyper-entities, hyper-relations, and rules to establish detailed knowledge models. With its intelligent language, Grakn executes logical inferences on data types, relationships, attributes, and intricate patterns in real-time across distributed and stored data. It also offers built-in distributed analytics algorithms, such as Pregel and MapReduce, which can be accessed using straightforward queries within the language. The system provides a high level of abstraction over low-level patterns, simplifying the expression of complex constructs while optimizing query execution automatically. By utilizing Grakn KGMS and Workbase, enterprises can effectively scale their knowledge graphs. Furthermore, this distributed database is engineered to function efficiently across a network of computers through techniques like partitioning and replication, ensuring seamless scalability and performance.

JanusGraph stands out as a highly scalable graph database designed for efficiently storing and querying extensive graphs that can comprise hundreds of billions of vertices and edges, all managed across a cluster of multiple machines. This project, which operates under The Linux Foundation, boasts contributions from notable organizations such as Expero, Google, GRAKN.AI, Hortonworks, IBM, and Amazon. It offers both elastic and linear scalability to accommodate an expanding data set and user community. Key features include robust data distribution and replication methods to enhance performance and ensure fault tolerance. Additionally, JanusGraph supports multi-datacenter high availability and provides hot backups for data security. All these capabilities are available without any associated costs, eliminating the necessity for purchasing commercial licenses, as it is entirely open source and governed by the Apache 2 license. Furthermore, JanusGraph functions as a transactional database capable of handling thousands of simultaneous users performing complex graph traversals in real time. It ensures support for both ACID properties and eventual consistency, catering to various operational needs. Beyond online transactional processing (OLTP), JanusGraph also facilitates global graph analytics (OLAP) through its integration with Apache Spark, making it a versatile tool for data analysis and visualization. This combination of features makes JanusGraph a powerful choice for organizations looking to leverage graph data effectively.

RocksDB is a high-performance database engine that employs a log-structured design and is entirely implemented in C++. It treats keys and values as byte streams of arbitrary sizes, allowing for flexibility in data representation. Specifically designed for rapid, low-latency storage solutions such as flash memory and high-speed disks, RocksDB capitalizes on the impressive read and write speeds provided by these technologies. The database supports a range of fundamental operations, from basic tasks like opening and closing a database to more complex functions such as merging and applying compaction filters. Its versatility makes RocksDB suitable for various workloads, including database storage engines like MyRocks as well as application data caching and embedded systems. This adaptability ensures that developers can rely on RocksDB for a wide spectrum of data management needs in different environments.

Introducing a premier high-performance distributed SQL database that is open source and designed specifically for cloud-native environments, ideal for powering applications on a global internet scale. Experience minimal latency, often in the single-digit milliseconds, allowing you to create incredibly fast cloud applications by executing queries directly from the database itself. Handle immense workloads effortlessly, achieving millions of transactions per second and accommodating several terabytes of data on each node. With geo-distribution capabilities, you can deploy your database across various regions and cloud platforms, utilizing synchronous or multi-master replication for optimal performance. Tailored for modern cloud-native architectures, YugabyteDB accelerates the development, deployment, and management of applications like never before. Enjoy enhanced developer agility by tapping into the full capabilities of PostgreSQL-compatible SQL alongside distributed ACID transactions. Maintain resilient services with assured continuous availability, even amidst failures in compute, storage, or network infrastructure. Scale your resources on demand, easily adding or removing nodes as needed, and eliminate the necessity for over-provisioned clusters. Additionally, benefit from significantly reduced user latency, ensuring a seamless experience for your app users.