Alternatives to Graph Engine

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.

Sparksee, which was previously referred to as DEX, optimizes both space and performance while maintaining a compact design that enables swift analysis of extensive networks. It supports a wide range of programming languages including .Net, C++, Python, Objective-C, and Java, making it versatile across various operating systems. The graph data is efficiently organized using bitmap data structures, achieving significant compression ratios. These bitmaps are divided into chunks that align with disk pages, enhancing input/output locality for better performance. By leveraging bitmaps, computations are executed using binary logic instructions that facilitate efficient processing in pipelined architectures. The system features complete native indexing, which ensures rapid access to all graph data structures. Node connections are also encoded as bitmaps, further reducing their storage footprint. Advanced I/O strategies are implemented to minimize the frequency of data pages being loaded into memory, ensuring optimal resource usage. Each unique value in the database is stored only once, effectively eliminating unnecessary redundancy, and contributing to overall efficiency. This combination of features makes Sparksee a powerful tool for handling large-scale graph data analyses.

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.

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.

Graph databases, which are a key feature of Oracle's converged database solution, remove the necessity for establishing a distinct database and transferring data. This allows analysts and developers to conduct fraud detection in the banking sector, uncover relationships and links to data, and enhance traceability in smart manufacturing, all while benefiting from enterprise-level security, straightforward data ingestion, and robust support for various data workloads. The Oracle Autonomous Database incorporates Graph Studio, offering one-click setup, built-in tools, and advanced security measures. Graph Studio streamlines the management of graph data and facilitates the modeling, analysis, and visualization throughout the entire graph analytics lifecycle. Oracle supports both property and RDF knowledge graphs, making it easier to model relational data as graph structures. Additionally, interactive graph queries can be executed directly on the graph data or via a high-performance in-memory graph server, enabling efficient data processing and analysis. This integration of graph technology enhances the overall capabilities of data management within Oracle's ecosystem.

OrigoDB allows you to create high-quality, mission-critical systems that deliver real-time performance while significantly reducing time and costs. This is not just empty advertising! We encourage you to continue reading for a straightforward overview of our features. Feel free to reach out with any inquiries or download and give it a try today! Operations performed in memory are significantly quicker than those conducted on disk. An individual OrigoDB engine is capable of processing millions of read transactions and thousands of write transactions per second, all while ensuring synchronous command journaling to a local SSD. This capability is the primary motivation behind the development of OrigoDB. Utilizing a single object-oriented domain model is much more efficient than managing an entire stack that encompasses a relational model, object/relational mapping, data access code, views, and stored procedures, which can lead to unnecessary complexity! Furthermore, the OrigoDB engine provides complete ACID compliance right from the start. Commands are executed sequentially, allowing the in-memory model to transition smoothly from one consistent state to another, ensuring data integrity at all times. This streamlined approach ultimately enhances overall system performance and reliability.

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.

Experience powerful graph analytics with simplicity through Aster SQL-GR™, a dedicated graph processing engine designed for tackling intricate business challenges like social network and influencer assessment, fraud detection, supply chain oversight, network evaluation, threat identification, and money laundering—issues that extend beyond fundamental graph navigation. Utilizing the Bulk Synchronous Processing (BSP) model, SQL-GR employs massively iterative, distributed, and parallel processing techniques to effectively address complex graph-related problems. This innovative platform is incredibly scalable, leveraging Teradata Aster’s massively parallel processing (MPP) architecture to distribute graph computations across numerous servers and nodes. Furthermore, SQL-GR operates without the constraints of memory limits or dependence on a single server, allowing users to harness exceptional power and speed for extensive graph analysis at a big data scale. In a world where data complexity is ever-increasing, SQL-GR stands out as an essential tool for businesses aiming to derive meaningful insights from their data.

The TigerGraph™, a graph platform based on its Native Parallel Graph™, technology, represents the next evolution in graph database evolution. It is a complete, distributed parallel graph computing platform that supports web-scale data analytics in real time. Combining the best ideas (MapReduce, Massively Parallel Processing, and fast data compression/decompression) with fresh development, TigerGraph delivers what you've been waiting for: the speed, scalability, and deep exploration/querying capability to extract more business value from your data.

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.

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.

HugeGraph is a high-performance and scalable graph database capable of managing billions of vertices and edges efficiently due to its robust OLTP capabilities. This database allows for seamless storage and querying, making it an excellent choice for complex data relationships. It adheres to the Apache TinkerPop 3 framework, enabling users to execute sophisticated graph queries using Gremlin, a versatile graph traversal language. Key features include Schema Metadata Management, which encompasses VertexLabel, EdgeLabel, PropertyKey, and IndexLabel, providing comprehensive control over graph structures. Additionally, it supports Multi-type Indexes that facilitate exact queries, range queries, and complex conditional queries. The platform also boasts a Plug-in Backend Store Driver Framework that currently supports various databases like RocksDB, Cassandra, ScyllaDB, HBase, and MySQL, while also allowing for easy integration of additional backend drivers as necessary. Moreover, HugeGraph integrates smoothly with Hadoop and Spark, enhancing its data processing capabilities. By drawing on the storage structure of Titan and the schema definitions from DataStax, HugeGraph offers a solid foundation for effective graph database management. This combination of features positions HugeGraph as a versatile and powerful solution for handling complex graph data scenarios.

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.

Gain a competitive advantage by leveraging the capabilities of vector search technology. Optimize your AI/ML pipeline processes, minimize infrastructure expenses, and maintain a leading position with a remarkable improvement in time-to-market efficiency, achieving speeds up to 10 times faster. Eliminate data silos with ApertureDB's comprehensive multimodal data management system, empowering your AI teams to drive innovation. Establish and expand intricate multimodal data infrastructures capable of handling billions of objects across your organization in mere days instead of months. By integrating multimodal data, sophisticated vector search, and a groundbreaking knowledge graph, along with a robust query engine, you can accelerate the development of AI applications at scale for your enterprise. ApertureDB promises to boost the efficiency of your AI/ML teams and enhance the returns on your AI investments, utilizing all available data effectively. Experience it firsthand by trying it for free or arranging a demo to witness its capabilities. Discover pertinent images by leveraging labels, geolocation, and specific regions of interest, while also preparing extensive multi-modal medical scans for machine learning and clinical research endeavors. The platform not only streamlines data management but also enhances collaboration and insight generation across your organization.

Blazegraph™ DB is an exceptionally high-performance graph database that offers support for Blueprints, along with RDF and SPARQL APIs. Capable of handling up to 50 billion edges on a single server, it has been adopted by numerous Fortune 500 companies, including EMC and Autodesk. This database is integral to various Precision Medicine applications and enjoys extensive use in the life sciences sector. Additionally, it plays a crucial role in cyber analytics for both commercial enterprises and government agencies. Moreover, Blazegraph powers the Wikidata Query Service for the Wikimedia Foundation. Users have the option to download it as an executable jar, a war file, or a tar.gz distribution. Designed with user-friendliness in mind, Blazegraph allows for a quick start, although it comes with SSL and authentication turned off by default. For those deploying in a production environment, it is highly advisable to activate SSL, establish authentication, and implement suitable network configurations to ensure security. Below, you will find valuable resources to assist you in making these configurations effectively. Furthermore, the documentation provides a comprehensive guide for new users to navigate setup and support effectively.

Apache TinkerPop™ serves as a framework for graph computing, catering to both online transaction processing (OLTP) with graph databases and online analytical processing (OLAP) through graph analytic systems. The traversal language utilized within Apache TinkerPop is known as Gremlin, which is a functional, data-flow language designed to allow users to effectively articulate intricate traversals or queries related to their application's property graph. Each traversal in Gremlin consists of a series of steps that can be nested. In graph theory, a graph is defined as a collection of vertices and edges. Both these components can possess multiple key/value pairs referred to as properties. Vertices represent distinct entities, which may include individuals, locations, or events, while edges signify the connections among these vertices. For example, one individual might have connections to another, have participated in a certain event, or have been at a specific location recently. This framework is particularly useful when a user's domain encompasses a diverse array of objects that can be interconnected in various ways. Moreover, the versatility of Gremlin enhances the ability to navigate complex relationships within the graph structure seamlessly.

Neo4j's graph platform is designed to help you leverage data and data relationships. Developers can create intelligent applications that use Neo4j to traverse today's interconnected, large datasets in real-time. Neo4j's graph database is powered by a native graph storage engine and processing engine. It provides unique, actionable insights through an intuitive, flexible, and secure database.

PuppyGraph allows you to effortlessly query one or multiple data sources through a cohesive graph model. Traditional graph databases can be costly, require extensive setup time, and necessitate a specialized team to maintain. They often take hours to execute multi-hop queries and encounter difficulties when managing datasets larger than 100GB. Having a separate graph database can complicate your overall architecture due to fragile ETL processes, ultimately leading to increased total cost of ownership (TCO). With PuppyGraph, you can connect to any data source, regardless of its location, enabling cross-cloud and cross-region graph analytics without the need for intricate ETLs or data duplication. By directly linking to your data warehouses and lakes, PuppyGraph allows you to query your data as a graph without the burden of constructing and maintaining lengthy ETL pipelines typical of conventional graph database configurations. There's no longer a need to deal with delays in data access or unreliable ETL operations. Additionally, PuppyGraph resolves scalability challenges associated with graphs by decoupling computation from storage, allowing for more efficient data handling. This innovative approach not only enhances performance but also simplifies your data management strategy.

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.

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.

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.

Create high-quality, essential applications that deliver real-time performance while significantly reducing both time and costs. Memstate is a groundbreaking solution that streamlines data management. The traditional process of transferring data between disk and RAM is not only highly inefficient, but it also necessitates numerous layers of complex software that can be entirely removed. With Memstate, you can organize and handle your data in memory, achieving seamless persistence, concurrency control, and transactions that meet rigorous ACID standards. This approach can enhance your application’s speed by 100 times and boost developer productivity by tenfold. Although Memstate can be applied in various scenarios, it is primarily tailored to manage complex Online Transaction Processing (OLTP) workloads commonly found in enterprise applications. In-memory operations provide a performance advantage that is exponentially greater than that of disk-based operations. A single Memstate engine is capable of processing millions of read transactions and tens of thousands of write transactions each second, all while maintaining submillisecond latency, making it an exceptional choice for high-demand environments. Embrace this innovative technology to elevate your applications and maximize efficiency across the board.

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.

Organizations that choose a do-it-yourself method for implementing a graph database should anticipate a timeline of about 2 to 3 months to achieve a production-ready state. In contrast, with Graph Story’s managed services, your operational database can be set up in just minutes. Discover various graph use cases and explore a side-by-side analysis of self-hosting versus managed services. We can accommodate deployments in your existing infrastructure, whether it's on AWS, Azure, or Google Compute Engine, in any geographical location. If you require VPC peering or IP access restrictions, we can easily adapt to your needs. For those looking to create a proof of concept, initiating a single enterprise graph instance only takes a few clicks. Should you need to scale up to a high-availability, production-ready cluster on demand, we are prepared to assist! Our graph database management tools are designed to simplify your experience, allowing you to monitor CPU, memory, and disk usage effortlessly. You also have access to configurations, logs, and the ability to backup your database and restore snapshots whenever necessary. This level of flexibility ensures that your graph database management aligns perfectly with your operational 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.

To fully appreciate the significance of ever-changing business data, it is essential to grasp the intricate connections within that data on a deeper level. In contrast to traditional databases, a graph database prioritizes these relationships, employing Graph theory and Linear Algebra to navigate and illustrate the interconnections among complex data networks, sources, and points. The TIBCO® Graph Database empowers users to uncover, store, and transform intricate dynamic data into actionable insights. This platform enables users to swiftly create data and computational models that foster dynamic interactions across various organizational silos. By leveraging knowledge graphs, organizations can derive immense value by linking their diverse data assets and uncovering relationships that enhance the optimization of resources and workflows. Furthermore, the combination of OLTP and OLAP capabilities within a single, robust enterprise database provides a comprehensive solution. With optimistic ACID transaction properties integrated alongside native storage and access, businesses can confidently manage their data-driven operations. Ultimately, this advanced technology not only simplifies data management but also paves the way for innovative decision-making processes.

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.

GUN is a real-time, decentralized, and offline-first graph database engine that allows for seamless data storage, loading, and sharing within applications without the need to manage servers, network requests, or handle issues related to offline changes or concurrency conflicts. This lightweight, efficient data synchronization and storage system operates wherever JavaScript is supported, enabling developers to concentrate on their app's data needs rather than technical hurdles. By using GUN, you can rapidly create innovative applications while harnessing powerful internet capabilities such as decentralization and genuine privacy, ultimately contributing to a freer and more open web. GUN serves as a versatile database engine that operates across browsers, mobile devices, and servers, empowering you to configure your ideal data management system. With GUN, you can build applications with enhanced functionality while ensuring user privacy and eliminating dependencies on traditional infrastructures.

RecallGraph is a versioned graph data store. It retains all changes its data (vertices, edges) have undergone to get to their current state. It supports point-in time graph traversals that allow the user to query any past state of a graph as well as the present. RecallGraph can be used in situations where data is best represented using a network of edges and vertices (i.e., as a graph). 1. Both edges and vertices can contain properties in the form attribute/value pairs (equivalent of JSON objects). 2. Documents (vertices/edges), can change throughout their lives (both in their individual attributes/values as well as in their relationships to each other). 3. Documents from the past are just as important as their current states, so it is essential to retain and queryable their change history. Also see this blog post for an intro - https://blog.recallgraph.tech/never-lose-your-old-data-again.

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.

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.

KgBase, short for Knowledge Graph Base, is a powerful collaborative database that features version control, analytics, and visualization tools. It enables individuals and communities to craft knowledge graphs that help derive insights from their data. Users can seamlessly import CSV files and spreadsheets or utilize the API for collaborative data work. With KgBase, you can create knowledge graphs without any coding, thanks to an intuitive user interface that allows for easy navigation of the graph and the display of results in tables, charts, and more. Engage with your graph data interactively; as you construct queries, the results are updated in real time, making the process much simpler than traditional query languages like Cypher or Gremlin. Additionally, your graph data can be represented in tabular form, so you can easily explore all results, regardless of the dataset size. KgBase is designed to handle both extensive graphs with millions of nodes and smaller projects effortlessly. Whether you prefer cloud hosting or self-hosting, it supports a diverse range of databases. You can introduce graph capabilities to your organization by starting with pre-existing templates. Moreover, any query results can be quickly transformed into visual chart representations, enhancing the interpretability of your data insights. This flexibility and ease of use make KgBase an ideal choice for anyone looking to leverage the power of knowledge graphs in their data analysis endeavors.

Virtuoso Universal Server represents a cutting-edge platform that leverages established open standards and utilizes Hyperlinks as Super Keys to dismantle data silos that hinder both user engagement and enterprise efficiency. With Virtuoso, users can effortlessly create financial profile knowledge graphs based on near real-time financial activities, significantly lowering the costs and complexity involved in identifying fraudulent behavior patterns. Thanks to its robust, secure, and scalable database management system, it allows for intelligent reasoning and inference to unify fragmented identities through personally identifiable information such as email addresses, phone numbers, social security numbers, and driver's licenses, facilitating the development of effective fraud detection solutions. Additionally, Virtuoso empowers users to craft impactful applications powered by knowledge graphs sourced from diverse life sciences-related data sets, thereby enhancing the overall analytical capabilities in that field. This innovative approach not only streamlines the processes involved in fraud detection but also opens new avenues for data utilization across various sectors.

Data engineers and scientists can be 95% better at their jobs with ready access to the most flexible semantic layer, explainable AI and reusable data modelling. They can create and expand semantic models, understand data interrelationships, and run federated query to speed up time to insight. Stardog's graph data virtualization and high performance graph database are the best available -- at a price that is up to 57x less than competitors -- to connect any data source, warehouse, or enterprise data lakehouse without copying or moving data. Scale users and use cases at a lower infrastructure cost. Stardog's intelligent inference engine applies expert knowledge dynamically at query times to uncover hidden patterns and unexpected insights in relationships that lead to better data-informed business decisions and outcomes.

data.world is a cloud-native service meticulously designed for contemporary data architectures, ensuring seamless management of updates, migrations, and ongoing maintenance. This streamlined setup process is complemented by a vast and expanding ecosystem of pre-built integrations with all major cloud data warehouses. When prompt results are essential, your team should concentrate on addressing genuine business challenges rather than grappling with cumbersome data management software. data.world simplifies the process for all users, not just data experts, enabling them to obtain clear, precise, and prompt answers to various business inquiries. Our platform features a cloud-based data catalog that connects isolated and distributed data to well-known business concepts, fostering a cohesive knowledge base that everyone can access, comprehend, and utilize. Furthermore, beyond our enterprise solutions, data.world hosts the largest collaborative open data community globally, where individuals collaborate on diverse projects ranging from social bot detection to acclaimed data journalism initiatives, promoting innovation and shared learning. This unique environment encourages knowledge sharing and empowers users to leverage data in creative and impactful ways.

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.

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.

GraphBase is a Graph Database Management System designed to streamline the development and upkeep of intricate data graphs. While Relational Database Management Systems often struggle with complex and interconnected structures, graph databases offer superior modeling capabilities, enhanced performance, and greater scalability. The existing range of graph database solutions, including triplestores and property graphs, has been available for almost twenty years; although they are effective tools with diverse applications, they still fall short in managing intricate data structures. With the introduction of GraphBase, we aimed to facilitate the handling of complex data architectures, allowing your information to evolve into something greater—Knowledge. We accomplished this by reimagining the management of graph data, ensuring that the graph is prioritized as a fundamental component. In GraphBase, users benefit from a graph equivalent of the familiar "rows and tables" framework, which contributes to the user-friendly nature of Relational Databases, making it easier to navigate and manipulate data. Ultimately, GraphBase transforms how organizations view and interact with their data, paving the way for innovative possibilities.

RelationalAI represents a cutting-edge database system tailored for advanced data applications that leverage relational knowledge graphs. By focusing on data-centric application design, it effectively merges data with logic into modular models. These intelligent applications possess the capability to comprehend and utilize every relationship present within a model. The system employs a knowledge graph framework that allows for the articulation of knowledge in the form of executable models. These models offer the benefit of being easily expanded through declarative programs that are accessible and understandable to humans. With RelationalAI's versatile and expressive declarative language, developers can achieve a remarkable reduction in code size, ranging from 10 to 100 times less. This accelerates the development of applications and enhances their quality by involving non-technical users in the creation process while automating complex programming tasks. By leveraging the adaptable graph data model, users can build a robust data-centric architecture. Additionally, the integration of models paves the way for the exploration of new relationships, effectively dismantling barriers that exist between various applications. Ultimately, this innovative approach not only streamlines development but also fosters collaboration across different domains.

Fluree is an immutable RDF graph database written in Clojure and adhering to W3C standards, supporting JSON and JSON-LD while accommodating various RDF ontologies. It operates with an immutable ledger that secures transactions with cryptographic integrity, alongside a rich RDF graph database capable of various queries. It employs SmartFunctions for enforcing data management rules, including identity and access management and data quality. Additionally, It boasts a scalable, cloud-native architecture utilizing a lightweight Java runtime, with individually scalable ledger and graph database components, embodying a "Data-Centric" ideology that treats data as a reusable asset independent of singular applications.

Locstat is a cutting-edge graph intelligence platform that offers tailored industry-specific and point solutions through the integration of graph-based AI, analytics, and event processing, allowing organizations to swiftly enhance their data solutions for the future. Studies indicate that embracing forward-thinking, AI-enhanced digitalization strategies can yield substantial advantages and improvements for organizations. Our success in boosting customer efficiencies has led to notable returns on investment, corroborated by independent research firms. This underscores the effectiveness of advanced analytics technologies in addressing the intricate challenges of today more economically compared to traditional relational database solutions. As the demand for innovative data strategies grows, organizations that leverage these advanced tools can position themselves for long-term success.

RushDB is an innovative, open-source graph database that requires no configuration and rapidly converts JSON and CSV files into a fully normalized, queryable Neo4j graph, all while avoiding the complexities associated with schema design, migrations, and manual indexing. Tailored for contemporary applications as well as AI and machine learning workflows, RushDB offers an effortless experience for developers, merging the adaptability of NoSQL with the organized capabilities of relational databases. By incorporating automatic data normalization, ensuring ACID compliance, and featuring a robust API, RushDB streamlines the often challenging processes of data ingestion, relationship management, and query optimization, allowing developers to direct their energies toward building applications rather than managing databases. Some notable features include: 1. Instantaneous data ingestion without the need for configuration 2. Storage and querying capabilities powered by graph technology 3. Support for ACID transactions and seamless schema evolution 4. A developer-friendly API that facilitates querying akin to an SDK 5. High-performance capabilities for search and analytics 6. Flexibility to be self-hosted or cloud-compatible. This combination of features positions RushDB as a transformative solution in the realm of data management.

IBM Cloud Databases serve as open source data repositories tailored for the development of enterprise applications. Leveraging a Kubernetes-based architecture, these databases support serverless application development. They are engineered to enhance storage and computing capabilities effortlessly, free from the restrictions typically imposed by single server environments. Fully integrated within the IBM Cloud console, they offer a unified approach to consumption, pricing, and user interaction. The goal is to deliver a streamlined experience for developers, encompassing features such as access control, backup orchestration, encryption key management, and comprehensive auditing, monitoring, and logging functionalities. This cohesive framework not only enhances usability but also ensures that developers can focus on building innovative solutions without worrying about underlying infrastructure constraints.

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.

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.

*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.

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.

Graphlytic is a web-based BI platform that allows knowledge graph visualization and analysis. Interactively explore the graph and look for patterns using the Cypher query language or query templates for non-technical users. Users can also use filters to find answers to any graph question. The graph visualization provides deep insights into industries such as scientific research and anti-fraud investigation. Even users with little knowledge of graph theory can quickly explore the data. Cytoscape.js allows graph rendering. It can render tens to thousands of nodes and hundreds upon thousands of relationships. The application is available in three formats: Desktop, Cloud, or Server. Graphlytic Desktop is a Neo4j Desktop app that can be installed in just a few mouse clicks. Cloud instances are great for small teams who don't want or need to worry about installing and need to be up and running quickly.

Luna offers a subscription service for Apache Cassandra support and expertise through DataStax, allowing users to take advantage of open-source Cassandra while benefiting from the knowledge and experience of the team that created most of its code and has supported some of the largest deployments globally. With Luna, you gain access to best practices, expert advice, and SLA-based support to ensure your Cassandra setup remains optimal. You can scale your operations without sacrificing performance or latency, effectively managing even the most demanding real-time workloads with ease. The service empowers you to create dynamic and highly-interactive customer experiences with exceptionally fast read and write capabilities. Additionally, Luna helps resolve issues and adhere to best practices in managing Cassandra clusters. The support provided encompasses the entire application life cycle, facilitating a deeper collaboration with your team throughout the implementation process, ensuring that your needs are met at every stage. Ultimately, Luna enhances your ability to harness the full potential of Cassandra for your business objectives.

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.