Alternatives to Memgraph

StarTree Cloud is a fully-managed real-time analytics platform designed for OLAP at massive speed and scale for user-facing applications. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, scalable upserts, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment. StarTree Cloud includes StarTree Data Manager, which allows you to ingest data from both real-time sources such as Amazon Kinesis, Apache Kafka, Apache Pulsar, or Redpanda, as well as batch data sources such as data warehouses like Snowflake, Delta Lake or Google BigQuery, or object stores like Amazon S3, Apache Flink, Apache Hadoop, or Apache Spark. StarTree ThirdEye is an add-on anomaly detection system running on top of StarTree Cloud that observes your business-critical metrics, alerting you and allowing you to perform root-cause analysis — all in real-time.

RaimaDB, an embedded time series database that can be used for Edge and IoT devices, can run in-memory. It is a lightweight, secure, and extremely powerful RDBMS. It has been field tested by more than 20 000 developers around the world and has been deployed in excess of 25 000 000 times. RaimaDB is a high-performance, cross-platform embedded database optimized for mission-critical applications in industries such as IoT and edge computing. Its lightweight design makes it ideal for resource-constrained environments, supporting both in-memory and persistent storage options. RaimaDB offers flexible data modeling, including traditional relational models and direct relationships through network model sets. With ACID-compliant transactions and advanced indexing methods like B+Tree, Hash Table, R-Tree, and AVL-Tree, it ensures data reliability and efficiency. Built for real-time processing, it incorporates multi-version concurrency control (MVCC) and snapshot isolation, making it a robust solution for applications demanding speed and reliability.

Amazon ElastiCache enables users to effortlessly establish, operate, and expand widely-used open-source compatible in-memory data stores in the cloud environment. It empowers the development of data-driven applications or enhances the efficiency of existing databases by allowing quick access to data through high throughput and minimal latency in-memory stores. This service is particularly favored for various real-time applications such as caching, session management, gaming, geospatial services, real-time analytics, and queuing. With fully managed options for Redis and Memcached, Amazon ElastiCache caters to demanding applications that necessitate response times in the sub-millisecond range. Functioning as both an in-memory data store and a cache, it is designed to meet the needs of applications that require rapid data retrieval. Furthermore, by utilizing a fully optimized architecture that operates on dedicated nodes for each customer, Amazon ElastiCache guarantees incredibly fast and secure performance for its users' critical workloads. This makes it an essential tool for businesses looking to enhance their application's responsiveness and scalability.

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.

Aerospike is the global leader for next-generation, real time NoSQL data solutions at any scale. Aerospike helps enterprises overcome seemingly impossible data bottlenecks and compete with other companies at a fraction of the cost and complexity of legacy NoSQL databases. Aerospike's Hybrid Memory Architecture™ is a patented technology that unlocks the full potential of modern hardware and delivers previously unimaginable value. It does this by delivering unimaginable value from huge amounts of data at both the edge, core, and in the cloud. Aerospike empowers customers with the ability to instantly combat fraud, dramatically increase shopping cart sizes, deploy global digital payment networks, and provide instant, one-to-1 personalization for millions. Aerospike customers include Airtel and Banca d'Italia as well as Snap, Verizon Media, Wayfair, PayPal, Snap, Verizon Media, and Nielsen. The company's headquarters is in Mountain View, California. Additional locations are in London, Bengaluru, India, and Tel Aviv in Israel.

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.

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.

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.

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.

Oracle's database offerings provide clients with cost-effective and high-efficiency options, including the renowned multi-model database management system, as well as in-memory, NoSQL, and MySQL databases. The Oracle Autonomous Database, which can be accessed on-premises through Oracle Cloud@Customer or within the Oracle Cloud Infrastructure, allows users to streamline their relational database systems and lessen management burdens. By removing the intricacies associated with operating and securing Oracle Database, Oracle Autonomous Database ensures customers experience exceptional performance, scalability, and reliability. Furthermore, organizations concerned about data residency and network latency can opt for on-premises deployment of Oracle Database. Additionally, clients who rely on specific versions of Oracle databases maintain full authority over their operational versions and the timing of any updates. This flexibility empowers businesses to tailor their database environments according to their unique requirements.

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.

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.

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.

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.

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.

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.

Dragonfly serves as a seamless substitute for Redis, offering enhanced performance while reducing costs. It is specifically engineered to harness the capabilities of contemporary cloud infrastructure, catering to the data requirements of today’s applications, thereby liberating developers from the constraints posed by conventional in-memory data solutions. Legacy software cannot fully exploit the advantages of modern cloud technology. With its optimization for cloud environments, Dragonfly achieves an impressive 25 times more throughput and reduces snapshotting latency by 12 times compared to older in-memory data solutions like Redis, making it easier to provide the immediate responses that users demand. The traditional single-threaded architecture of Redis leads to high expenses when scaling workloads. In contrast, Dragonfly is significantly more efficient in both computation and memory usage, potentially reducing infrastructure expenses by up to 80%. Initially, Dragonfly scales vertically, only transitioning to clustering when absolutely necessary at a very high scale, which simplifies the operational framework and enhances system reliability. Consequently, developers can focus more on innovation rather than infrastructure management.

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.

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.

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.

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.

*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.

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.

Aiven takes the reins on your open-source data infrastructure hosted in the cloud, allowing you to focus on what you excel at: developing applications. While you channel your energy into innovation, we expertly handle the complexities of managing cloud data infrastructure. Our solutions are entirely open source, providing the flexibility to transfer data between various clouds or establish multi-cloud setups. You will have complete visibility into your expenses, with a clear understanding of costs as we consolidate networking, storage, and basic support fees. Our dedication to ensuring your Aiven software remains operational is unwavering; should any challenges arise, you can count on us to resolve them promptly. You can launch a service on the Aiven platform in just 10 minutes and sign up without needing to provide credit card information. Simply select your desired open-source service along with the cloud and region for deployment, pick a suitable plan—which includes $300 in free credits—and hit "Create service" to begin configuring your data sources. Enjoy the benefits of maintaining control over your data while leveraging robust open-source services tailored to your needs. With Aiven, you can streamline your cloud operations and focus on driving your projects forward.

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.

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.

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.

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.

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.

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.

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.

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://ancillary-proxy.atarimworker.io?url=https%3A%2F%2Fblog.recallgraph.tech%2Fnever-lose-your-old-data-again.

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.

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.

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.

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.

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.

Symas LMDB is an incredibly swift and memory-efficient database that we created specifically for the OpenLDAP Project. Utilizing memory-mapped files, it achieves the read speed typical of purely in-memory databases while also providing the durability associated with traditional disk-based systems. In essence, despite its modest size of just 32KB of object code, LMDB packs a significant punch; it is indeed the perfect 32KB. The compact nature and efficiency of LMDB are integral to its remarkable capabilities. For those integrating LMDB into their applications, Symas provides fixed-price commercial support. Development is actively carried out in the mdb.master branch of the OpenLDAP Project’s git repository. Moreover, LMDB has garnered attention across numerous impressive products and publications, highlighting its versatility and effectiveness in various contexts. Its widespread recognition further cements its status as a vital tool for developers.

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.

A user-friendly, robust, and dependable system for data processing and distribution is offered by Apache NiFi, which facilitates the creation of efficient and scalable directed graphs for routing, transforming, and mediating data. Among its various high-level functions and goals, Apache NiFi provides a web-based user interface that ensures an uninterrupted experience for design, control, feedback, and monitoring. It is designed to be highly configurable, loss-tolerant, and capable of low latency and high throughput, while also allowing for dynamic prioritization of data flows. Additionally, users can alter the flow in real-time, manage back pressure, and trace data provenance from start to finish, as it is built with extensibility in mind. You can also develop custom processors and more, which fosters rapid development and thorough testing. Security features are robust, including SSL, SSH, HTTPS, and content encryption, among others. The system supports multi-tenant authorization along with internal policy and authorization management. Also, NiFi consists of various web applications, such as a web UI, web API, documentation, and custom user interfaces, necessitating the configuration of your mapping to the root path for optimal functionality. This flexibility and range of features make Apache NiFi an essential tool for modern data workflows.

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.

SingleStore, previously known as MemSQL, is a highly scalable and distributed SQL database that can operate in any environment. It is designed to provide exceptional performance for both transactional and analytical tasks while utilizing well-known relational models. This database supports continuous data ingestion, enabling operational analytics critical for frontline business activities. With the capacity to handle millions of events each second, SingleStore ensures ACID transactions and allows for the simultaneous analysis of vast amounts of data across various formats, including relational SQL, JSON, geospatial, and full-text search. It excels in data ingestion performance at scale and incorporates built-in batch loading alongside real-time data pipelines. Leveraging ANSI SQL, SingleStore offers rapid query responses for both current and historical data, facilitating ad hoc analysis through business intelligence tools. Additionally, it empowers users to execute machine learning algorithms for immediate scoring and conduct geoanalytic queries in real-time, thereby enhancing decision-making processes. Furthermore, its versatility makes it a strong choice for organizations looking to derive insights from diverse data types efficiently.

Oracle TimesTen In-Memory Database (TimesTen) enhances real-time application performance by rethinking the runtime data storage approach, resulting in reduced response times and increased throughput. By utilizing in-memory data management and refining data structures alongside access algorithms, TimesTen maximizes the efficiency of database operations, leading to significant improvements in both responsiveness and transaction throughput. The launch of TimesTen Scaleout introduces a shared-nothing architecture that builds on the existing in-memory capabilities, enabling seamless scaling across numerous hosts, accommodating vast data volumes of hundreds of terabytes, and processing hundreds of millions of transactions per second, all without requiring manual sharding or workload distribution. This innovative approach not only streamlines performance but also simplifies the overall database management experience for users.

HarperDB is an innovative platform that integrates database management, caching, application development, and streaming capabilities into a cohesive system. This allows businesses to efficiently implement global-scale back-end services with significantly reduced effort, enhanced performance, and cost savings compared to traditional methods. Users can deploy custom applications along with pre-existing add-ons, ensuring a high-throughput and ultra-low latency environment for their data needs. Its exceptionally fast distributed database offers vastly superior throughput rates than commonly used NoSQL solutions while maintaining unlimited horizontal scalability. Additionally, HarperDB supports real-time pub/sub communication and data processing through protocols like MQTT, WebSocket, and HTTP. This means organizations can leverage powerful data-in-motion functionalities without the necessity of adding extra services, such as Kafka, to their architecture. By prioritizing features that drive business growth, companies can avoid the complexities of managing intricate infrastructures. While you can’t alter the speed of light, you can certainly minimize the distance between your users and their data, enhancing overall efficiency and responsiveness. In doing so, HarperDB empowers businesses to focus on innovation and progress rather than getting bogged down by technical challenges.