Alternatives to VelocityDB

ArcadeDB allows you to manage complex models without any compromises. Polyglot Persistence is gone. There is no need to have multiple databases. ArcadeDB Multi-Model databases can store graphs and documents, key values, time series, and key values. Each model is native to the database engine so you don't need to worry about translations slowing down your computer. ArcadeDB's engine was developed with Alien Technology. It can crunch millions upon millions of records per second. ArcadeDB's traversing speed does not depend on the size of the database. It doesn't matter if your database contains a few records or a billion. ArcadeDB can be used as an embedded database on a single server. It can scale up by using Kubernetes to connect multiple servers. It is flexible enough to run on any platform that has a small footprint. Your data is protected. Our unbreakable fully transactional engine ensures durability for mission-critical production database databases. ArcadeDB uses the Raft Consensus Algorithm in order to maintain consistency across multiple servers.

It is a highly scalable, embedded SQL database that can be accessed from anywhere. It also includes commercial-grade data security, disaster recovery, change synchronization, and data security.

OrientDB is the fastest graph database in the world. Period. A benchmark study by IBM and Tokyo Institute of Technology found that OrientDB is 10x more efficient than Neo4j for graph operations. This applies to all workloads. OrientDB can help you gain competitive advantage and increase innovation through new revenue streams.

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.

LanceDB is an open-source database for AI that is developer-friendly. LanceDB provides the best foundation for AI applications. From hyperscalable vector searches and advanced retrieval of RAG data to streaming training datasets and interactive explorations of large AI datasets. Installs in seconds, and integrates seamlessly with your existing data and AI tools. LanceDB is an embedded database with native object storage integration (think SQLite, DuckDB), which can be deployed anywhere. It scales down to zero when it's not being used. LanceDB is a powerful tool for rapid prototyping and hyper-scale production. It delivers lightning-fast performance in search, analytics, training, and multimodal AI data. Leading AI companies have indexed petabytes and billions of vectors, as well as text, images, videos, and other data, at a fraction the cost of traditional vector databases. More than just embedding. Filter, select and stream training data straight from object storage in order to keep GPU utilization at a high level.

Natively store data for graphs, documents and search needs. One query language allows for feature-rich access. You can map data directly to the database and access it using the best patterns for the job: traversals, joins search, ranking geospatial, aggregateions - you name them. Polyglot persistence without the cost. You can easily design, scale, and adapt your architectures to meet changing needs with less effort. Combine the flexibility and power of JSON with graph technology to extract next-generation features even from large datasets.

Perst, an object-oriented embedded database (ODBMS) from McObject, is open source and dual licensed. It is available as a Java-only embedded database and a C# version (for Microsoft's.NET Framework). Perst allows developers to store, sort and retrieve objects with minimal memory and storage overhead, while leveraging Java and C#'s object-oriented paradigm. Perst's performance advantage over Java or.NET embedded databases is evident in the TestIndex benchmarks and PolePosition benchmarks. Perst stores data in Java and.NET object, eliminating the need for translations required to store in relational or object-relational database. This increases performance at runtime. Perst is a core program that only has five thousand lines. The small footprint places minimal demands on the system resources.

Apache Phoenix combines the best of both worlds to enable OLTP and operational analysis in Hadoop. This allows for low-latency Hadoop applications. HBase is used as the backing store for Apache Phoenix, which combines the power of SQL and JDBC with ACID transaction support and flexibility of late bound, schema-on read capabilities from the NoSQL realm. Apache Phoenix is fully compatible with other Hadoop tools such as Spark and Hive. It also integrates with Pig, Flume and Map Reduce. Become the trusted Hadoop data platform for OLTP, operational analytics and Hadoop via well-defined APIs. Apache Phoenix compiles your SQL query into a series HBase scans and orchestrates their running to produce regular JDBC results sets. Direct use of HBase API along with coprocessors, custom filters and other tools results in performance of milliseconds or seconds for small queries.

H2, the Java SQL database, is your welcome. An embedded mode allows an application to open a database within the same JVM by using JDBC. This connection mode is the fastest and most convenient. However, a database can only be opened in one virtual machine (and a class loader) at a time. Both in-memory and persistent databases are supported, as in all modes. There is no limit to the number of databases that can be opened simultaneously or the number of connections. Mixed mode is a combination between the server and embedded modes. The first application to connect to a database uses embedded mode. However, it also starts a server so other applications (running in different processes and virtual machines) can simultaneously access the same data. The local connections are just as fast as if the data were used in embedded mode. Remote connections are slightly slower.

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.

HyperGraphDB is an open-source, general-purpose data storage system that uses a powerful knowledge management approach called directed hypergraphs. Although it is a persistent memory model, it can also serve as an embedded object-oriented data base for Java projects of any size. Or a graph database or a (non SQLL) relational database. HyperGraphDB is a storage system that uses generalized hypergraphs for its underlying data model. A tuple is a collection of 0 or more tuples. Each atom is a tuple of this type. The data model can be viewed as either relational, where higher-order, non-ary relationships are permitted, or graph-oriented where edges point to an arbitrary set nodes. Each atom is assigned a strongly-typed, arbitrary value. The hypergraph that manages these values is embedded in the type system and can be customized from the ground up.

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.

Actian Zen is a low-maintenance, embedded database management system that is high-performance and designed for edge applications, IoT, mobile devices and IoT environments. It provides developers with flexibility when working with unstructured and structured data. Actian Zen's small footprint, scalability and high reliability make it ideal for resource-constrained settings where consistent performance and minimal administration overhead are essential. It supports real-time analytics and data processing with built-in security and a self tuning architecture without the need for constant maintenance or monitoring. Actian Zen is used by many industries, including healthcare, retail and manufacturing, in which edge computing and distributed data environments play a critical role for business operations.

HugeGraph is a high-speed, highly-scalable graph database. HugeGraph's excellent OLTP capability allows for the storage and querying of billions of edges and vertices. Gremlin, a powerful graph traversal and query language, can handle complex graph queries in compliance with Apache TinkerPop 3. It supports Gremlin and is compliant to Apache TinkerPop 3. Schema Metadata Management includes VertexLabel EdgeLabel PropertyKey and IndexLabel. Multi-type Indexes that support complex combination queries, range query, and exact query. Plug-in Backend Store Driver Framework. Supports RocksDB, Cassandra and ScyllaDB. It is easy to add another backend store driver if necessary. Integration with Hadoop/Spark. HugeGraph is built on the TinkerPop framework. We refer to the storage structure and schema definition of DataStax.

The Open, Multi-Cloud Stack to Modern Data Apps. Built on Apache Cassandra™, an open-source Apache Cassandra™. Global scale and 100% uptime without vendor lock in You can deploy on multi-clouds, open-source, on-prem and Kubernetes. For a lower TCO, use elastic and pay-as you-go. Stargate APIs allow you to build faster with NoSQL, reactive, JSON and REST. Avoid the complexity of multiple OSS projects or APIs that don’t scale. It is ideal for commerce, mobile and AI/ML. Get building modern data applications with Astra, a database-as-a-service powered by Apache Cassandra™. Richly interactive apps that are viral-ready and elastic using REST, GraphQL and JSON. Pay-as you-go Apache Cassandra DBaaS which scales easily and affordably

InterSystems Cache®, a high-performance database, powers transaction processing applications all over the globe. It's used for everything, from mapping a million stars in the Milky Way to processing a trillion equity trades per day to managing smart energy grids. InterSystems has developed Cache, a multi-model (object-relational, key-value), DBMS and application server. InterSystems Cache offers multiple APIs that allow you to work with the same data simultaneously: key/value, relational/object, document, multidimensional, object, object, and object. Data can be managed using SQL, Java, node.js.NET, C++ and Python. Cache also offers an application server that hosts web apps (CSP, REST, SOAP and other types TCP access for Cache data).

Amazon Neptune is a fully managed graph database service that allows you to quickly and reliably build applications that can work with highly connected data sets. Amazon Neptune's core is a purpose-built graph database engine that can store billions of relationships and query the graph with only milliseconds latency. Amazon Neptune supports the popular graph models Property Graph, W3C's RDF, as well as their respective query languages Apache TinkerPop Gremlin, SPARQL. This allows you to quickly build queries that efficiently navigate large datasets. Neptune supports graph use cases like recommendation engines, fraud detection and knowledge graphs. It also powers network security and drug discovery.

Couchbase, unlike other NoSQL database, provides a multicloud to edge enterprise-class database that offers robust capabilities for business-critical apps on a highly available and scalable platform. Couchbase is a distributed cloud native database that runs on any cloud. It can be managed by the customer or fully managed. Couchbase is built using open standards and combines the best of NoSQL and SQL with the power and familiarity that mainframes and relational databases provide. Couchbase Server is an open-source, multipurpose distributed database. It combines the best of relational databases, such as SQL, ACID transactions, and JSON, with a foundation which is fast and scalable. It is used in many industries for things such as user profiles, dynamic catalogs, GenAI applications, vector search, caching at high speed, and more.

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.

Free to create, manage, query, and explore Valentina DB and SQLite databases. You can create business reports in Valentina Studio Pro, Valentina Server, or in an application using an Application Developer Kit. Standard backward engineering with forwarding engineering in Valentina Studio Pro Create diagrams from existing databases and reverse engineering. Add new objects to diagrams. SQL queries can be written with color syntax and auto-completion. Define, manage, save favorite queries; access recent queries. Each function has a function browser dictionary. Consoles for errors and warnings. Search, Export result records to CSV, JSON or Excel. Edit multiple properties at once. You can drill down to fields and tables; it is a fast way to search. Create diagrams from existing databases by reverse engineering. Diagrams can be updated with new objects. Manage privileges and users by adding and dropping users and groups.

Titan is a graph database that can store and query graphs with hundreds of billions of edges and vertices distributed across a multi-machine cluster. Titan is a transactional database which can handle thousands of concurrent users performing complex graph traversals in real-time. For a growing user and data base, you can use linear and elastic scaling. Data replication and data distribution for performance and fault tolerance. Hot backups and high availability for multi-datacenters Support for ACID, eventual consistency and other storage backends. Support for Apache Cassandra and Apache HBase storage backends, as well as Oracle BerkeleyDB. Integration with big data platforms such as Apache Spark, Apache Giraph, and Apache Hadoop allows for global graph data analytics, reporting and ETL. Native integration with TinkerPop graph stack to support Gremlin's graph query language, Gremlin's graph server, and Gremlin apps.

Apache TinkerPop™, a graph computing framework, is available for graph databases (OLTP), and graph analytic system (OLAP). Apache TinkerPop's graph traversal language is Gremlin. Gremlin allows users to express complex traversals (or queries) on their application's property diagram in a concise, data-flow language. Each Gremlin traversal consists of a sequence (potentially nested). A graph is a structure that is composed of vertices or edges. Each edge and vertices can have an unlimited number of key/value pairs, called properties. Vertices can be used to denote discrete objects, such as a person or a place or an event. Edges denote relationships between vertices. A person might know another person, be involved in an event, or have been to a specific place recently. If a domain contains a heterogeneous set objects (vertices), that can be linked to one another in many ways (edges), it is called a domain.

SQLite is a C language library that implements a small, fast and self-contained SQL database engine. It is highly reliable, compact, efficient, and fully-featured. SQLite is the most widely used database engine in the globe. SQLite is embedded in all mobile phones and computers. It also comes with countless other applications that people use every single day. SQLite is an embedded library that implements a self contained, serverless, zero configuration, transactional SQL database engine. The code for SQLite can be used for commercial and private purposes. SQLite is the most used database in the world, with many high-profile projects and more applications than we can count.

Understanding the relationships between data is key to unlocking the true value of continuously changing business data. A graph database, unlike other databases, puts relationships first. It uses Linear Algebra and graph theory to explore and show how complex data webs, sources, and points relate. TIBCO®, Graph Database allows users to store, transform, and interpret complex dynamic data into meaningful insights. Users can quickly build data and computational models that create dynamic relationships between organizational silos. These knowledge graphs provide value by connecting the vast array of data in your organization and revealing relationships that allow you to optimize assets and processes. OLTP and OLAP features combined in a single enterprise-grade data base. Optimistic ACID-level transaction properties with native storage access.

The superfast nosql database for mobile devices and iot, with integrated data sync. High-performance Objectbox runs 10x faster than other databases, improving response times and enabling real time applications. Check out our benchmarks. From sensor to server, and everything in between. We support windows, mac/ios and android. Containerized or embedded. Sync data seamlessly. Objectbox's out-of-the box synchronization makes data readily available so your app can go live faster. Offline first Create applications that can work offline and online, without the need for an internet connection. This gives you an "always on"-feeling. Save time and dev. Save time and dev. Objectbox can help you reduce time-to-market, development and lifecycle costs, and free up valuable developer time to do tasks that add value. Objectbox helps reduce cloud costs by persisting data locally (on-the edge) and syncing data faster and more efficiently.

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

Cayley is an open source database for Linked Data. It was inspired by Google's Knowledge Graph graph database (formerly Freebase). Cayley is an open source graph database that allows you to store complex data and makes it easy to use. Built-in query editor, visualizer, and REPL. Cayley supports multiple query languages, including Gizmo, a query engine inspired by Gremlin and GraphQL-inspired query languages, MQL, a simplified version for Freebase lovers, and MQL. Cayley is modular and easy to connect with your favorite programming languages. It can also be used by back-end stores. Cayley has been well tested and used by many companies for their production workloads. It is also fast and optimized for use in applications. Rough performance testing has shown that on 2014 consumer hardware, 134m quads of LevelDB are not a problem, and a multi-hop intersection query - films starring X or Y - takes 150ms. Cayley is set up to run in memory by default (that's what backendmemstore means).

IBM Informix®, a fast and flexible database that can seamlessly integrate SQL, NoSQL/JSON and time series data, is available. Informix's versatility and ease-of-use make it a popular choice for a wide variety of environments, including enterprise data warehouses and individual application development. Informix is also well-suited for embedded data management solutions due to its small footprint and self-managing capabilities.

Graph Engine (GE), a distributed in-memory processing engine, is supported by a strongly-typed RAM storage and a general distributed computing engine. The distributed RAM store is a global addressable, high-performance key-value storage that can be accessed by a cluster of computers. GE's RAM store allows fast random data access over a large data set. GE is a natural platform for large graph processing due to its ability to speed data exploration and distribute parallel computing. GE supports both low latency online query processing as well as high-throughput offline analysis on billion-node large Graphs. Schema is important when data processing must be efficient. For data storage that is compact, quick and clear, strong data modeling is essential. GE has the ability to manage billions of runtime objects of different sizes. As the number of objects increases, each byte counts. GE offers fast memory reallocation and allocation with high memory ratios.

Sparksee, formerly known as DEX, is space- and performance-friendly. It has a small footprint and can quickly analyze large networks. It is natively compatible with.Net, C++ and Python and Objective-C. The graph is represented using bitmap data structures, which allow for high compression rates. Each bitmap is divided into chunks that can be placed on disk pages to increase I/O location. Bitmaps allow operations to be computed using binary logic instructions, which simplify execution in pipelined processors. Full native indexing allows for extremely fast access to all graph data structures. Bitmaps are used to represent node adjacencies in order to reduce their footprint. Advanced I/O policies reduce the number of times each page is brought into memory. Each value in the database can only be represented once, which prevents unnecessary replication.

Microsoft has worked hard on making.NET and SQL Server a strong combination, and VistaDB brings this capability into a small and easy-to-deploy packaged. VistaDB is a small, managed assembly that you can deploy with your app. Each database is a separate file. Using Xamarin you can fit a complete RDBMS onto your phone. VistaDB is a compliant ADO.NET provider of data with support for Entity Frame, ADO.NET and Typed Datasets, as well as its direct data access API to perform efficient cursor-based operations. VistaDB is compatible with a variety of third-party ORMs and reporting systems thanks to ADO.NET. VistaDB fully supports ADO.NET's provider factory model, allowing you to create a single codebase that targets VistaDB or SQL Server. Imagine all the things that are built on ADO.NET. Not just your application, but also reporting systems, ORMs and other data-driven libraries.

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.

Azure Cosmos DB, a fully managed NoSQL databank service, is designed for modern app development. It offers guaranteed single-digit millisecond response time and 99.999 percent availability. This service is backed by SLAs and instant scalability. Open source APIs for MongoDB or Cassandra are also available. With turnkey multi-master global distribution, you can enjoy fast writes and readings from anywhere in the world.

Realtime, realtime, offline-first, graph database engine. You can store, load, and share the data you need in your app without worrying too much about servers, network calls, database access, or tracking offline changes. GUN is a simple, fast, and easy-to-use data sync and storage tool that runs wherever JavaScript does. GUN's goal is to let you concentrate on the data that must be stored, loaded, shared, and shared in your app. It doesn't need to worry about servers, database calls, tracking offline changes, concurrency conflicts, or monitoring network calls. This allows you to quickly build cool apps. GUN gives you the most powerful tools of the internet, decentralization and privacy. This allows you to reclaim the web and make the internet truly open and free. GUN is a database engine which runs on all JavaScript devices, including mobile devices and servers. It allows you to create the data system that you want.

GraphBase (Graph Database Management System, Graph DBMS), is a Graph Database Management System designed to simplify the creation and maintenance complex data graphs. The Relational Database Management System is challenged by complex and interconnected structures. A graph database offers better modeling utility, performance, and scalability. The triplestores and property diagrams are the most recent graph database products. They have been around for almost two decades. Although they are powerful tools with many uses, they are not well-suited for managing complex data structures. GraphBase was created to make complex data management easier. It could be Knowledge. This was possible by redefining the way graph data should be managed. GraphBase makes the graph a first-class citizen. A graph equivalent to the "rows & tables" paradigm makes it so easy to use a Relational Database.

Data collaboration made easy. We make collaboration easy for developers looking to innovate and data people looking for version control. TerminusDB is an open source knowledge graph database that allows for reliable, private and efficient revision control and collaboration. Nothing will make it easier to collaborate with others or create data-intensive apps. TerminusDB offers a full range of revision control features. TerminusHub allows users access to databases and to collaborate on shared resources. Flexible data storage, versioning, and sharing capabilities. Integration into your app or team collaboration. You can work locally and sync your changes when you push them. Easy querying, cleaning, visualization. You can integrate powerful version control and collaboration to your enterprise and individual customers. Remote data teams can collaborate on data projects easily.

Vector search can give you a competitive edge. Streamline your AI/ML workflows, reduce costs and stay ahead with up to a 10x faster time-to market. ApertureDB’s unified multimodal management of data will free your AI teams from data silos and allow them to innovate. Setup and scale complex multimodal infrastructure for billions objects across your enterprise in days instead of months. Unifying multimodal data with advanced vector search and innovative knowledge graph, combined with a powerful querying engine, allows you to build AI applications at enterprise scale faster. ApertureDB will increase the productivity of your AI/ML team and accelerate returns on AI investment by using all your data. You can try it for free, or schedule a demonstration to see it in action. Find relevant images using labels, geolocation and regions of interest. Prepare large-scale, multi-modal medical scanning for ML and Clinical studies.

PuppyGraph allows you to query multiple data stores in a single graph model. Graph databases can be expensive, require months of setup, and require a dedicated team. Traditional graph databases struggle to handle data beyond 100GB and can take hours to run queries with multiple hops. A separate graph database complicates architecture with fragile ETLs, and increases your total cost ownership (TCO). Connect to any data source, anywhere. Cross-cloud and cross region graph analytics. No ETLs are required, nor is data replication. PuppyGraph allows you to query data as a graph directly from your data lakes and warehouses. This eliminates the need for time-consuming ETL processes that are required with a traditional graph databases setup. No more data delays or failed ETL processes. PuppyGraph eliminates graph scaling issues by separating computation from storage.

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.

AnzoGraph DB offers a wide range of analytical features that can be used to enhance your analytical framework. This video will show you how AnzoGraph DB, a native graph database for massively parallel processing (MPP), is designed for data harmonization. Horizontally scalable graph database designed for online analytics and harmonization. AnzoGraphDB, a market-leading graph database, can help you tackle linked data problems and data harmonization. AnzoGraph DB offers industrialized online performance for enterprise-scale graph apps. AnzoGraph DB supports Labeled Property Graphs (LPGs) and familiar SPARQL*/OWL semantic graphs. You have access to many data science, machine learning, and analytical capabilities that will help you gain new insights at an unparalleled speed and scale. Your analysis will be more effective if you consider the context and relationships of data. Data loading and queries ultra-fast

OrigoDB allows you to create high-quality, mission-critical systems in a fraction of time and cost. This isn't marketing gibberish! For a detailed description of our features, please read on. Contact us if you have any questions. You can also download the software and start it right away! In-memory operations are a lot faster than disk operations. One OrigoDB engine can execute millions upon millions of read transactions per minute and thousands upon thousands of write transactions every second. Asynchronous command journaling to local SSDs is also available. This is why OrigoDB was built. A single object-oriented domain model is much simpler than a full stack that includes a relational model, object/relational map, data access code and views, as well as stored procedures. This is a lot of waste that can easily be eliminated. The OrigoDB engine runs 100% ACID right out of the box. Each command executes one at a moment, transitioning the in memory model from one consistent state into another.

The graph database is designed for graphs up to super large scale with very low latency. We continue to work with the community to promote, popularize, and prepare the graph database. Nebula Graph allows only authenticated access through role-based access control. Nebula Graph can support multiple storage engines and the query language is extensible to support new algorithms. Nebula Graph offers low latency read/write while maintaining high throughput to simplify complex data sets. Nebula Graph's distributed, shared-nothing architecture allows for linear scaling. Nebula Graph's SQL query language is similar to SQL and can be used to address complex business requirements. Nebula Graph's horizontal scalability, snapshot feature and high availability guarantee that there will be no downtime. Nebula Graph has been used in production environments by large Internet companies such as JD, Meituan and Xiaohongshu.

JanusGraph is an optimized graph database that can store and query graphs with hundreds of billions of edges and vertices distributed across a multi-machine cluster. JanusGraph is a project of The Linux Foundation and includes participants from Expero and Google, GRAKN.AI., Hortonworks. IBM, and Amazon. Linear and elastic scaling for growing data and users. Data replication and data distribution for performance and fault tolerance. Hot backups and high availability for multi-datacenters All functionality is completely free. There is no need to purchase commercial licenses. JanusGraph is completely open source under the Apache 2 License. JanusGraph is an open source transactional database that can handle thousands of concurrent users performing complex graph traversals in real-time. ACID and eventual consistency support. JanusGraph offers online transactional processing (OLTP) and global graph analytics (OLAP), through its Apache Spark integration.

You can create high-quality, mission-critical applications in a fraction of the time and costs. Memstate is new. It is extremely inefficient to move data between RAM and disk. Additionally, it requires complex software that can be eliminated. Memstate allows you to manage and structure your data in-memory. It also provides transparent persistence, concurrency control, and transactions with strong ACID warranties. This is too technical. Make your applications faster and your developers more productive. Memstate can be used in many ways, but it is designed to handle complex OLTP workloads within an enterprise application. In-memory operations are a factor of ten times faster than disk operations. A single Memstate engine can execute millions upon millions of read transactions, and tens to thousands upon thousands of write transactions per second. All this at submillisecond latency.

Empress Embedded Database engine, a relational database management software that specializes in embedded database technology, is the heartbeat behind EMPRESS RDBMS. It's a relational database management tool that focuses on embedded database technology. From car navigation systems to mission-critical military command and control systems, to complex medical systems and Internet routers, EMPRESS keeps a steady beat, 24 hours / 7, at the core of embedded system applications all over the world. Empress kernel level mr API gives users access the Empress Database kernel libraries. This is a unique feature in Empress. This Empress API is the fastest way to access Empress databases. MR Routines allow developers to have complete control over space and time when developing embedded database applications. Empress ODBC APIs and JDBC APIs allow Empress databases to be accessed in standalone or client/server modes. Empress ODBC APIs and JDBC APIs allow many 3rd-party ODBC/JDBC capable software packages access to Empress databases via Empress Connectivity Server or local Empress databases.

*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.

HSQLDB (HyperSQL DataBase), is the most popular SQL relational database system in Java. It is a small, fast, multithreaded, transactional database engine that supports both embedded and server modes. It also includes simple GUI query tools and a powerful command-line SQL tool. HSQLDB supports all the SQL Standard features found in an open-source database engine, including the SQL:2016 core language features as well as a wide range of optional SQL:2016 features. With only two exceptions, it supports Advanced ANSI-92 SQL. Many extensions to the Standard are supported, including syntax compatibility modes, and features of popular database engines.

Symas LMDB, an extremely fast and memory-efficient database that we created for the OpenLDAP Project, is Symas LMDB. It uses memory-mapped files to provide the same read performance as an in-memory database but retains the persistence of standard disk databases. LMDB is small at 32KB in object code. It's still the right 32KB. LMDB is both compact and efficient. That's why LMDB is so powerful. Symas provides fixed-price commercial support for those who use LMDB in their applications. Development takes place in the OpenLDAP Project's Git repo in mdb.master branch. Symas LMDB has been featured in numerous publications and publications.

Companies who choose a DIY approach to their graph database can expect a wait of 2 to 3 months before production-ready implementation. Your production-ready database will be available within minutes with Graph Story's managed services. Learn more about graph use cases and compare self-hosting to managed services. We can deploy your servers where they are already located: AWS, Azure or Google Compute Engine in any region. Do you need VPC peering? Let us know. We are flexible like that. How do you build a proof-of-concept? In just a few clicks, you can fire up one enterprise graph instance. Do you need to move to a cluster that is high-availability and production-ready on-demand? We've got you covered! We created graph db management tools to make it easy for you! You can see CPU, Memory, and Disk utilization in one glance. Access configs, logs and backups of your database.

RocksDB uses a log-structured database engine written entirely in C++ for maximum performance. Keys and values can be stored in arbitrarily-sized byte streams. RocksDB is optimized to store flash drives and high speed disk drives in fast, low latency storage. RocksDB makes the most of flash and RAM's high read/write speeds. RocksDB can perform basic operations like opening and closing a table, reading and writing, and more complex operations such as merging or compaction filters. RocksDB can adapt to different workloads. RocksDB can be used to meet a wide range of data needs, including database storage engines like MyRocks and application data caching.