Alternatives to KgBase

TeamDesk is the leading low-code platform for creating powerful and flexible web-based databases with no-coding. TechRadar named TeamDesk as the best database platform of the year. TeamDesk provides Artificial Intelligence as well as predefined solutions for rapid online database creation without coding. Business owners and citizen developers can utilize AI to build unique databases for any type of industry that precisely fit their business workflow and organize gathering, sharing and managing business information. TeamDesk online database software is fully scalable and customizable to accommodate customers’ ever evolving business needs. TeamDesk provides: AI (Artificial Intelligence) integration API, Web hooks, Zapier unlimited data storage unlimited number of records and tables unlimited database complexity free trial free unlimited support for a low flat rate. TeamDesk is fully scalable. From small companies to large enterprises, from specific manufactures to vertical business integration, system scalability accommodates customers' business growth and adjusts to evolving business model. Enterprise Edition supports custom domain, white labeling, SSO via SAML2, unlimited databases centralized security management.

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.

PerfectApps puts the power to create completely customized no-code solutions in your hands. Subject matter experts can build enterprise-ready web apps, web forms and reports in days or weeks instead of months – without coding. Using a visual designer, simply drag & drop pre-coded elements on a canvas to build the entire application. The no-code approach removes coding as a barrier between ideas and solutions. Benefits include: Faster Delivery. Organizations can be more agile and deploy solutions faster to address their mission critical needs. No-code development is 10x faster than traditional methods (Forrester, 2019). Accessibility. Subject matter experts can build their own web apps without coding which frees up overburdened IT resources. The demand for app development will surpass IT's capacity to deliver by 5 times through 2022, according to Gartner. Lower Cost. App development can be distributed across the organization, reducing total operating costs and improving productivity. Organizations have reported reducing development cost by 74% using no-code app builders (Forrester, 2020).

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).

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.

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.

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.

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.

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.

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.

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.

Graph databases are part of Oracle's converged data platform. They eliminate the need for a separate database to store and move data. Analysts and developers are able to detect fraud in banking, locate connections and link data, and improve traceability and smart manufacturing traceability. All this while gaining enterprise-grade security and ease of data ingestion and strong support for data workloads. Oracle Autonomous Database also includes Graph Studio. It offers one-click provisioning, integrated tools, and security. Graph Studio automates graph data administration and simplifies analysis, modeling, and visualization throughout the graph analytics lifecycle. Oracle supports both RDF knowledge graphs and property graphs. It also simplifies the process for modeling relational data as graph structures. Interactive graph queries can be run directly on graph data, or in high-performance, in-memory graph servers.

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.

The database is the foundation of intelligent systems. Grakn is an intelligent database, a knowledge graph. A data schema that is intuitive and expressive. It can be used to create rich knowledge models by defining hierarchies, hyperentities, hyperrelations, rules, and constructs. Intelligent language that infers data types, relationships and attributes, as well as complex patterns, at runtime and with persistent and distributed data. Accessible through simple queries, out-of-the box distributed analytics (Pregel & MapReduce), are available through the language. Strong abstraction allows for simpler expressions of complex constructs while the system determines the best query execution. Grakn KGMS & Workbase allow you to scale your enterprise Knowledge Graph. A distributed database that can scale across a network of computers by partitioning and replicating.

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.

AllegroGraph is a revolutionary solution that allows infinite data integration. It uses a patented approach that unifies all data and siloed information into an Entity Event Knowledge Graph solution that supports massive big data analytics. AllegroGraph uses unique federated sharding capabilities to drive 360-degree insights, and enable complex reasoning across a distributed Knowledge Graph. AllegroGraph offers users an integrated version Gruff, a browser-based graph visualization tool that allows you to explore and discover connections within enterprise Knowledge Graphs. Franz's Knowledge Graph Solution offers both technology and services to help build industrial strength Entity Event Knowledge Graphs. It is based on the best-of-class products, tools, knowledge, skills, and experience.

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.

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.

*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.

RelationalAI is a next generation database system that allows intelligent data applications to be built on relational knowledge graphs. Data-centric application design combines logic and data into reusable models. Intelligent data applications can understand and make use each relation in a model. Relational provides a knowledge graph system that allows knowledge to be expressed as executable models. These models can easily be extended using declarative, human-readable software. RelationalAI's expressive and declarative language results in a 10-100x decrease in code. By involving non-technical domain specialists in the creation process, and automating complex programming tasks, applications are created faster and with better quality. The extensible graph data model is a foundation for data-centric architecture. Integrate models to uncover new relationships and reduce barriers between applications.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

VelocityDB is a database platform unlike any other. It stores data faster and more efficiently than other databases engines at a fraction the cost. It stores.NET objects in their original form without any mapping to tables, JSON, or XML. VelocityGraph, an open-source property graph database, can be used in conjunction the VelocityDB object data base. Object and graph database engine VelocityDB, a C#.NET NoSQL object database, can be extended to VelocityGraph. World's fastest most scalable & flexible database. A bug reported with a reproducible case is usually fixed within one week. This database system offers the greatest benefit, flexibility. You can fine-tune your application like no other database system. You can choose the most suitable data structure for your application with VelocityDB. You can choose where and how the data is indexed and accessed.

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.

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.

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.

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

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.

Powerful graph analytics made easy. Aster SQL-GR™, a native graph processing engine for graph analysis, makes it easy to solve complex business issues such as social network/influencer analysis. It also helps with fraud detection, supply chain management and network analysis. These problems are more impactful than simple graph navigation analysis. SQL-GR is based upon the Bulk Synchronous Process (BSP) model. It uses massively iterative and parallel processing to solve complex graph problems. SQL-GR is extremely scalable because it is based upon the BSP iterative process model. It also takes advantage of Teradata Aster’s massively scalable parallel processor (MPP) architecture to distribute graph processing across multiple servers/nodes. SQL-GR does not have memory limits and is not limited to one server/node. SQL-GR can easily perform complex graph analysis on large data sets with unmatched speed and power.

Locstat, a graph intelligence platform, is a graph-based AI platform that integrates analytics, event processing and graph-based AI. It allows organizations to scale up next-generation data-driven solutions quickly. Research shows that adopting innovative AI-supported digitalization strategy can result in significant benefits and gains. We have had great success in improving customer efficiency and delivering significant ROI, as measured by ourselves and confirmed by research firms. This shows the value of advanced analytics technologies to solve today's complex issues more cost-effectively compared to solutions based on relational databases.

We are developing autonomous systems that can write and deploy custom software to complete complex tasks. This is done using only English instruction. Maya can translate steps from English into visual programs you can edit and extend without having to write code. To create a visual program, describe the business logic of your application in English. In seconds, dependencies are automatically detected, installed, then deployed. Drag-and-drop editor allows you to extend functionality to hundreds of nodes. To automate your work, quickly create useful tools. Simply describe how multiple data sources work together to connect them. Pipe data into tables, charts, or graphs using natural language descriptions. To assist a human entering and changing data, you can create, edit, or deploy dynamic forms. Copy and paste your natural-language program into a note-taking application or share it with friends. English-language apps can be written, modified, debugged, deployed, and used. Describe the steps that you want Maya code to take.

Virtuoso, a Data Virtualization platform that enables fast and flexible harmonization between disparate data, increases agility for both individuals and enterprises. Virtuoso Universal server is a modern platform built upon existing open standards. It harnesses the power and flexibility of Hyperlinks (functioning like Super Keys) to break down data silos that hinder both enterprise and user ability. Virtuoso's core SQL & SPARQL powers many Enterprise Knowledge Graph initiatives, just as they power DBpedia. They also power a majority nodes in Linked Open Data Cloud, the largest publicly accessible Knowledge Graph. Allows for the creation and deployment of Knowledge Graphs atop existing data. APIs include HTTP, ODBC and JDBC, OLE DB and OLE DB.

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.

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.

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.

Frontly is an advanced SaaS/web app builder that does not require any coding. It offers powerful features and a rapid development process. Frontly is different from other app builders in that it allows users to create apps and components using AI, to build and maintain a library of reusable components and to duplicate entire apps and web pages. Users can also share and sell their designs on Frontly's marketplace for revenue. OpenAI allows you to automate repetitive tasks, edit and generate content using structured output. Transform your spreadsheets with charts, tables, forms and more into stunning visual interfaces. Access controls allow you to control which data users see down to the individual row level. Add your logo, brand colours, and custom domain to create a professional client-facing portal. Create apps in minutes that display data in a customized visual interface, filter the data based on permissions, generate with AI and revise your data, and connect your external systems via workflow automation integrations.

G.V() is an all in one Gremlin IDE that allows you to write, debug and test your Gremlin graph database. It has a rich UI with graph visualization, editing, and connection management. G.V() automatically detects the connection requirements based upon the hostname you provide. It prompts you to enter the next required information so that you can have an easy onboarding experience regardless of which Gremlin database it is. To build, test, visualize, and query your data quickly, load, visualize, and draw your graph in true "What you see is what you get" fashion. Learn Gremlin using the embedded documentation and G.V()’s in-memory diagram. You can view your Gremlin query results quickly in different formats. Compatible with all major Apache TinkerPop enabled Graph Data Database Providers: Amazon Neptune; Azure Cosmos DB’s Gremlin API; DataStax Enterprise Graph; JanusGraph, ArcadeDB; Aliyun TairForGraph; Gremlin Server.

Luna is a subscription for Apache Cassandra support at DataStax. You can enjoy all the benefits offered by open-source Cassandra with the assurance that you have direct access the team that wrote the majority of the code. They also support some of the most important deployments around the globe. You will receive best practices, advice, as well as SLA-based support to maintain your Cassandra deployment. Scale without compromising performance or latency to manage the most complex real-time workloads. You can create highly interactive customer experiences that are real-time and highly interactive. Luna can help you resolve issues and follow best practices for Cassandra clusters. Services can be used to assist with the entire application life cycle. They also allow for deeper integration of your team as they work together on implementation.

To get the functionality you require, combine different UI components. Advanced business logic can be implemented through formulas and workflows. No design or coding required. You can visualize and take action on your data with tables, forms, buttons and charts. You can change themes and create custom branding in a matter of seconds. You can set granular permissions to manage how your team, customers, and partners interact with the app's data. Collaborate on tasks, share your app with customers and colleagues, and create approval workflows. To streamline your operations, connect data from DBs, business software, and APIs. Jet offers powerful developer tools that allow you to extend your applications using code and queries. You can create your own components using HTML/CSS or Javascript, React and Angular. To implement custom actions, tables and forms, create custom SQL queries or HTTP queries. You can perform calculations and parse the results with formulas or Java Script. Apps can be created for Customer service, Operations, Sales, and other teams to support daily internal operations.

Blaze gives you everything you need to create your tool without any code. Blaze allows you to create multiple apps and connect them easily, allowing your team to be more productive. We mean it when we say "all-in-one". Our Implementation Team builds your app from scratch so you can concentrate on growing your business. Our templates are fully customizable and you can start building apps such as customer portals or internal tools to move your business forward. Blaze's ChatGPT system can help you create your app. Create tables that visualize data, add forms and more. You can also drag-and-drop Blaze components into your app. Blaze's database is a no-code database, or you can connect to your own database and create custom tools. You can be the most valuable member of your team without having to write any code.

In a visual drag and drop builder, quickly create enterprise-ready, compliant, and intelligent experiences at the business line level. You can create your Crowd Machine experience using a customized selection of charts, graphs and summaries. Crowd Machine Gadgets can be used by your business to create your Crowd Machine experience. They are highly functional components and intelligent microservices. Crowd Machine Gadgets are able to be almost anything your business needs, from standalone components to intelligent microservices that learn from each other. Crowd Machine gadgets can be combined with existing business applications to create a seamless experience that allows you to access all of your business's information.

Blazegraph™, a graph database that supports Blueprints and RDF/SPARQL, is an ultra-high-performance graph database. It can support up to 50 Billion edges per machine. It is currently in production for Fortune 500 customers like EMC, Autodesk, among others. It supports key Precision Medicine applications, and is widely used for life sciences applications. It is extensively used to support Cyber analytics in government and commercial applications. It powers Wikidata Query Service, a Wikimedia Foundation project. You can choose an executable jar, war file, or tar.gz distribution. Blazegraph was designed to be simple to use and easy to get started. This is why it ships without SSL and authentication by default. We strongly recommend that you enable SSL, authentication and the appropriate network configurations for production deployments. Below are some useful links to help you do this.