Alternatives to Memstate

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

You can use Ignite as a traditional SQL Database by leveraging JDBC drivers or ODBC drivers. Or, you can use the native SQL APIs for Java, C# and C++, Python, or other programming languages. You can easily join, group, aggregate, or order your distributed on-disk and in-memory data. You can accelerate your existing applications up to 100x by using Ignite as an in memory cache or in-memory grid that is deployed over one of several external databases. You can query, transact, and calculate on this cache. Ignite is a database that scales beyond your memory capacity to support modern transactional and analytical workloads. Ignite allocates memory to your hot data and writes to disk when applications query cold records. Execute custom code up to kilobytes in size over petabytes. Your Ignite database can be transformed into a distributed supercomputer that can perform low-latency calculations, complex analysis, and machine learning.

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.

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.

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.

The Apache Cassandra database provides high availability and scalability without compromising performance. It is the ideal platform for mission-critical data because it offers linear scalability and demonstrated fault-tolerance with commodity hardware and cloud infrastructure. Cassandra's ability to replicate across multiple datacenters is first-in-class. This provides lower latency for your users, and the peace-of-mind that you can withstand regional outages.

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.

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.

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.

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.

IBM Cloud®, purpose-built databases, deliver high availability and enhanced security as well as scalable performance. You can choose from a range of database engines, including relational and NoSQL databases, such as graph, key-value and in-memory databases, and document, key-value and graph databases. You can build distributed, modern applications that are highly scalable and distributed thanks to the support for multiple data models. There is no one size fits all. You can speed up development and meet your business needs by choosing the right database for the job. IBM Cloud DBaaS solutions include hosting, auto provisioning, and 24x7 management with automated backup and restore, version updates, security, and more.

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.

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.

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.

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.

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.

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.

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.

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

High-speed, data-intensive apps that meet all performance requirements can be built. Apache Geode's unique technology combines advanced techniques for data replication and partitioning with distributed processing. Apache Geode offers a database-like consistency model, reliable transactions processing, and a shared nothing architecture to maintain very low latency with high concurrency. Data can be easily partitioned (sharded), or replicated among nodes, allowing for performance scaling as required. Reliable in-memory copies are used to ensure durability. Disk-based persistence is also used to ensure longevity. Super fast write-ahead log (WAL) persistence that uses a shared-nothing architecture optimized for parallel recovery of nodes and entire clusters.

With our ACID in-memory technologies and application servers, you can build enterprise software lightning fast. You can build enterprise software without using new syntax or custom tools. Starcounter applications can improve performance by up to 1000 times without adding complexity. Applications are written using regular C#, LINQ and SQL. Even the ACID transactions can be written in C# code. Full Visual Studio support, including IntelliSense and debugger. All the features you love, without the headache. Write regular C# syntax using the MVVM pattern for ACID in-memory and thin client UI to achieve extreme performance. Starcounter technology is a business asset from the first day. We use technology that is already developed and in production to process millions of business transactions. Starcounter combines ACID's in-memory databases and application servers into a single platform that is unmatched for performance, simplicity and price.

Amazon ElastiCache makes it easy to create, manage, and scale popular open source compatible in-memory cloud data stores. You can build data-intensive apps and improve the performance of existing databases by retrieving data in high-throughput and low latency, in-memory storages. Amazon ElastiCache is popular for real-time applications such as Caching, Session Stores and Gaming, Geospatial Service, Real-Time Analytics and Queuing. Amazon ElastiCache provides fully managed Redis, Memcached and other services for demanding applications that need sub-millisecond response time. Amazon ElastiCache is an in-memory cache and data store that can support the most demanding applications that require sub-millisecond response time. Amazon ElastiCache delivers secure, lightning fast performance by using an optimized stack that runs on customer-dedicated nodes.

Terracotta DB, a distributed in-memory database management solution, is a comprehensive and flexible data management tool that caters to both operational storage and caching. It also enables transactional processing and analysis. Ultra-Fast Ram and Big Data = Business Power. BigMemory gives you: Real-time access and control over terabytes in-memory data. High throughput and predictable latency. Support for Java®, Microsoft®,.NET/C# and C++ applications. 99.999 percent uptime. Linear scalability. Data consistency guarantees across multiple servers. Optimized data storage across SSD and RAM. SQL support for querying in memory data. Maximal hardware utilization results in lower infrastructure costs. High-performance persistent storage for durability and fast restart. Advanced monitoring, management, and control. Data storage that is ultra-fast and in-memory, which automatically moves data to the right place. Support for data replication across multiple data centers for disaster recovery. Real-time management of fast-moving data

Dragonfly replaces Redis with a plug-and-play solution that reduces costs and improves performance. Dragonfly is designed to take full advantage of the power of cloud hardware, and meet the data needs of modern applications. It frees developers from traditional in-memory databases. Legacy software cannot take advantage of the power of modern cloud hardware. Dragonfly is optimized to work with modern cloud computing. It delivers 25x more throughput, and 12x less snapshotting latency, when compared to traditional in-memory stores like Redis. This makes it easy to provide the real-time experiences your customers expect. Due to Redis' inefficient single-threaded design, scaling Redis workloads can be expensive. Dragonfly has a much higher memory and compute efficiency, resulting in infrastructure costs that are up to 80% less. Dragonfly scales first vertically, and only requires clustering when the scale is extremely high. This results in an operational model that is simpler and more reliable.

Dqlite is an embedded, persistent SQL database that runs on Raft consensus. It is ideal for fault-tolerant IoT devices and Edge devices. Dqlite ("distributed SQLite") extends SQLite over a cluster of machines with automatic failover and high availability to keep your application running. C-Raft is an optimised Raft implementation in C that allows for high-performance transactional consensus, fault tolerance, and SQlite's small footprint. C-Raft has been designed to minimize transaction latency. C-Raft, dqlite and other programs are written in C to maximize cross-platform portability. Publication under the LGPLv3 License with a static linking exemption for maximum compatibility. Common CLI pattern is used for database initialization, voting member joins and exits. With automatic leader election, there is minimal, but not insignificant, delay for failover. Disk-backed database with in memory options and SQLite transaction.