Best Graph Databases in Brazil - Page 2

Building intelligent systems starts at the database. Grakn is an intelligent database - a knowledge graph. An insanely intuitive & expressive data schema, with constructs to define hierarchies, hyper-entities, hyper-relations and rules, to build rich knowledge models. An intelligent language that performs logical inference of data types, relationships, attributes and complex patterns, during runtime, and over distributed & persisted data. Out-of-the-box distributed analytics (Pregel and MapReduce) algorithms, accessible through the language through simple queries. Strong abstraction over low-level patterns, enabling simpler expressions of complex constructs, while the system figures out the most optimal query execution. Scale your enterprise Knowledge Graph with Grakn KGMS and Workbase. A distributed database designed to scale over a network of computers through partitioning and replication.

Build high quality, mission critical applications with real-time performance at a fraction of the time and cost. Memstate is a new. Moving data back and forth between disk and RAM is not just extremely inefficient, it requires multiple layers of complex software that can be eliminated entirely. Use Memstate to structure and manage your data in-memory, obtain transparent persistence, concurrency control and transactions with strong ACID guarantees. note: this is too techy... Make your applications 100x faster, and your developers 10x more productive. Memstate has many possible use cases but is designed primarily to handle complex OLTP workloads in a typical enterprise application. In-memory operations are orders of magnitude faster than disk operations. A single Memstate engine can execute millions of read transactions and tens of thousands of write transactions per second, all at submillisecond latency.

HyperGraphDB is a general purpose, open-source data storage mechanism based on a powerful knowledge management formalism known as directed hypergraphs. While a persistent memory model designed mostly for knowledge management, AI and semantic web projects, it can also be used as an embedded object-oriented database for Java projects of all sizes. Or a graph database, or a (non-SQL) relational database. HyperGraphDB is a storage framework based on generalized hypergraphs as its underlying data model. The unit of storage is a tuple made up of 0 or more other tuples. Each such tuple is called an atom. One could think of the data model as relational where higher-order, n-ary relationships are allowed or as graph-oriented where edges can point to an arbitrary set of nodes and other edges. Each atom has an arbitrary, strongly-typed value associated with it. The type system managing those values is embedded as a hypergraph and customizable from the ground up.

Companies that opt for a DIY approach for their graph database can expect 2 to 3 months for a production-ready implementation. With Graph Story’s managed service, your production-ready database is available within minutes. Learn more about graph use cases as well as see a comparison between self-hosting and using a managed service. We can deploy where your servers already live: AWS, Azure, or Google Compute Engine, in any region. Need VPC peering or IP-restricted access? Just let us know. We're flexible like that. Building a proof of concept? Fire up a single, enterprise graph instance with a few clicks. Need to move up to a high-availability, production-ready cluster on-demand? We've got you covered! We built graph db management tools so you don't have to! See CPU, Memory and Disk utilization at glance. Get access to configs, logs, backup your database & restore snapshots.

HugeGraph is a fast-speed and highly-scalable graph database. Billions of vertices and edges can be easily stored into and queried from HugeGraph due to its excellent OLTP ability. As compliance to Apache TinkerPop 3 framework, various complicated graph queries can be accomplished through Gremlin (a powerful graph traversal language). Among its features, it provides compliance to Apache TinkerPop 3, supporting Gremlin. Schema Metadata Management, including VertexLabel, EdgeLabel, PropertyKey and IndexLabel. Multi-type Indexes, supporting exact query, range query and complex conditions combination query. Plug-in Backend Store Driver Framework, supporting RocksDB, Cassandra, ScyllaDB, HBase and MySQL now and easy to add other backend store driver if needed. Integration with Hadoop/Spark. HugeGraph relies on the TinkerPop framework, we refer to the storage structure of Titan and the schema definition of DataStax.

Graph databases, part of Oracle’s converged database offering, eliminate the need to set up a separate database and move data. Analysts and developers can perform fraud detection in banking, find connections and link to data, and improve traceability in smart manufacturing, all while gaining enterprise-grade security, ease of data ingestion, and strong support for data workloads. Oracle Autonomous Database includes Graph Studio, with one-click provisioning, integrated tooling, and security. Graph Studio automates graph data management and simplifies modeling, analysis, and visualization across the graph analytics lifecycle. Oracle provides support for both property and RDF knowledge graphs, and simplifies the process of modeling relational data as graph structures. Interactive graph queries can run directly on graph data or in a high-performance in-memory graph server.

The Open, Multi-Cloud Stack for Modern Data Apps. Built on open-source Apache Cassandra™. Global-scale and 100% uptime without vendor lock-in. Deploy on multi-cloud, on-prem, open-source, and Kubernetes. Elastic and pay-as-you-go for improved TCO. Start building faster with Stargate APIs for NoSQL, real-time, reactive, JSON, REST, and GraphQL. Skip the complexity of multiple OSS projects and APIs that don’t scale. Ideal for commerce, mobile, AI/ML, IoT, microservices, social, gaming, and richly interactive applications that must scale-up and scale-down with demand. Get building modern data applications with Astra, a database-as-a-service powered by Apache Cassandra™. Use REST, GraphQL, JSON with your favorite full-stack framework Richly interactive apps that are elastic and viral-ready from Day 1. Pay-as-you-go Apache Cassandra DBaaS that scales effortlessly and affordably.

Dgraph is an open source, low-latency, high throughput, native and distributed graph database. Designed to easily scale to meet the needs of small startups as well as large companies with massive amounts of data, DGraph can handle terabytes of structured data running on commodity hardware with low latency for real time user queries. It addresses business needs and uses cases involving diverse social and knowledge graphs, real-time recommendation engines, semantic search, pattern matching and fraud detection, serving relationship data, and serving web apps.

JanusGraph is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. JanusGraph is a project under The Linux Foundation, and includes participants from Expero, Google, GRAKN.AI, Hortonworks, IBM and Amazon. Elastic and linear scalability for a growing data and user base. Data distribution and replication for performance and fault tolerance. Multi-datacenter high availability and hot backups. All functionality is totally free. No need to buy commercial licenses. JanusGraph is fully open source under the Apache 2 license. JanusGraph is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time. Support for ACID and eventual consistency. In addition to online transactional processing (OLTP), JanusGraph supports global graph analytics (OLAP) with its Apache Spark integration.

XtendR unhides detailed, privacy-preserving insights across multiple independent data sources. XtendR enables access to thus far inaccessible data, and protects you during your entire data lifecycle, giving you confidence in complete privacy and regulatory compliance. XtendR is more than anonymity, it’s the critical missing piece for multi-party data sharing with true privacy protection - it is cryptography on duty so you can reach your full potential. The most advanced privacy-enhancing data collaboration technology. Xtendr solved the decades-long cryptography challenge of data sharing between mutually mistrustful parties. Take your business further with an enterprise-grade data protection offering that allows individual organizations to form data partnerships while protecting sensitive data. Data is the currency of our digital age. Some argue that it is replacing oil as the world's most valuable resource and there is no doubt about its growing importance.

The graph database built for super large-scale graphs with milliseconds of latency. We are continuing to collaborate with the community to prepare, popularize and promote the graph database. Nebula Graph only allows authenticated access via role-based access control. Nebula Graph supports multiple storage engine types and the query language can be extended to support new algorithms. Nebula Graph provides low latency read and write , while still maintaining high throughput to simplify the most complex data sets. With a shared-nothing distributed architecture , Nebula Graph offers linear scalability. Nebula Graph's SQL-like query language is easy to understand and powerful enough to meet complex business needs. With horizontal scalability and a snapshot feature, Nebula Graph guarantees high availability even in case of failures. Large Internet companies like JD, Meituan, and Xiaohongshu have deployed Nebula Graph in production environments.

GraphBase is a Graph Database Management System (Graph DBMS) engineered to simplify the creation and maintenance of complex data graphs. Complex and highly-connected structures are a challenge for the Relational Database Management System (RDBMS). A graph database provides much better modelling utility, performance and scalability. The current crop of graph database products - the triplestores and property graphs - have been around for nearly two decades. They're powerful tools, they have many uses, but they're still not suited to the management of complex data structures. With GraphBase, our goal was to simplify the management of complex data structures, so that your data could become something more. It could become Knowledge. We achieved this by redefining how graph data should be managed. In GraphBase, the graph is a first-class citizen. You get a graph equivalent of the "rows and tables" paradigm that makes a Relational Database so easy to use.

Graph Engine (GE) is a distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine. The distributed RAM store provides a globally addressable high-performance key-value store over a cluster of machines. Through the RAM store, GE enables the fast random data access power over a large distributed data set. The capability of fast data exploration and distributed parallel computing makes GE a natural large graph processing platform. GE supports both low-latency online query processing and high-throughput offline analytics on billion-node large graphs. Schema does matter when we need to process data efficiently. Strongly-typed data modeling is crucial for compact data storage, fast data access, and clear data semantics. GE is good at managing billions of run-time objects of varied sizes. One byte counts as the number of objects goes large. GE provides fast memory allocation and reallocation with high memory ratios.

Powerful graph analytics with ease. Aster SQL-GR™ is a native graph processing engine for Graph Analysis that makes it easy to solve complex business problems such as social network/influencer analysis, fraud detection, supply chain management, network analysis and threat detection, and money laundering that are more impactful than simple graph navigation analysis. SQL-GR is based on the Bulk Synchronous Processing (BSP) model and uses massively iterative, distributed & parallel processing to solve complex graph problems. SQL-GR is massively scalable as it is based on the BSP iterative processing model and takes advantage of Teradata Aster’s massively scalable parallel processing (MPP) architecture to distribute the graph processing across multiple servers/nodes. SQL-GR is not bound by memory limits or to a single server/node. Easily apply unmatched power and speed to perform complex graph analysis at big data scale.

With a huge collection of analytical features, AnzoGraph DB can enhance your analytical framework. Watch this video to learn how AnzoGraph DB is a Massively Parallel Processing (MPP) native graph database that is built for data harmonization and analytics. Horizontally scalable graph database built for online analytics and data harmonization. Take on data harmonization and linked data challenges with AnzoGraph DB, a market-leading analytical graph database. AnzoGraph DB provides industrialized online performance for enterprise-scale graph applications. AnzoGraph DB uses familiar SPARQL*/OWL for semantic graphs but also supports Labeled Property Graphs (LPGs). Access to many analytical, machine learning and data science capabilities help you achieve new insights, delivered at unparalleled speed and scale. Use context and relationships between data as first-class citizens in your analysis. Ultra-fast data loading and analytical queries.

Sparksee (formerly known as DEX), makes space and performance compatible with a small footprint and a fast analysis of large networks. It is natively available for .Net, C++, Python, Objective-C and Java, and covers the whole spectrum of Operating Systems. The graph is represented through bitmap data structures that allow high compression rates. Each of the bitmaps is partitioned into chunks that fit into disk pages to improve I/O locality. Using bitmaps, operations are computed with binary logic instructions that simplify the execution in pipelined processors. Full native indexing allows an extremely fast access to each of the graph data structures. Node adjacencies are represented by bitmaps to minimize their footprint. The number of times each data page is brought to memory is minimized with advanced I/O policies. Each value in the database is represented only once, avoiding unnecessary replication.

Making data collaboration easy. If you are a developer looking to innovate or a data person looking for version control, we make collaboration work for everyone. TerminusDB is an open-source knowledge graph database that provides reliable, private & efficient revision control & collaboration. If you want to collaborate with colleagues or build data-intensive applications, nothing will make you more productive. TerminusDB provides the full suite of revision control features. TerminusHub allows users to manage access to databases and collaboratively work on shared resources. Flexible data storage, sharing, and versioning capabilities. Collaboration for your team or integrated into your app. Work locally then sync when you push your changes. Easy querying, cleaning, and visualization. Integrate powerful version control and collaboration for your enterprise and individual customers. Make it easy for remote data teams to work together on data projects.

To unveil the true value of constantly evolving business data, you need to understand the relationships in data in a much more profound way. Unlike other databases, a graph database puts relationships at the forefront, using Graph theory and Linear Algebra to traverse and show how complex data webs, data sources, and data points relate. TIBCO® Graph Database allows you to discover, store, and convert complex dynamic data into meaningful insights. Enable users to rapidly build data and computational models that establish dynamic relationships among organizational silos. These knowledge graphs deliver value by connecting your organization’s vast array of data and revealing relationships that let you accelerate optimization of assets and processes. Combined OLTP and OLAP features in a single enterprise-grade database. Optimistic ACID level transaction properties with native storage and access.

RelationalAI is a next-generation database system for intelligent data applications based on relational knowledge graphs. Data-centric application design brings data and logic together into composable models. Intelligent data applications understand and make use of each relation that exists in a model. relational provides a knowledge graph system to express knowledge as executable models. These models can be easily extended through declarative, human-readable programs. RelationalAI’s expressive, declarative language leads to a 10-100x reduction in code. Applications are developed faster, with superior quality by bringing non-technical domain experts into the creation process and by automating away complex programming tasks. Take advantage of the extensible graph data model as the foundation of data-centric architecture. Integrate models to discover new relationships and break down barriers between applications.

Luna is a subscription to the Apache Cassandra support and expertise at DataStax. It allows you to enjoy all the benefits of open-source Cassandra, with the peace of mind knowing you have direct access to the team that authored the majority of the code and supported some of the largest deployments in the world. Best practices, advice, and SLA-based support to keep your Cassandra deployment in top shape. Scale without compromising on performance or latency to seamlessly manage the most demanding real-time workloads. Create real-time and highly-interactive customer experiences with blisteringly fast read and writes. Luna provides assistance with resolving issues and following best practices with Cassandra clusters. Services provide help through the full application life cycle, with a deeper integration in your team working together on implementation.

Locstat is a graph intelligence platform with customer-driven industry and point solutions, incorporating graph-based AI, analytics and event processing, that enables organizations to rapidly scale next-generation data solutions. Research shows that by adopting innovative, AI-supported digitalization strategies an organization can realize considerable benefits and gains. We have achieved great success in improving customer efficiencies and delivered significant ROI as measured by us and confirmed by research companies. This illustrates the value of next-generation advanced analytics technologies in solving today’s complex problems more cost-effectively than solutions driven by relational databases.

Natively store data for graph, document and search needs. Utilize feature-rich access with one query language. Map data natively to the database and access it with the best patterns for the job – traversals, joins, search, ranking, geospatial, aggregations – you name it. Polyglot persistence without the costs. Easily design, scale and adapt your architectures to changing needs and with much less effort. Combine the flexibility of JSON with semantic search and graph technology for next generation feature extraction even for large datasets.

OrientDB is the world’s fastest graph database. Period. An independent benchmark study by IBM and the Tokyo Institute of Technology showed that OrientDB is 10x faster than Neo4j on graph operations among all the workloads. Drive competitive advantage and accelerate innovation with new revenue streams.