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Solutions Review's annual compilation of the best Graph Databases reflects the current market landscape, assessing products based on the Authority Score, a meta-analysis of real user sentiments from trusted review sites. This curated list aids buyers in navigating the complex process of choosing the right Graph Database for their organizational needs, considering factors beyond technical capabilities. Here's an in-depth exploration of some of the top 10 Graph Databases featured in the list:
Description:
Amazon Neptune, a fully managed graph database service, empowers users to build and run applications handling highly connected datasets. It boasts a purpose-built, high-performance graph database engine optimized for storing vast relationships and executing rapid graph queries. Supporting graph models like Property Graph and W3C's RDF, Neptune is recommended for use cases such as fraud detection and network security.
Description:
AnzoGraphDB by Cambridge Semantics is a massively parallel processing graph database designed to accelerate data integration analytics. Offering over 40 functions for regular line-of-business analytics, along with graph and data science algorithms, it facilitates in-graph feature engineering and transformations.
Description:
DataStax Enterprise, built on Apache Cassandra, provides a distributed hybrid cloud database. Simplifying the exploitation of hybrid and multi-cloud environments eliminates complexities associated with deploying applications across various data centers or public clouds.
Description:
Dgraph is a graph database solution offering a single schema approach to development. Users can create a schema, deploy it, and access fast database and API functionalities without code. Dgraph supports GraphQL or DQL, making it accessible to users with no prior graph database experience. With features like simple data import, data streaming, and Dgraph Lambda, it simplifies business logic implementation.
Description:
IBM Graph is an enterprise-grade property graph as a Service built on open-source database technologies. Enabling the storage, querying, and visualization of data points, connections, and properties, ensures an always-on service. Designed for scalability, organizations can start small and scale on demand as data complexity increases.
Description:
MarkLogic Server focuses on unifying silos of data, making it ideal for applications involving heterogeneous large-scale data integration or content delivery. With a flexible data model adapting to changing data, it natively stores JSON, XML, text, and geospatial data. MarkLogic's Universal Index allows searching across all data, while APIs support application development and deployment.
Description:
Azure Cosmos DB, a fully managed NoSQL database service, is tailored for modern application development. Backed by SLAs, automatic scalability, and open-source APIs for MongoDB and Cassandra, it accommodates spiky workloads and offers serverless alternatives to provisioned throughput.
Description:
Neo4j offers a graph database that enables organizations to decipher data relationships among people, processes, and systems. Natively storing interconnected data simplifies the understanding of complex relationships. The property graph model facilitates the evolution of machine learning and AI models, supporting high-performance graph queries on large datasets.
Description:
Oracle Spatial and Graph, part of the converged database offering, is available within the Oracle Autonomous Database. This solution automates graph data management, simplifying modeling, analysis, and visualization across the data lifecycle. With support for both property and RDF knowledge graphs, it allows interactive graph queries directly on graph data or in a high-performance memory graph.
Description:
OrientDB Enterprise is a NoSQL database management system written in Java, offering multi-model support for graph, document, key/value, and object models. Managing relationships like graph databases supports direct connections between records.
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