DBMS > AnzoGraph DB
AnzoGraph DB System Properties
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|Editorial information provided by DB-Engines|
|Description||Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization|
|Primary database model||Graph DBMS|
|Current release||2.2, August 2020|
|License Commercial or Open Source||commercial free trial version available|
|Cloud-based only Only available as a cloud service||no|
|DBaaS offerings (sponsored links) Database as a Service|
Providers of DBaaS offerings, please contact us to be listed.
|Server operating systems||Linux|
|Data scheme||Schema-free and OWL/RDFS-schema support|
|XML support Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.||no|
|SQL Support of SQL||SPARQL and SPARQL* as primary query language. Cypher preview.|
|APIs and other access methods||Apache Mule|
OData access for BI tools
RESTful HTTP API
|Supported programming languages||C++|
|Server-side scripts Stored procedures||user defined functions and aggregates|
|Partitioning methods Methods for storing different data on different nodes||Automatic sharding|
|Replication methods Methods for redundantly storing data on multiple nodes||Multi-source replication in MPP-Cluster|
|MapReduce Offers an API for user-defined Map/Reduce methods||Kerberos/HDFS data loading|
|Consistency concepts Methods to ensure consistency in a distributed system||Immediate Consistency in MPP-Cluster|
|Foreign keys Referential integrity||no not needed in graphs|
|Transaction concepts Support to ensure data integrity after non-atomic manipulations of data||ACID|
|Concurrency Support for concurrent manipulation of data||yes|
|Durability Support for making data persistent||yes|
|In-memory capabilities Is there an option to define some or all structures to be held in-memory only.||yes|
|User concepts Access control||Access rights for users and roles|
|More information provided by the system vendor|
AnzoGraph DB is a massively parallel processing (MPP) native graph database built for diverse data harmonization and analytics at scale (trillions of triples & more), speed and deep link insights. Use it for embedded analytics that requires graph algorithms, graph views, named queries, aggregates, built-in data science functions, data warehouse-style BI and reporting functions.
AnzoGraph DB’s flexible data model and analytical capabilities not only let you load diverse data sets but can perform data warehouse-style analytics, graph algorithms, inferencing and more. It offers a broad set of analytics on a wide range of data, delivered at unparalleled speed and scale.
Our engine supports labeled properties under the new proposed W3C standard RDF*. Apply properties to vertices and edges for extra analytical firepower. You can intermix RDF and RDF* triples in the same database with no special setup.
Anzograph DB supports geospatial analytics through a partnership with ESRI. We also support virtualization, allowing users to leverage data sets that are not necessarily in the database.
Utilize unique architecture with ultra-fast data loading and super-fast analytical queries, and where performance and data volume capacity handling linearly scale as you add servers. Easily scales to trillions of triples. ACID compliance lets you handle transactions, too.
|Typical application scenarios|
Developers can use AnzoGraph DB to build and execute data warehouse-style BI analytics while leveraging our graph algorithms, data science functions and inferencing.
Knowledge Graph - Organizations are using graph databases to build knowledge graphs to provide common business understanding to the data harmonized from diverse sources. Knowledge Graphs stores entities and relationships in data and allows users to search, analyze and use this connected data to accelerate vital new discoveries.
Unstructured Data Analytics - Combined with Natural Language Processing (NLP), graph database offers a free-form repository to store the output of NLP, which is often formatted in RDF triples and use of such data for data discovery and analytics.
AI & Machine Learning - AI and machine learning offer workloads that are well-suited for graph databases. Many of the machine-based algorithms are graph algorithms such as community detection algorithms, pathfinding algorithms, similarity or centrality algorithms.
Anzograph DB has customers in biotech, pharma, financial services and other verticals.
|Licensing and pricing models|
AnzoGraph DB is offered in a free edition, free for commercial use for workloads that use less than 16 GB. Commercial pricing is based primarily on the number of nodes needed to perform workload.
Related products and services
We invite representatives of vendors of related products to contact us for presenting information about their offerings here.
|Recent citations in the news|
The 7 Best Graph Databases to Consider for 2021
AnzoGraph review: A graph database for deep analytics
Cambridge Semantics Fits AnzoGraph DB with More Speed, Free Access
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