Why Anzo

Anzo is the most complete scalable knowledge graph platform in the world.

From its hyper agile in-memory MPP graph engine to its point-and-click user experience and open flexible architecture, Anzo transcends the limitations of traditional knowledge graphs and gives you all the capabilities and flexibilities that complex, enterprise-scale solutions need. Combined with Cambridge Semantics’ 80+ team of knowledge graph engineers, architects, and customer success experts, Anzo empowers you to quickly stand up your first knowledge graph while also laying the foundation for future expansion to add support for new domains, data sources, users, and use cases.

AnzoGraph, Anzo’s embedded MPP graph engine, analyzes large volumes of data in graph at unmatched speed.

AnzoGraph is an extremely high-performance MPP graph engine that can execute queries and deliver results against very large datasets in seconds, allowing more of an enterprise’s data to be used to drive pervasive analytics and digital transformation initiatives.

AnzoGraph supports enormous knowledge graphs comprising very large numbers of RDF triples by automatically sharding graph data across cluster servers and processing queries in parallel, making full use of all cores and memory. Because of its MPP and fast intra-cluster network implementation, the AnzoGraph engine maintains near-linear load and query performance as the data and cluster size grow. AnzoGraph features a distributed “shared nothing” architecture that is uniquely full MPP providing horizontal scalability.

Anzo works in concert with AnzoGraph to automate scalable graph pipelines that create and optionally persist graph data in compressed files on commodity storage. AnzoGraph rapidly loads graph data into memory in parallel from persisted graph storage or directly from source systems such as RDBMS, Hive, HDFS, APIs, or other graph databases.

As a complete platform, Anzo lets users build a knowledge graph in days or weeks, rather than months.

Anzo features an easy-to-use GUI and a complete set of built-in best practice tools and workflows that automatically complete core tasks in a collaborative knowledge graph development process, reducing risk, speeding deployment, and resulting in better, more impactful business solutions.

Anzo supports virtualization as well as onboarding and straight-to-memory loading of data from source systems.

When deciding how to best link metadata and data from source systems into a knowledge graph, numerous considerations come into play including cost and performance, geographic- and industry-specific data privacy regulations, data licensing constraints, data latency/freshness, and data security policies.

As a complete knowledge graph platform, Anzo meets this need by giving enterprise architects flexible options to connect source systems with the knowledge graph including data onboarding pipelines, virtualization, and straight-to-memory loading options.

With Anzo, any data in the knowledge graph can be integrated and enhanced with semantics in a graph model, regardless of where the data actually resides and how it is connected to the knowledge graph platform. Moreover, the connection approach to a given source can be changed without losing any of the existing mappings and modeling that bind that source into the knowledge graph.

Anzo makes data preparation and exploration extremely fast and agile with in-memory querying.

Anzo’s in-memory data integration, transformation, and visualization architecture allow users to quickly and iteratively experiment with different data modeling and manipulation processes to build optimized datasets in minutes.

You can select and group any data from Anzo’s catalog into a Graphmart to be loaded into memory for immediate transformation, including creating new links between related but previously siloed data elements, applying cleansing, transformation, or validation steps, or applying analytics. The Graphmart organizes these steps into an easy to manage recipe, called Data Layers.

Using Graphmarts, you can quickly experiment with and tune Data Layer workflow steps and sequences to generate views of data perfect for your applications. And since Anzo is applying Data Layers in AnzoGraph’s memory, your experimentation process is quick, iterative, and agile and no extra copies of the data are made.

Anzo adds a semantic layer to the knowledge graph, making it more integrated and easier to understand.

Anzo natively uses the W3C RDF semantic graph standards for data and metadata. These future-proof data standards support the use of business-oriented concepts, properties, and relationships natively in the knowledge graph, making the data more integrated and easier to understand and use.

The semantic standards also foster interoperability between systems and platforms, and allow for flexible ongoing modification and extensions to the semantic model in response to new data sources, changing business requirements, and unanticipated questions.

Anzo uses metadata to build knowledge graphs that are automated, intelligent, and scalable.

Anzo’s metadata catalog combines technical, operational, and business metadata to comprehensively describe and manage all aspects of the knowledge graph, including lineage that reflects the complete end-to-end data journey.

Users access Anzo’s metadata in various reports, visualizations, workflows, APIs, and query building tools to make the data in the knowledge graph more transparent, trustworthy, and easier to understand and use. Anzo also uses this metadata to increasingly automate building and managing the knowledge graph to provide organizations speed and scale for large implementations.

Used in combination with data profiles and algorithms, Anzo’s metadata enables advanced logic and fuels increasingly intelligent operations in the matching, integration, cleansing, and transformation of data from source systems within the knowledge graph. The same metadata is also available for use alongside data with Anzo’s many built-in libraries of analytic and data science primitives and toolkits.

Anzo supports multiple cloud deployments and native cloud architectures, using Kubernetes and OpenShift

Anzo supports a variety of native or hybrid cloud deployment options on commercial cloud platforms including OpenShift, Google Cloud Platform, Microsoft Azure, and Amazon Web Services.

Flexible deployment models allow you to customize where you deploy major components of Anzo.

Anzo uses clusters of commodity VMs or Kubernetes to execute a variety of tasks, including data onboarding and high-speed in-memory data modeling, transformation, and analytics. These clusters can be on-premises or in the cloud. Anzo uses Kubernetes to automatically spin up or down Anzo computing resources to support specific user-driven knowledge graph tasks and to automate cloud infrastructure for data management operations.

Anzo affords enterprise architects options to persist subsets of the knowledge graph to disk for performance benefits. Anzo can use your existing storage locations including NFS, Hadoop Distributed File Systems (HDFS), Azure Data Lake storage, Google Cloud Platform (GCP) storage, and Amazon Simple Cloud Storage Service (S3).

Our accelerated insight and time to value differentiators.

Semantic Layer and Graph Foundation

Semantic Layer and graph models offer inherent flexibility, pushing the modeling, integration, and analytics closer to the end-user, reducing time to value in dynamic business environments.

In-Memory Speed/Scale

Anzo has unique horizontally scalable ingestion and query capabilities that channel the power of cloud computing into accelerating value.

On Demand Data Access & Analytics

Anzo’s Discovery and Analytics tools make it fast and easy to explore and analyze all of your data, including unstructured, using your own tool sets.