Database Administration
This beginner-level training teaches the foundational concepts of Stardog Database Administration. This training will review user interfaces, Stardog Studio and CLI, and basic data base operations like how to create/load/drop, backup/restore, repair/optimize, view/change db status, and view/kill running queries. The training will conclude with an overview of Stardog server administration and how to configure memory, log, and run as a Linux service.
Java
This intermediate-level training will cover the two main APIs Stardog has to offer to connect to the server and manage data, called SNARL and Stark. SNARL stands for Stardog Native API for the RDF Language and is the main API used to create connections to Stardog and perform CRUD operations and administrative tasks. Stark provides utilities and interfaces for writing parameterized queries, managing constraints, RDF graph statements, and Axioms. More about Stardog APIs:
GraphQL in Stardog
This intermediate-level training will cover how GraphQL can be used in Stardog. The differentiations between GraphQL, RDF, and SPARQL will be reviewed. From there, you’ll learn how GraphQL works in Stardog, and how to query Stardog with GraphQL. By the end of this training you’ll understand how GraphQL schemes are handled using Stardog’s “data model” utility to automatically generate a GraphQL schema from RDF data. More about Stardog APIs: Stardog provides a rich set of application programmer interfaces, or APIs, to work with the product.
Data Quality
This intermediate-level training reviews the concepts and importance of data quality. You will learn how to assess quality requirements for various kinds of data, and then act on data quality reports. Finally, you will learn how to operate Stardog to ensure quality of integrated data.
Security
This beginner-level training teaches Stardog’s security model in detail. During the training, you will become familiar with Stardog’s Role-Based Access control implementation, including creating new Users and Roles, and how to assign permissions to each. Learn the specifics of Named Graph Security, specifically how named graph permissions work and how to restrict access to them, as well as the basics of LDAP integration. By the end of the training, you’ll understand the requirements for running Stardog with SSL and how to deploy Stardog securely.
High Availability
This advanced-level training covers Clustering for High Availability. The training explains the elements of a Stardog cluster and teaches how to build a Stardog cluster with ZooKeeper and a load balancer. The trainer will show you how standby and cache nodes work with examples and review setting up a cache node for a Virtual Graph.
.NET
This intermediate-level training teaches how to use Stardog in your .NET solutions. This training will teach you how to connect to and query Stardog using .NET, including using TrinityRDF to create ontology mappings. More about Stardog APIs: Stardog provides a rich set of application programmer interfaces, or APIs, to work with the product. All actions seen in Stardog Studio or in the Stardog product command line interface can be viewed as interacting with one of these APIs.
Data Science + Machine Learning
This intermediate-level training teaches Stardog’s Machine Learning capabilities for the data science domain and predictive analytics. Learn how to build a Machine Learning model and use it for prediction, as well as best practices on modeling your data and evaluating accuracy and quality of your results. Review Machine Learning definition and the steps of Machine Learning model development lifecycle, Stardog Machine Learning services and their implementation, and various types of Machine Learning model approaches.
Modeling
This beginner-level training teaches the basics of successful data modeling for developing an Enterprise Knowledge Graph. Learn about the various types of data models used across the graph domain with a specific focus on ontology models, a particular model type frequently used in knowledge graph development. Review the step-by-step process of model development and learn the practical considerations for ontology development, including use of naming scheme, ontology modularity, and versioning, and testing and debugging.