This intermediate-level training teaches you how to manage and query Stardog with Python, a popular language for data science applications. You can access your Stardog knowledge graph using Python and our wrapper library called pystardog. Learn how to create a Python virtualenv and install pystardog, manage the Stardog server with pystardog, and query Stardog. By the end of this training, you’ll be able to demonstrate pystardog in a Jupyter notebook.
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.
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.
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.
This intermediate-level training covers BI/SQL server within Stardog. During this training, you’ll learn how relational and graph databases work together. By the end of the training you will have an understanding of the Stardog BI/SQL server, used this feature through various implementation options, and understand the concept of the SQL Mapping Syntax.
Advanced RDF & SPARQL
This intermediate-level training teaches several advanced features within Stardog. Learn how Stardog extends SPARQL to find paths between nodes in the RDF graph, which we call path queries. Review how to use full text-search, Stardog Studio’s Provenance, and Stardog’s GeoSPARQL for spatial data. By the end of this training, you’ll be able to centrally manage your queries using Stored Query Service (SQS) and write flexible queries using Named Graph Aliases. You’ll also have an understanding of Stardog’s support for edge properties, and how to best bridge the gap between the RDF data model and the Property Graph data model.
Accessing Stardog Data with Power BI
Watch this Stardog training to learn how to use the BI Connector to access Stardog within Power BI. For additional information please reference our BI Tools FAQ page.