Trainings

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All Trainings

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Performance

Performance

55:41

Learn how Stardog uses memory, how to monitor metrics, server settings, and options for performing data loads. Understand how to enable self-diagnosis of a performance issue, how query execution is performed, and how to best identify query performance problems.

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Reasoning

Reasoning

47:55

Review how to make implicit information explicit through the use of reasoning, and how to unveil this information. You’ll learn how reasoning helps derive new facts or inferences from the existing dataset based on definitions in the data model and a set of applicable rules.

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Getting Started with RDF & SPARQL

Getting Started with RDF & SPARQL

27:31

Review the basics of RDF data including graph, triples, nodes, IRIs, prefixes, datatypes, and Turtle. By the end of this training, you will understand how to construct SPARQL queries, demonstrate the use of SPARQL to create or update RDF data, and know how to work with named graphs.

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Python

Python

17:57

Learn how to manage and query Stardog with Python, a popular language for data science applications. Create a Python virtualenv and install pystardog, manage the Stardog server with pystardog, and query Stardog.

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Database Administration

Database Administration

38:54

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.

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Java

Java

28:44

Discover 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.

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GraphQL in Stardog

GraphQL in Stardog

8:27

Learn how GraphQL works in Stardog, and how to query Stardog with GraphQL. Understand how GraphQL schemes are handled using Stardog’s “data model” utility to automatically generate a GraphQL schema from RDF data.

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Data Quality

Data Quality

43:10

Review the concepts and importance of data quality. Learn how to assess quality requirements for various kinds of data, and then act on data quality reports.

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Working with Text

Working with Text

26:08

Review how to use the full text search feature within Stardog, and how to leverage and extend Stardog’s full text search feature. By the end of this training, you will understand how to turn text into information for querying and linking.

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JavaScript

JavaScript

11:03

Learn how to install the Stardog.js npm package and review the details of what is contained within the package. View the steps to creating a database, adding data within Stardog, and how to drop a database into Stardog.

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Architectures

Architectures

20:51

During this training you will gain an understanding of Stardog deployment options including single nodes and clusters. We’ll review the Stardog ecosystem including typical patterns within different Stardog architectures, common architecture design choices, and the fundamentals of Stardog Cloud and Stardog Connectors.

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Security

Security

23:45

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.

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High Availability

High Availability

28:28

This 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.

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.NET

.NET

30:23

Learn 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.

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Data Science + Machine Learning

Data Science + Machine Learning

51:25

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.

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BI/SQL Server

BI/SQL Server

19:26

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.

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Advanced RDF & SPARQL

Advanced RDF & SPARQL

26:37

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.

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Accessing Stardog Data with Power BI

Accessing Stardog Data with Power BI

7:53

Watch this Stardog training to learn how to use the BI Connector to access Stardog within Power BI.

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Accessing Stardog Data with Tableau

Accessing Stardog Data with Tableau

5:03

Watch this Stardog training to learn how to use the BI Connector to access Stardog within Tableau.

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Virtual Graphs

Virtual Graphs

25:29

Learn how to define and apply Virtual Graphs, including reviewing the new data source feature, Virtual Graph mappings, applying virtual transparency, leveraging named graph aliases, and applying caches to Virtual Graphs.

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Modeling

Modeling

54:01

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.

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