ETL Structured and Unstructured Data into a Knowledge Graph with Tom Sawyer Data Streams 1.0

What to expect

During this live webinar, attendees will learn how to:

• Subscribe to Apache Kafka (or Confluent) topics, then transform and persist those events into a knowledge graph model
  
  
• Automatically extract schemas from Kafka topics, refine them in a visual editor, and apply advanced filters and rules with Spring Expression Language (SpEL)
  
  
• Use the web-based designer to define sources, transformations, conditions, and sinks to build and monitor data flows
  
  
• Run flows in batch or continuously to ingest and transform events as they arrive
  
  
• Persist transformed data to a graph database and publish results back to Kafka for downstream services
  
  
• Pair Data Streams with Tom Sawyer Perspectives or Tom Sawyer Explorations for advanced graph and data visualization and analysis
  
  
• Discover how Data Streams can prepare and process data for LLMs, validate generative AI outputs, and unify agentic results in a governed single source of truth

Interactive Q&A session

Attendees will have the opportunity to engage with our host during the interactive Q&A session. Have your questions answered in real-time and gain deeper insights.

Who should attend

• Chief Data Officers

• Data Architects and Engineers

• Knowledge Graph Engineers

• AI/ML Engineers

• LLM Pipeline Developers

• Anyone interested in knowledge graphs, data streaming, and LLMs