Entity Resolution at Scale: Graphs, LLMs, and Real-World Data Messiness

Data Engineering Podcast
This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.
Share on social media:
Loading transcript...
Introducing Steven Renick and TeloRes
What entity resolution means in practice
Where entity resolution matters most
Why LLMs help, but do not replace matching
Using graphs without graph databases
How teams should scope entity resolution work
Evolving rules, entities, and backfills
LLMs as prototypes and AI data infrastructure
Humanitarian use cases and messy real data
When not to use entity resolution
What comes next for entity resolution and AI
The missing trustworthy identity layer
Share on social media:
Listen in your favorite app:
Here are shows you might like
Elevate your AI agents from simple chat bots to powerful, research-driven systems with Parallel. While model releases dominate the conversation, the most critical tool for any modern agent is reliable, high-quality web context. Parallel provides the enterprise-grade infrastructure needed to seamlessly retrieve accurate information from the web, offering a suite of APIs designed for Pareto-optimal combinations of quality, speed, and cost. Whether your application requires instant, 200-millisecond latency for voice agents, balanced speed and depth for chat bots, or deep, overnight research for long-horizon tasks, Parallel is your single platform for agentic context. Beyond best-in-class search, our full platform includes Web Monitors, Deep Research tools, and Batch Record Enrichment. Get started with complimentary MCP and $5 in free credits, and discover the best quality web search solution, no matter your latency or scale needs. Get started for free at Parallel.ai.