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Vector DBs, Knowledge Graphs, Data Fabric, and Why Process Still Rules (w/ Terry Dorsey)
Manage episode 520453315 series 3579845
Maven Analytics द्वारा प्रदान की गई सामग्री. एपिसोड, ग्राफिक्स और पॉडकास्ट विवरण सहित सभी पॉडकास्ट सामग्री Maven Analytics या उनके पॉडकास्ट प्लेटफ़ॉर्म पार्टनर द्वारा सीधे अपलोड और प्रदान की जाती है। यदि आपको लगता है कि कोई आपकी अनुमति के बिना आपके कॉपीराइट किए गए कार्य का उपयोग कर रहा है, तो आप यहां बताई गई प्रक्रिया का पालन कर सकते हैं https://hi.player.fm/legal।
In this episode, we're joined by Terry Dorsey, Senior Data Architect & Evangelist at Denodo, to unpack the conceptual differences between terms like data fabrics, vector databases, and knowledge graphs, and remind you not to forget about the importance of structured data in this new AI-native world!
What You'll Learn:The difference between data fabrics, vector databases, and knowledge graphs — and the pros and cons
Why organizing and managing data is still the hardest part of any AI project (and how process design plays a critical role)
Why structured data and schemas are still crucial in the age of LLMs and embeddings
How knowledge graphs help model context, relationships, and "episodic memory" more completely than other approaches
If you've ever wondered about different data and AI terms, here's a great glossary to check out from Denodo: https://www.denodo.com/en/glossary
Register for free to be part of the next live session: https://bit.ly/3XB3A8b
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