The Future of Vector Databases and the Rise of Instant Updates

1

Introduction

00:00 • 4min

2

The Evolution of Nearest Neighbor Clustering

03:39 • 2min

3

The Gap Between Real Time Analytics and Vector Search

06:04 • 2min

4

The Importance of Vectors in Real Time Analytics

07:55 • 4min

5

How Far Should the API Go in a Vector Database?

11:47 • 2min

6

The Importance of Scaling Your Data

13:44 • 2min

7

Why Open Source Is Better Than Managed?

15:17 • 2min

8

How to Use Vector Search to Find Your Nearest Neighbors

17:03 • 2min

9

The Importance of Vector Search and Analytics

19:16 • 2min

10

Roxy's Metadata Filtering for Vector Databases

21:05 • 2min

11

The Hard Problem With Vector Search

23:15 • 2min

12

Rock CB: The Storage Plane for Rock Set

24:58 • 2min

13

The Importance of Data Latency in Vector Search

26:50 • 2min

14

The Importance of Incremental Update and Scale in Vector Search

28:23 • 2min

15

The Core Value of Real-Time Analytics

30:49 • 2min

16

The Future of Databases

32:43 • 2min

17

The Difference Between Vector Search and Vector Databases

35:08 • 2min

18

The Pros and Cons of Hybrid Search

36:46 • 2min

19

How to Revise a Reference Architecture for Streaming

38:37 • 2min

20

Vector Search and Embeddings: The Future of Data Management

40:41 • 2min

21

The Future of Semantic Search

42:13 • 2min

22

The Future of Chat GPT

44:03 • 2min

23

The Future of AI and Machine Learning

45:49 • 2min