Powering Vector Search With Real Time And Incremental Vector Indexes

Summary

The rapid growth of machine learning, especially large language models, have led to a commensurate growth in the need to store and compare vectors. In this episode Louis Brandy discusses the applications for vector search capabilities both in and outside of AI, as well as the challenges of maintaining real-time indexes of vector data.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management

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Your host is Tobias Macey and today I'm interviewing Louis Brandy about building vector indexes in real-time for analytics and AI applications

Interview

Introduction

How did you get involved in the area of data management?

Can you describe what vector search is and how it differs from other search technologies?

What are the technical challenges related to providing vector search?

What are the applications for vector search that merit the added complexity?

Vector databases have been gaining a lot of attention recently with the proliferation of LLM applications. Is a dedicated database technology required to support vector indexes/vector search queries?

What are the use cases for native vector data types that are separate from AI?

With the increasing usage of vectors for data and AI/ML applications, who do you typically see as the owner of that problem space? (e.g. data engineers, ML engineers, data scientists, etc.)

For teams who are investing in vector search, what are the architectural considerations that they need to be aware of?

How does it impact the data pipeline strategies/topologies used?

What are the complexities that need to be addressed when updating vector data in a real-time/streaming fashion?

How does that influence the client strategies that are querying that data?

What are the most interesting, innovative, or unexpected ways that you have seen vector search used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on vector search applications?

When is vector search the wrong choice?

What do you see as future potential applications for vector indexes/vector search?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Machine Learning Podcast helps you go from idea to production with machine learning. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.

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Links

Rockset

Podcast Episode

Vector Index

Vector Search

Rockset Implementation Explanation

Vector Space

Euclidean Distance

OLAP == Online Analytical Processing

OLTP == Online Transaction Processing

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Sponsored By:Rudderstack: 

Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack)Materialize: 

You shouldn't have to throw away the database to build with fast-changing data. Keep the familiar SQL, keep the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date.

That is Materialize, the only true SQL streaming database built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable — all in a familiar SQL UI. Built on Timely Dataflow and Differential Dataflow, open source frameworks created by cofounder Frank McSherry at Microsoft Research, Materialize is trusted by data and engineering teams at Ramp, Pluralsight, Onward and more to build real-time data products without the cost, complexity, and development time of stream processing.

Go to [materialize.com](https://materialize.com/register/?utm_source=depodcast&utm;_medium=paid&utm;_campaign=early-access) today and get 2 weeks free!Hex: 

Hex is a collaborative workspace for data science and analytics. A single place for teams to explore, transform, and visualize data into beautiful interactive reports. Use SQL, Python, R, no-code and AI to find and share insights across your organization. Empower everyone in an organization to make an impact with data. Sign up today at dataengineeringpodcast.com/hex to get a 30-day free trial of the Hex Team plan!Datafold: 

This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting [dataengineeringpodcast.com/datafold](https://www.dataengineeringpodcast.com/datafold) today!Support Data Engineering Podcast