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.
Hello and welcome to the Data Engineering Podcast, the show about modern data management
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)
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)
You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and 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. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free!
If you’re a data person, you probably have to jump between different tools to run queries, build visualizations, write Python, and send around a lot of spreadsheets and CSV files. Hex brings everything together. Its powerful notebook UI lets you analyze data in SQL, Python, or no-code, in any combination, and work together with live multiplayer and version control. And now, Hex’s magical AI tools can generate queries and code, create visualizations, and even kickstart a whole analysis for you – all from natural language prompts. It’s like having an analytics co-pilot built right into where you’re already doing your work. Then, when you’re ready to share, you can use Hex’s drag-and-drop app builder to configure beautiful reports or dashboards that anyone can use. Join the hundreds of data teams like Notion, AllTrails, Loom, Mixpanel and Algolia using Hex every day to make their work more impactful. Sign up today at dataengineeringpodcast.com/hex (https://www.dataengineeringpodcast.com/hex) to get a 30-day free trial of the Hex Team plan!
Your host is Tobias Macey and today I'm interviewing Louis Brandy about building vector indexes in real-time for analytics and AI applications
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?
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Thank you for listening! Don't forget to check out our other shows. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used.
Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected]
)) with your story.
To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers
Podcast Episode (https://www.dataengineeringpodcast.com/rockset-serverless-analytics-episode-101/)
Vector Index (https://www.datastax.com/guides/what-is-a-vector-index)
Vector Search (https://www.datastax.com/guides/what-is-vector-search)
Rockset Implementation Explanation (https://rockset.com/videos/vector-search-architecture/)
Vector Space (https://en.wikipedia.org/wiki/Vector_space)
Euclidean Distance (https://en.wikipedia.org/wiki/Euclidean_distance)
OLAP == Online Analytical Processing (https://en.wikipedia.org/wiki/Online_analytical_processing)
OLTP == Online Transaction Processing (https://en.wikipedia.org/wiki/Online_transaction_processing)
The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)