Going beyond legacy ETL: Modern practices for connecting any data to GenAI

Learn the eight critical categories of GenAI data connectors, which matter most to deliver high performance with low overhead, and why traditional ETLs fall short.

Watch this webinar

Speakers

Nina Lopatina

Developer Relations Engineer, Unstructured

Roman Isecke

Senior Software Engineer, Unstructured

Robert Roskam

Senior Engineering Manager, Unstructured

Date of recording

Tuesday, March 4th

Overview

Connectors are super important and a complete waste of time for my team to build and maintain.” We agree with our customer’s statement. But, how you approach choosing connectors is pivotal, and buyer beware if you’re expecting the previous generation of ETL tools built for structured data to handle a messy mix of unstructured sources.

Technical Details

Join Technical Leaders from Unstructured for a deep dive into data connectors for generative AI applications. Our goal is to share bleeding-edge best practices to help you deliver production-ready results and keep your engineering team out of the weeds.

Watch this recording to learn:

Frameworks for evaluating ETL tools for GenAI use cases across eight critical feature domains: data synchronization, schema flexibility, security, scalability, error handling, metadata management, data quality, and cost efficiency.
Which connectors matter for GenAI applications, both on the source and destination side
Head-to-head comparisons between legacy players (Boomi, Fivetran, AirByte) and GenAI-native ETLs like Unstructured.
Best practices for handling a mix of structured/unstructured data feeding vector stores, knowledge graphs, and training pipelines.

Frameworks for evaluating ETL tools for GenAI use cases across eight critical feature domains: data synchronization, schema flexibility, security, scalability, error handling, metadata management, data quality, and cost efficiency.
Which connectors matter for GenAI applications, both on the source and destination side
Head-to-head comparisons between legacy players (Boomi, Fivetran, AirByte) and GenAI-native ETLs like Unstructured.
Best practices for handling a mix of structured/unstructured data feeding vector stores, knowledge graphs, and training pipelines.

Frameworks for evaluating ETL tools for GenAI use cases across eight critical feature domains: data synchronization, schema flexibility, security, scalability, error handling, metadata management, data quality, and cost efficiency.
Which connectors matter for GenAI applications, both on the source and destination side
Head-to-head comparisons between legacy players (Boomi, Fivetran, AirByte) and GenAI-native ETLs like Unstructured.
Best practices for handling a mix of structured/unstructured data feeding vector stores, knowledge graphs, and training pipelines.