This session will provide a Maia demo with roadmap teasers. The demo will showcase Maia's core capabilities: authoring pipelines in business language, multiplying productivity by accelerating tasks, and enabling self-service. It demonstrates how Maia takes natural language prompts and translates them into YAML-based, human-readable Data Pipeline Language (DPL), generating graphical pipelines. Expect to see Maia interacting with Snowflake metadata to sample data and suggest transformations, as well as its ability to troubleshoot and debug pipelines in real-time. The session will also cover how Maia can create custom connectors from REST API documentation in seconds, a task that traditionally takes days . Roadmap teasers will likely include the upcoming Semantic Layer, a Pipeline Reviewing Agent, and enhanced file type support for various legacy ETL tools and code conversions.

This session will explore the evolving role of data engineers. Data engineering is currently a bottleneck due to overwhelming requests and complex knowledge work. Maia acts as a "digital data engineer" or a "virtual data team" that amplifies productivity by 100x. It enables users, from skilled engineers to citizen data analysts, to author pipelines in natural business language. The session will demonstrate Maia's ability to accelerate mundane and advanced tasks,troubleshoot and debug pipelines in real-time, and generate high-quality, auditable pipelines using Matillion's proprietary, human-readable Data Pipeline Language (DPL), which avoids "spaghetti code" common with generic LLMs.