Maia Glossary

A class of artificial intelligence capable of autonomous reasoning and action. Unlike standard Generative AI which creates content, Agentic AI plans, executes, and manages complex workflows to achieve high-level goals.

The use of Agentic AI to plan, build, and optimize data pipelines. Learn how it moves beyond simple automation to provide intelligent, goal-oriented execution, and how Maia uses safe abstractions to reduce manual toil.

Cloud data warehouses are high-performance, elastic storage and compute environments designed specifically for your large-scale analytics and BI. They enable you to decouple storage from compute, move from rigid ETL to agile ELT.

Context Engineering stops AI from hallucinating. It's the infrastructure that designs what facts an LLM holds when it answers, not just how you ask the question.

The process of transporting data from diverse sources into a storage medium, such as a Data Warehouse or Data Lake, for immediate use or downstream transformation.

ELT is the cloud-native data integration standard, extract your data, load it raw into your warehouse, then transform it there using the warehouse's own compute power. Learn how it differs from traditional ETL, why it's faster, and how Maia's AI agents are now automating the entire process.

ETL, Extract, Transform, Load, is the foundational process behind how raw data becomes usable insight. It moves data from disparate source systems into a centralized warehouse, where it can be analyzed, trusted, and acted on.

A collaborative workflow where AI systems perform the heavy lifting but operate within a transparent environment that allows for human verification. It bridges the gap between the speed of automation and the reliability of expert oversight.

A critical property of data pipelines ensuring that re-running a job produces the same result as running it once, preventing data duplication during retries.

A modular, cloud-native architecture that decouples ingestion, storage, and transformation to enable scalable, AI-ready analytics.

An AI architecture where specialized agents (like Engineers, Analysts, and Ops) collaborate to solve complex data problems autonomously, rather than relying on a single, rigid script.

Schema drift occurs when the structure of a data source changes unexpectedly, such as added columns, removed fields, or modified data types, causing traditional pipelines to break or silently ingest incorrect data.

A semantic layer is an abstraction layer that sits between complex data sources and business users, translating technical database schemas into clear, consistent business terminology. It allows non-technical teams to query data using terms like "Annual Recurring Revenue" instead of navigating raw SQL tables and joins.

Serverless data integration is a cloud-based architectural pattern where data pipelines are built, executed, and scaled without requiring manual management of servers or infrastructure. Pipelines scale automatically while engineers focus on delivering outcomes, not keeping the lights on.

Data democratization is the process of making data accessible and queryable for every member of an organization, regardless of their technical background. By removing the engineering bottleneck, it transforms data from a guarded technical asset into a shared utility for faster business impact.