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Maia is made up of three integrated components: Maia Foundation, Maia Team, and Context Engine. Maia Foundation provides the cloud-native execution infrastructure for building and running data pipelines. Maia Team operates within that infrastructure to build, maintain, and optimize pipelines using natural language. Context Engine supplies the institutional knowledge that Maia Team reasons against, so that the pipelines it produces are consistent with your data standards and business logic. These docs use “Maia” to refer to the platform as a whole. Where it’s necessary to distinguish between the three components, the full component name is used.

Maia Foundation

Maia Foundation is the cloud-native execution layer that underpins Maia. It provides the infrastructure for connecting to data sources, building and running pipelines, and governing data operations across your organization.

Designer

Designer is the visual canvas where you build and manage data pipelines in Maia. Pipelines are composed of connected components that you configure to load, transform, and orchestrate data. Designer uses a Git-based branching model so that multiple team members can work on pipelines simultaneously and promote changes through environments in a controlled way. For more information, read Using Designer.

Pipelines

Maia supports three pipeline types:
  • Orchestration pipelines define the sequence and control flow of data loading tasks, including conditional branching, iteration, and error handling.
  • Transformation pipelines reshape and prepare data within your warehouse using drag-and-drop components, without requiring you to write SQL.
  • Test pipelines run another pipeline and check its results, so you can verify that pipelines behave correctly before deploying them to production.
Maia Team can build all three pipeline types when you describe what you need in Mission Control or the Designer chat panel. To learn about pipelines and the components you can use to build them, read Building pipelines and Components overview. For information about test pipelines specifically, read Test pipelines.

Connectors

Maia Foundation includes over 150 connectors for loading data from sources into your cloud data platform. You can also create custom connectors to integrate any REST API that returns a JSON response. For more information, read Connectors overview and Custom connector overview.

Streaming

Maia Foundation supports near-real-time data collection through streaming pipelines. These pipelines capture and synchronize data changes from source databases and write them to a target data warehouse. For more information, read Streaming pipelines.

Runners

A Maia runner executes the tasks defined in your Designer pipelines. In a Full SaaS deployment, Matillion hosts and manages the Maia runner on your behalf. In a Hybrid SaaS deployment, you host the Maia runner in your own cloud environment (AWS, Azure, Google Cloud, or Snowflake). A Streaming runner handles execution of streaming pipelines. For more information, read Maia runner overview.

Deployment and security

Maia Foundation can be deployed as a fully Matillion-hosted SaaS solution, as a Hybrid SaaS deployment where the Maia runner runs in your own cloud, or natively inside Snowflake. All deployment models use a pushdown architecture, meaning data is processed within your cloud data platform and does not pass through Matillion infrastructure. Maia Foundation supports Single Sign-On (SSO), Multi-Factor Authentication (MFA), and Role-Based Access Control (RBAC). For compliance details, read Cloud platform security and compliance.

Maia Team

Maia Team is the agentic layer of Maia. It operates inside the Maia Foundation execution environment, interacting with Designer and your data platform directly on your behalf. You can communicate with Maia Team using natural language in the chat panels in Designer and Mission Control. Maia Team can build orchestration and transformation pipelines, create custom connectors, query and explore your data warehouse, manage project files, analyze and optimize existing pipelines, and commit and push changes to Git. Context Engine provides the knowledge base that Maia Team draws on during these tasks, so that the pipelines it builds reflect your organization’s data standards and business rules. Maia Team is enabled at account level by default, meaning all users in your organization can prompt it immediately. Admins can disable Maia Team or restrict its ability to sample data from the account settings. To learn more, read Maia Team overview.

Mission Control

Mission Control is the organizational center for Maia Team. Use it to create and manage autonomous tasks that Maia Team works through without requiring you to stay in the chat panel. When creating a task, you select a knowledge graph from Context Engine to give Maia Team the context it needs for that task. You can also create and manage tasks programmatically using the agent tasks API. For more information, read Mission Control and Using the agent tasks API.

Context Engine

Context Engine is the knowledge layer of Maia. It stores and structures institutional knowledge—including business definitions, compliance rules, data lineage, table and column relationships, and warehouse metadata—so that Maia Team can reason against it when building and maintaining pipelines. Knowledge is organized into knowledge graphs, each populated by crawlers that connect to your data warehouse and pipeline execution history on a schedule. Context Engine also learns from your chat history with Maia Team; each conversation can automatically update semantic information in the knowledge graph, so the knowledge base evolves as your data landscape does. For more information, read Context Engine.