Welcome to this week's new features blog! We're bringing you a packed set of improvements that make Maia more powerful and intuitive than ever—from enhanced data exploration and expanded cloud support to streamlined variable management and brand-new pipeline testing. For all the details, read our docs changelog.

☁️ Maia runner for Google Cloud

The Maia runner for Google Cloud has been released, expanding hybrid deployment options beyond AWS and Azure. This new runner enables you to seamlessly integrate your Google Cloud infrastructure with Snowflake through Maia's hybrid architecture.

You can create a Google runner through the Runner manager, just like other runners. To streamline deployment, our public repository includes Terraform templates specifically designed for deploying the runner into Google Kubernetes Engine (GKE), providing a comprehensive Kubernetes-based deployment experience.

For more information, read our GKE deployment guide and explore the deployment templates in our public deployment library.

🔧 Public API now supports Azure DevOps

You can now provision Maia projects backed by Azure DevOps repositories directly through the Maia API. Once provisioned, Maia operates on your Azure DevOps repository as a machine user. This expansion gives you more flexibility in how you integrate Maia with your existing development workflows and repository management systems.

The Project Provisioning API guide walks you through the one-time prerequisite of granting the service principal access to your Azure DevOps organization and shows you how to use "provider": "azure-devops" in your request body.

🚀 Guided custom connector setup with Maia Team

Setting up custom connectors in Maia just got easier! You can now create custom connectors in Designer without interrupting your workflow, simply by asking Maia Team to help.

In the Maia Team chat interface, ask for help setting up a custom connector, and Maia Team will help you get started without ever leaving the Designer canvas. Maia Team will guide you through custom connector creation and can automatically add the new custom connector to your pipeline, making data ingestion from new sources faster and more intuitive.

🔐 Cloud credential environment overrides and APIs

We've released significant updates to cloud credentials that provide greater flexibility and automation capabilities:

• Cloud credentials now support environment overrides directly in the UI, giving you better control across different deployment stages.
• The Connections API endpoints can now manage cloud credentials programmatically, enabling zero-touch provisioning workflows for AWS, Azure, and GCP credentials.
• Combined with the Project Provisioning API endpoints, you can now fully automate the setup and management of your cloud infrastructure.

Learn more: Check out our updated Cloud provider credentials documentation and the Project Provisioning guide.

🧪 Pipeline testing is now live

You can now create tests for your pipelines! These enable you to perform tests with mock inputs, comparison tests for competitive migration equality, and operational tests like idempotency and temporal behavior.

To get started, make sure that Test suggestions is toggled in in your Maia Team chat interface settings. Maia Team’s built-in skill will then offer to create tests when building a new pipeline. Plus, when you make changes to a pipeline, it will automatically flag any existing tests that are affected.

Tests can also be triggered in bulk via the Maia API Test Execution endpoints. Check out the API reference at that link, or our Test pipelines documentation to learn more.

🖌️ Designer quality of life improvements

We've released a host of improvements for all users in Maia, ranging from variable management to sampling, and from warehouse data to pipeline notes. Read on for all the details!

📐 Manage variables while chatting with Maia Team

Managing variables in Maia just became much more user-friendly! The Variables panel has been relocated from the left panel in Designer to a tab at the bottom of the canvas. This means you can now manage variables while keeping Maia Team visible at the same time.

All variable capabilities remain fully available in the new location, including pipeline variables, project variables, grid variables, and environment overrides. Changes save automatically, eliminating the need for a separate save step.

🧩 Select variables in component properties

You can now search for and add pipeline variables directly in any ‘dual listbox’ type parameter, such as the Column Name parameter in the Table Input component.

Dual listboxes now let you search for and select variables alongside static options. Variables appear in the same picker and can be added to the selection like any other item. This solves a common workflow challenge where you could reference variables in single-value fields but not in multi-value parameters.

🔍 Explore warehouse data without leaving Maia

These improvements let you inspect tables and navigate across databases without interrupting your pipeline work:

• In transformation pipelines, the Schemas panel now shows column details and sample rows when you click a table or view.
• If you work in an environment that spans multiple databases, a database picker is now available when browsing warehouse data, letting you switch databases without changing your environment configuration. This drop-down is available in the Schemas panel and the Warehouse data tab when adding data directly to a pipeline.

🎯 Sampling filter is always visible

The filter field in the Sample data tab in Designer is now visible as soon as a component has been validated, rather than only appearing after a successful sample has been taken. As a result:

• You can now enter a data filter before taking your first sample, perfect for when you already know the filter you want to apply.
• If a sample request fails, the filter bar and retry controls stay on screen, allowing you to correct your input and resample without interruption.

🗒️ And a whole lot more…

We’ve also made the following improvements in Designer, designed to eliminate friction when building pipelines, making the design process smoother and more intuitive:

• Skip, copy, or delete multiple pipeline components at once — Selecting two or more components on the canvas now shows an action toolbar so you can act on the whole group in a single click.
• Shift-click to fine-tune a canvas selection — Shift-clicking an already-selected component now removes just that item from a multi-selection.
• Copy and paste canvas notes — Notes can now be copied and pasted on the canvas, including multi-note selections, so you can duplicate annotations as easily as pipeline components.
• Multi-select note action bar improvements — When multiple canvas notes are selected, a single unified toolbar applies color and other formatting changes to the whole group in one action.

📤 Post-processing and grid variable updates

You can now use grid variables in orchestration component post-processing, and export grid variables from child to parent pipelines. This new feature allows you to reuse values from child pipelines in parent pipelines, update grid variables after an orchestration component runs, and import existing Matillion ETL pipelines that use grid export features more easily. Interested? Read our Post-processing guide for more information.

💬 We'd love to hear from you!

Let us know how these new features are improving your workflows — we're all ears! Feel free to add any comments or questions below.

Want to get involved? Join the Matillion Community to stay up to date, share feedback, and help shape our product roadmap for future innovations.

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📚 Introducing docs.maia.ai

We're excited to launch a major upgrade to how you access and interact with Matillion’s product documentation. This week, we announced docs.maia.ai, the new home for all of our product and API documentation for Maia (formerly Data Productivity Cloud).

docs.maia.ai is a modern, responsive docs site that we believe delivers a significantly improved experience for accessing Maia documentation.

Key improvements include:

• Subscribable changelog: Stay up-to-date with automatic notifications via RSS feed for our product and API changelogs. We’ve made our changelogs available in two formats: by year and by runner tracks. Check them out:
  • 2026 changelog (use the navigation if you want to look back through 2025, 2024, and 2023 releases).
  • Stable runner track changelog (current coming soon!)
• Interactive API playground: Test API endpoints directly in your browser with our native OpenAPI spec rendering, no plugins required.
• Cleaner reading experience: Modern, responsive design that works seamlessly across all devices, including mobile.
• Streamlined maintenance: More efficient content delivery as Maia continues to scale.
• Improved navigation: Find the content you want faster than before.

The new site includes integrated AI-powered support for answering your questions, and all previous documentation links automatically redirect to the new platform.

What are you waiting for? Explore docs.maia.ai today and use the thumbs-up/thumbs-down and feedback form to let us know what you think or how we can improve.

Terminology changes

Matillion originally launched Maia in June 2025 as the agentic AI workforce inside Data Productivity Cloud with the goal of providing our customers with the capabilities to exponentially increase the rate of efficiency and productivity with which they do data engineering.

In early 2026, we made the decision at Matillion to rebrand “Data Productivity Cloud” as a whole to “Maia” to clearly reflect our mission: to build the best AI data automation platform in the world.

Additionally, with the rise of agentic AI, we have opted to rename “agents” to “runners” to free up the term “agent” for AI-related nomenclature.

The following terminology changes have been made across our documentation and other assets (Academy, Certification, Support, etc) to reflect updates in our product naming:

Additionally, to reflect the broader scope of the name “Maia” as it replaces “Data Productivity Cloud”, the term “Maia AI Agents” now reflects the AI capabilities of Maia. You might also see Maia AI Agents referred to in our marketing material as Maia Team.

Matillion ETL documentation

While we have launched docs.maia.ai to supercharge your documentation experience for Maia, Matillion ETL users can still make use of docs.matillion.com, which remains the home of our product and API docs for Matillion ETL.

💬 We'd love to hear from you!

Let us know how these new features are improving your workflows—we're all ears! Feel free to add any comments or questions below.

Want to get involved?

Join the Matillion Community to stay up to date, share feedback, and help shape our product roadmap for future innovations.

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Welcome back to the Maia New Features Blog! This week, we're excited to deliver updates that enhance your observability experience, make troubleshooting more intelligent, and unlock new programmatic possibilities with Maia. Let's explore what's new in our changelog updates.

🤖 Maia AI Agents' public API is here

We're excited to introduce the Agent Tasks API — Maia's first public API that lets you programmatically create, monitor, and manage tasks. This new capability allows you to send instructions to Maia AI Agents and have them executed as background processes. You now have the same level of control and agentic power via the API as you do when chatting to Maia AI Agents in Designer.

The API enables you to integrate Maia AI Agents’ problem-solving capabilities into your own workflows, CI/CD pipelines, or custom scripts—allowing Maia AI Agents to work through complex instructions as a background service.

What's new:

• From chat to API: Transition from manual 1-to-1 conversations to programmatic task creation. Define an objective for Maia AI Agents and manage the entire work lifecycle via the API.
• Asynchronous background tasks: Unlike a chat session that requires you to stay in the window, API tasks run independently. Send a request, receive a task ID, and check back whenever you're ready to review the output.
• Programmatic decision handling: Maia AI Agents still pause for safety when high-impact actions are needed. You can approve tool usage or answer clarifying questions through the dedicated endpoint to keep the task moving.
• Full Designer parity: You maintain the same level of granular control you have in the Designer—including the ability to manage permissions, track reasoning, and iterate on work—giving you complete command over Maia AI Agents' actions via the API.

How do I use it?

To get started, create a task by providing an initial instruction—this serves as your first prompt to Maia AI Agents. Once the task is created, you will receive a task ID which allows you to monitor the task’s status, handle decisions, and iterate on the task using follow-up messages.

Check out our API reference to try out the new endpoints, or read our guide to Using the Agent Tasks API for more details.

🔍 Root cause analysis gets smarter with enhanced pipeline visibility

Root Cause Analysis (RCA) has received significant improvements to make it more accurate and useful when pipeline failures occur. The enhanced RCA now provides comprehensive visibility across your entire pipeline infrastructure.

Key improvements include:

• Complete pipeline visibility: RCA now analyzes failures inside iterators and orchestration steps, not just top-level pipeline issues.
• Enhanced failure detection: Pipeline-level failures such as specific SQL errors are now captured and analyzed.
• Richer contextual analysis: Warning-level agent task logs are now included to provide the lead-up context needed to understand why failures occurred.
• Structured, actionable output: Every identified issue now includes a category and a clear indication of whether it's fixable within your pipeline.

For more information about Root Cause Analysis, check out our documentation.

💾 Saved views in Pipeline Run History

Pipeline Run History just got a major productivity boost! You can now save your filter sets and set a default view, eliminating the need to reapply filters every time you visit this page. Simply apply and save a view, and it’s there every time you need it.

💬 We'd love to hear from you!

Let us know how these new features are improving your workflows—we're all ears! Feel free to add any comments or questions below.

Want to get involved? Join the Matillion Community to stay up to date, share feedback, and help shape our product roadmap for future innovations.

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This week, we're excited to bring you powerful new controls for pipeline execution and significant improvements to Maia's troubleshooting and Git capabilities. These changelog updates are designed to give you more flexibility in managing your data workflows and a smoother development experience overall.

🛑 Stop on condition for iterator components

We're excited to announce the addition of Stop On Condition functionality across our iterator components, giving you greater control over your pipeline execution.

What this means for you:

• Stop iteration mid-loop based on a configurable condition, across the File, Fixed, Grid, Loop, and Table iterator components.
• Build flexible conditions using a range of comparators including Less Than, Equal To, Greater Than, Blank, and more.
• Combine multiple conditions using And/Or logic to handle more complex stopping criteria.

If you’re upgrading from Matillion ETL to Maia, read our Iterators upgrade guide for more information about how Stop On Condition works in Maia.

🤖 Maia AI Agents update: Enhanced pipeline troubleshooting and Git workflow

Maia AI Agents continue to evolve with several key improvements that enhance your development experience. The latest updates include smarter pipeline troubleshooting capabilities that can drill into nested pipelines and iterator runs to identify failing steps, page through long step lists, and filter by status.

The Git workflow has been significantly improved thanks to Maia AI Agents’ new ability to directly inspect branch changes before committing, commit selected files individually, and separate push operations from commits. You'll now see dedicated "Git status" and "Git diff" actions, each with their own approval prompts.

💬 We'd love to hear from you!

Let us know how these new features are improving your workflows—we're all ears! Feel free to add any comments or questions below.

Want to get involved?

Join the Matillion Community to stay up to date, share feedback, and help shape our product roadmap for future innovations.

This week, we're excited to introduce powerful new API capabilities for shared pipelines, enhanced variable support for Maia AI agents, and Terraform for Streaming runners and pipelines, making your data workflows more automated and intelligent.

🔌 New Public API endpoints for shared pipelines

We're excited to announce the availability of a new set of public API endpoints, allowing you to manage the full lifecycle of your shared pipelines programmatically.

What you can do:

• Publish versions: Publish new shared pipeline artifact versions directly from CI/CD pipelines
• List and filter: List and filter published versions by source project or specific pipeline
• Retrieve details: Retrieve detailed information about any version, including the latest by default
• Enable or disable versions: Enable or disable specific versions to support rollback and controlled rollouts
• Browse pipelines: Browse all shared pipelines in an account, including their latest enabled version and description

See the API documentation and how-to guides for further details:

• Shared pipeline artifacts API endpoints
• Understanding shared pipeline artifacts
• Publishing shared pipeline artifacts with the API

🤖 Maia AI agent update: Improved variable support

We're happy to introduce system variable and project variable support, expanding your Maia AI agent's understanding of the variables available in your project.

Why is this helpful?

When building pipelines, knowing which variables are already defined—and what system variables are available at runtime—saves time and prevents duplication. Maia AI agents can now check what exists before creating something new.

What's new:

• System variables reference: Maia AI agents now know the available system variables (${sysvar.})—environment defaults, pipeline context, component metadata, and more. Ask your Maia AI agent which variables are available for your platform and it'll guide you to the right one.
• Project variable awareness: Maia AI agents can list your project-level variables, showing their name, type, behavior, and default value—so it can reference existing configuration instead of creating redundant pipeline variables.

How do I use it?

Maia AI agents will automatically use system variables and check your project variables when building pipelines—no special prompting needed. For more control, you can use context files to define standards like which project variables to use, helping Maia AI agents stay consistent across your pipelines.

🏗️ Public Terraform provider now available for Streaming runners and pipelines

Creating multiple Streaming runners and pipelines just got significantly more scalable! The new public Terraform provider eliminates the need to manually create runners and pipelines one at a time through the UI—a process that can be extremely time-consuming when dealing with dozens of databases.

With this Terraform provider, you can now programmatically create, update, and delete Streaming runners and pipelines at scale. This is particularly valuable for organizations managing large numbers of data sources, transforming what was once a lengthy, repetitive manual process into an automated, efficient workflow.

The provider includes comprehensive examples and covers both runner and pipeline configuration. While client-side and local infrastructure requirements may vary, this solution provides a solid foundation that can be customized to meet your specific needs.

Check out the Streaming pipelines documentation to get started.

💬 We'd love to hear from you!

Let us know how these new features are improving your workflows—we're all ears! Feel free to add any comments or questions below.

Want to get involved? Join the Matillion Community to stay up to date, share feedback, and help shape our product roadmap for future innovations.

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Autonomous Migration for Legacy ETL Platforms

New capability converts legacy ETL pipelines from 14 platforms to modern cloud data warehouses — compressing multi-quarter migration programs into weeks.

Denver and Manchester, UK – March 26 2026 — Matillion today announced the public preview of Migration Agent, a new capability within Maia, its flagship AI Data Automation platform, that autonomously converts legacy ETL pipelines into native, warehouse-optimized pipelines on Snowflake, Databricks, and Amazon Redshift.

Migration Agent addresses one of the most persistent and costly challenges in enterprise data modernization: the inability to move off legacy ETL platforms without committing to expensive, multi-quarter consulting engagements. With Migration Agent, teams can convert pipelines from 14 legacy platforms — including Informatica PowerCenter, Alteryx, IBM DataStage, SSIS, Oracle ODI, SAS Enterprise Guide, and dbt — with no manual rewrite and no GSI dependency.

How it works

Unlike conventional migration tools that replicate legacy logic into a new environment, Migration Agent performs a predictable, structured conversion. Maia parses the original transformation logic, dependency graphs, and pipeline metadata, then reconstructs each pipeline as a native, warehouse-optimized ELT pipeline. Unsupported or ambiguous constructs are flagged explicitly for human review, ensuring transparency and correctness throughout.

Engineers move from builders, manually recreating pipeline logic step by step — to managers, inspecting generated pipelines that already preserve the original system's logic and structure.

Read the full news article here.