Maia is the AI Data Automation platform by Matillion. It autonomously creates, manages and evolves data products and pipelines for humans and AI agents at scale.

What is AI Data Automation?

AI Data Automation is a category of platform where AI agents take on the work of building, running and maintaining data pipelines. Instead of teams hand-coding each pipeline, AI agents do the work end-to-end under human governance.

How is Maia different from traditional ETL tools like Informatica or Alteryx?

Traditional ETL tools require manual pipeline development and ongoing maintenance. Maia uses AI agents to design, build and evolve pipelines automatically, and accelerates migration off legacy ETL platforms.

Which cloud data warehouses does Maia support?

Maia supports Snowflake, Databricks, Amazon Redshift and Google BigQuery, with pushdown processing inside the customer's own cloud data warehouse.

Your AI Enterprise Data Team

Maia handles the entire data lifecycle, so your team can deliver trusted data products at scale.

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▤ Files ▾ O create-star-schema N

MaiaSelect Knowledge Layer ▾ ⊕ Edit context ✎ ↺ ⋮

Maia

Your team of expert AI agents

◍

Help me build a pipeline

Create a new data pipeline from scratch

◍

Summarize my pipeline

Understand your existing pipeline

▦

Set up a skill for me

Adapt Maia's responses to your workflow

Update this pipeline to take into account slowly changing dimensions (type 2 and 3) on core customer attributes

› Thought

Switch to plan mode

✓Maia wants to switch to plan mode

Implementing SCD Type 2 and 3 on customer dimensions requires understanding the current pipeline structure, identifying customer-related components, and determining the best approach for slowly changing dimensions.

› Performed 3 actions

Now I have a clear picture. The pipeline creates a star schema with SQL executors, and DIM_CST_PROF (the customer dimension) needs SCD Type 2 and Type 3 support on core attributes. Let me propose a plan:

✓Maia has a plan for your review

Add SCD Type 2 & 3 to the customer dimension

1Add 5 SCD columns to DIM_CST_PROF
2Type 2 (full history): seg_cd, ltv_idx, sts_flg
3Type 3 (keep previous): eml_ref, ph_hsh
4Seed one historical row (segment change)
5No change to FCT_TXN_LGR

Applying changes to DIM_CST_PROF…

✓Done — DIM_CST_PROF restructured for SCD Type 2 & 3. Review the diff before you commit.

▦ Skills (1) ▾

Ask Maia, use / for commands, or @ to explore more

@ Plan Mode

(x)

⊕

Review ▾ Validate Schedule Run

Start

Create Schema

✓

DIM_CST_PROF

SCD 2 & 3 · +5 cols

DIM_PRD_CTL

DIM_GEO_LOC

DIM_TM_PRD

Wait For Dims

FCT_TXN_LGR

▤ Files ▾ T Sales Performance Metrics.tran N

MaiaSelect Knowledge Layer ▾ ⊕ Edit context ✎ ↺ ⋮

Maia

Your team of expert AI agents

◍

Help me build a pipeline

Create a new data pipeline from scratch

◍

Summarize my pipeline

Understand your existing pipeline

▦

Set up a skill for me

Adapt Maia's responses to your workflow

build a CRM pipeline

› Thought

?Maia needs your input

What would you like to build from your CRM data?

✓Sales performance metrics — win rates, deal velocity, revenue by rep

✓Customer segmentation — group customers by value, industry, behavior

✓Pipeline analytics — stage conversion, forecasting

✓Allow Maia to query the warehouse (read-only)

Discover available CRM tables

SELECT TABLE_NAME FROM "JOHN_SMITH"."INFORMATION_SCHEMA".TABLES

› Performed 4 actions

I queried your warehouse and found ACCOUNT, OPPORTUNITY and CONTACT in Snowflake, alongside your Salesforce and Customer 360 data. No existing pipeline matches — I'll build the transformations from scratch.

Building from your context, not a blank slate. I'm applying your GWF standards — the CRM_ table prefix, medallion layer suffixes, and .tran.yaml naming.

Building Sales Performance Metrics.tran — placing components…

✓Built Sales Performance Metrics.tran — 7 components, writing to CRM_SALES_METRICS. Review before you run.

▦ Skills (0) ▾

Ask Maia, use / for commands, or @ to explore more

@ Plan Mode

(x)

⊕

Review ▾ Validate Schedule Run

Load Opportunities

Load Accounts

Join Opps to Accounts

Calculate Deal Velocity

Sales Metrics by Owner

Calculate Win Rate

Write Sales Metrics

→ CRM_SALES_METRICS

▤ Files ▾ T failed_payment_rate failed_payment_rate.test N

MaiaSelect Knowledge Layer ▾⊕ Add context

Maia

Your team of expert AI agents

◍

Help me build a pipeline

Create a new data pipeline from scratch

◍

Summarize my pipeline

Understand your existing pipeline

▦

Set up a skill for me

Adapt Maia's responses to your workflow

Please create tests for this pipeline

Performed 5 actions

No tests exist yet for this pipeline. I'd like to clarify a few things before creating one:

Maia needs your input1/2

How would you like to set up test data for the PAYMENTS table?

Mock data (Recommended) — create a small set of test rows with known values

Clone existing data — zero-copy clone PAYMENTS into the test schema

Other

Maia needs your input2/2

What should the test verify about the output?

Row count — assert expected number of rows

Column names and types — assert the output schema

Data values — assert specific output values

Performed 6 actions

I've created a separate test pipeline — failed_payment_rate.test — so your original pipeline stays untouched. It spins up an isolated schema, generates mock payments, runs the pipeline, asserts the results, then tears the schema down.

Running test in an isolated schema…

✓All assertions passed — 3 rows (2 processors × 2 dates), schema matched, failure rates correct. Test green.

▦ Skills (0) ▾

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@ Plan Mode

Review ▾Validate ⊘Schedule ◷ Run ▶

Failed Payment Rate

Calculates the daily failure rate per payment processor. Groups payments by date and processor, counts total vs failed, then writes results to payment_failure_summary.

Failed Payment Rate Test

Creates mock PAYMENTS data with known failure rates, runs the pipeline in a temporary schema, then asserts: 3 output rows, correct failure rates, expected column schema.

Read Payments

Flag Failed

Aggregate by Date & Processor

Calculate Failure Rate

Rename Columns

Write summary

Start

Create Test Schema

Create Mock Payments

Run Pipeline

✓

Assert Row Count

✓

Assert Output Values

Join Assertions

Drop Test Schema

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Maia

Matillion ETL

Pipeline

Project ▽

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Environment ▽

Artifact version

Triggered by

Started at

Duration

Feature_Store_Setup

Fraud_Detection

Demo

Success

snowflake_summit_2026_spin_workshop_schema_162

089b5083-ba82-4807-ae59-3cc56cb12076

Jordan Blake

16:07:06

2026-06-23

~7s

run_customer_segmentation

spin/finance/segmentation

Demo

Success

host

8a6bf959-6f9d-4e1c-b702-d7ee7ee3f94c

Alex Rivera

15:32:52

2026-06-12

~42s

run_customer_segmentation

spin/finance/segmentation

Demo

Success

host

07260228-e0a6-4b74-8201-eb3ad5ec3f5c

Alex Rivera

15:30:22

2026-06-12

~44s

run_customer_segmentation

spin/finance/segmentation

Demo

Failed

host

bc438bf8-13f0-424f-a971-d97501cbbb1a

Alex Rivera

15:29:47

2026-06-12

~13s

run_customer_segmentation

spin/finance/segmentation

Demo

Failed

host

db67a137-92d6-4537-989f-55b8fcd1dca9

Alex Rivera

15:28:58

2026-06-12

~19s

run_customer_segmentation

spin/finance/segmentation

Demo

Failed

host

70eeb588-3977-476a-adcf-c6e15cc69b3c

Alex Rivera

15:28:30

2026-06-12

~17s

run_customer_segmentation

spin/finance/segmentation

Demo

Success

host

957add18-7e1f-4da2-8e7c-5553a45bf678

Alex Rivera

15:24:19

2026-06-12

~16s

Back to pipeline run history

Pipeline run details Failed

spin/finance/segmentation/run_customer_segmentation.orch.yaml

Troubleshoot with Maia. Review root causes and see recommended fixes.

Summary of issue(s)

The pipeline is failing because a target column SEGMENT_CD is expected but not present in the data being processed. This error occurs during query execution with variables in Snowflake. [MLUserError] Target column SEGMENT_CD is not present in the data. Issues in detail — Issue 1: Missing target column SEGMENT_CD …

Project

Demo

Started

2026-06-12 at 15:29:47

Completed

2026-06-12 at 15:30:00

Source

Designer

Environment

host

Artifact version

bc438bf8-13f0-424f-a971-d97501cbbb1a

Duration

~13s

Show more Copy details

Steps with errors

Pipeline

Component

Started

Duration

Row count

Message

Root cause analysis

spin/finance/segmentation/run_customer_segmentation.orch.yaml

Here's what was found in the pipeline.

Summary of issue(s)

The pipeline is failing because a target column SEGMENT_CD is expected but not present in the data being processed. This error occurs during query execution with variables in Snowflake.

[MLUserError] Target column `SEGMENT_CD` is not present in the data.

Issues in detail

Issue 1: Missing target column SEGMENT_CD in data

Category: Data Fixable in pipeline: Yes — update the pipeline to either provide the missing column or adjust the query/transformation logic.

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# general

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# data-eng-alerts 1

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# product

# releases

# data-eng-alertsMaia pipeline health · production · automated alerts only

Wednesday, 7 May

Maia APP 7:22 AM

🔴 Schema drift — campaign_lead_enrichment

Column renamed in the Salesforce source broke the pipeline reference — run failed. Downstream pipelines on hold. Fix task created in Mission Control.

Failed component

extract_crm_contacts

Error

Column 'contact_email' not found in source

Classification

Schema drift — column renamed

Maia confidence

High — deterministic schema diff

Mission Control

Monitor, review, and act on tasks

All projects ▾Last 7 days ▾⌕ Search tasks…

Backlog 3

Add row count assertion — stg_lead_enrichment

maia/dq-rowcount-e4a1b3f2d1

Document pipeline logic — nightly_customer_sync

maia/docs-pipeline-c2b8a9e4c7

Implement safe rerun checkpoint — campaign_lead_enrichment

maia/checkpoint-f7d3d1a8f2

In progress 0

Needs attention 1

Fix schema drift — campaign_lead_enrichment

maia/schema-fix-20260507c5e9b3

Completed 3

Fix schema drift — campaign_lead_enrichment

maia/schema-fix-20260507

completed just now

Resolve null handling — src_crm_contacts

maia/null-fix-a3c1

e2b4d9 · completed 2026-05-06

Add freshness alert — mart_campaign_targets

maia/freshness-b8f2

f7a1c4 · completed 2026-05-05

Update retry config — salesforce_lead_sync

maia/retry-d4e7

a3d6f1 · completed 2026-05-04

Fix schema drift — campaign_lead_enrichment

View Pipeline✕

Schema drift detected — campaign_lead_enrichment · 07:22 AM

Thinking…

Thought▸

Classification

Schema drift — column rename

Confidence

High — deterministic schema diff

Column change

contact_email → email_primary

Branch

maia/schema-fix-20260507

What Maia will change

Update column reference in extract_crm_contacts from contact_email to email_primary, then re-run the extract.

The transformation layer and all downstream logic are untouched. This is a single-line change.

Downstream pipelines on hold (3)

stg_lead_enrichment — write blocked, holding last clean extract

mart_campaign_targets — refresh blocked, depends on staging

lead_segment_refresh — scheduled 09:00, on hold

All three will release automatically once the extract completes successfully.

✓Fix approved. Extract re-running on maia/schema-fix-20260507 — 3 downstream pipelines released.

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Data Quality

Shift-left data quality that validates and cleanses to keep integrations trusted and resilient

Data Engineering

Design, build and maintain data pipelines, end-to-end within a secure, governed architecture

Connectivity

Connect, extract and sync from anywhere, integrate everywhere - with APIs, secure authentication and reverse ETL

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Keep data flowing: pinpoint failures, automate debugging, RCA and remediation to own your SLAs

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Cut costs, spot bottlenecks: performance tuning and optimization to keep data pipelines under budget

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Quick, automated migration from legacy ETL/ELT to transparent, testable, cloud-ready data pipelines

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data work

AI demand is rising fast, but manual data work slows teams down-Maia automates the work so teams can shift from maintenance to building what matters and turning more ideas into business impact.

Without Maia

Hard-coded pipelines and brittle models

Manual backfills, fixes, and documentation

Weeks to deliver new datasets

Drowning in ad-hoc requests

No time for strategic work

Fragmented tech stack

With

Delegate pipeline creation and changes via prompt

Maintain documentation automatically

Embed agentic data engineers directly with analytics teams

Scale pipelines and teams without adding headcount

Fulfill data requests in hours, not weeks

Migrate and modernize legacy systems with AI

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“Maia offers a glimpse into the future of data engineering. It’s intuitive, powerful, and feels like a real accelerant for how teams build with data.”

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Sridhar Ramaswamy

CEO at Snowflake

Enable every data use case

Code conversion

Screenshot of Maia app showing a step-by-step pipeline creation interface on the left and a visual workflow diagram on the right that handles SCD types with components like NORM_EVENT_REF, RANK, LATEST, SCD Types, joins, and dimension events.

Migrate and optimize legacy code

SQL, SparkSQL, dbt and Python optimization and conversion

Data quality and data ops

User interface showing a data pipeline with boxes labeled Day Spine to Day Dimension and Month Spine to Month Dimension, and a commit changes panel on the right with branch 'void', modified changes for Data Spine plus Data Dimension and Kafka, and a commit message about updating date range and poll timeout.

Data cleaning and preparation

Git based data ops

CICD pipeline that includes auditability, auto-documentation and lineage

Legacy ETL migration

User interface of Maia showing a converted pipeline with components: Sample Data, Filter Audits, Rename Columns, and detailed conversion summary on the left.

Convert from Alteryx, Informatica, Talend and Qlik to the cloud

Reduce costs and modernize for AI

Data engineering and analysis

Screenshot of Maia analytics interface showing a healthcare analytics pipeline validation with components Encounter Fact, Patient Dim, Date Dim joined in Join Star Schema leading to Monthly Aggregate, alongside chatbot text explaining successful pipeline validation and metrics.

ETL/ELT development

Citizen data engineering for data analysts

Pipeline optimization for existing processes

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Maia Foundation provides enterprise data tools for any workload with built-in security, elastic scale, and centralized control.

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Data professionals love Maia

Maia automates manual data work for teams at global enterprises and fast-growing startups alike.

“Maia is helping us become an AI-ready organization by transforming how we build pipelines. In some cases we’ve seen pipeline build time go from 2 days to 10 minutes.”

John Tentomas

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Roberto Lara

VP of Digital Transformation & Analytics

Precision Medicine Group

“The biggest impact of Maia is our data engineers embracing it and helping us work smarter, not harder.”

Ammad Baig

Director of Enterprise Data & AI Services

Precision Medicine Group

"Maia is like having an autonomous data engineering team in digital form. It handles everything from legacy ETL migrations to building complex, production-ready pipelines at machine speed, and the quality of the logic is something we can trust. It’s dramatically accelerating our workflow while reducing the manual overhead."

Global Head of Data Transformation

"With Maia, our teams no longer wait in queues for data. Business users are self-serving insights securely, and our engineering team is now focused on strategy, not support. It’s the first real GenAI platform that understands our data reality."

Chief Data Officer

Insurance Company

This is a reimagining of data engineering and ETL - we’re rethinking what’s possible. With Maia, our analysts can build and debug complex pipelines using natural language, whether in the UI or directly in SQL and Python. It’s incredible to see non-engineers creating production-ready workflows without relying on our dev team.

“Maia replaced thousands of hours of manual work, helped us de-risk audits, and let business teams generate insights in days instead of months. It’s the first AI investment that delivered value fast.”

Group CFO

Global Financial Services Firm

"Maia feels like having a team of junior data engineers who never sleep. We’re a small team, but with Maia we’re punching well above our weight."

Dan Adams

Global Analytics Manager @ Edmund Optics