Directory Data & AnalyticsData Integration & ETLMatillion

Matillion

About Matillion

Matillion is behind Maia, described as an agentic data team. Maia is an AI team that plans, builds, tests, runs, and fixes data pipelines while users steer. The company describes itself as data transformation software for cloud data warehouses.

Products

Maia Team: AI agents that plan, build, test, deploy, and fix data pipelines, with every plan reviewed by the user before it runs.
Context Engine: Applies a company's data, standards, and business rules so agents build according to those rules.
Maia Foundation: Provides connectors, orchestration, and governance across Snowflake, Databricks, BigQuery, and Redshift, formerly known as Data Productivity Cloud.
Mission Control: Used to manage the agentic data team, run tasks in the background, review plans, and see what is in flight.
Matillion ETL: A data integration and ETL platform built for cloud data warehouses.

Used for

Building and managing data pipelines
Moving, integrating, and managing data
Converting legacy ETL pipelines from other platforms to cloud data warehouses
Capturing and processing real-time data changes
Tracking data lineage from source to output

From Matillion's own website, read 2026-10-23. How we build listings.

How to evaluate Data Integration & ETL

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Matillion. It comes from our Data Integration & ETL/ELT buyer guide.

25%

Connector Breadth & Reliability

Coverage of your actual sources (SaaS apps, databases, files, events), depth of each connector (full vs. partial schemas, history), automated schema-drift handling, custom-connector SDK, and who maintains connectors when an upstream API changes

20%

Transformation Model & Engineering Workflow

ELT (in-warehouse SQL/dbt) vs. ETL (engine-side) vs. visual push-down; version control, testing, CI/CD, and lineage; reusability and modularity of models; and fit with your team’s SQL-vs-Python-vs-low-code balance

20%

Real-Time, CDC & Streaming

Log-based change-data-capture, achievable freshness, replication from operational databases, incremental-load handling, schema-evolution on streams, exactly-once semantics, and reverse-ETL/activation back to operational systems

15%

Governance, Lineage & Data Quality

Column-level lineage across the pipeline, in-line data-quality and validation, catalog/metadata integration, masking and PII handling, audit trails, and master-data or data-contract support for regulated environments

10%

Deployment, Security & Sovereignty

SaaS vs. self-hosted vs. hybrid/customer-VPC, where data and credentials reside, SOC 2 / ISO 27001 / HIPAA posture, private networking and BYO-cloud options, RBAC/SSO, and open-source vs. proprietary lock-in

10%

Cost Model & Predictability

Pricing unit (rows/MAR, capacity, credits, compute-hours, per-connector, per-seat), how cost behaves as volume and source count grow, warehouse-compute cost the tool pushes onto you, and whether spend is forecastable against your data growth

Other Data Integration & ETL Vendors

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