Overview
Matillion has shifted its center of gravity from Matillion ETL (METL) to Maia – its AI-native data automation platform – and every METL customer will eventually need to make that move.
As a Matillion Gold Partner with a expertise on Change Management, CloudEQS has built a structured five-step migration framework to help customers transition from an open-ended re-platforming project into a scoped, predictable engagement.
The framework prioritizes lower-risk workloads first, uses Matillion’s own migration tooling to automate pipeline translation, and closes with a formal cutover strategy that retires the legacy METL workload within zero business impact.
Context
Matillion built its reputation on Matillion ETL (METL), the on-instance orchestration and transformation tool that a generation of data teams used to move workloads into Snowflake, Redshift, Databricks, and BigQuery. Now, Matillion has a successor to METL – Maia, an agentic AI-native data integration platform. No instances to patch, no infrastructure to size, AI build within the product, and elastic scaling that removes a class of operational overhead METL customers have carried for years.
This shift creates real pressure on METL customers. Matillion ETL releases follow a defined support lifecycle — long-term support versions are backed for a fixed window before they reach end of life — so METL environments that stand still eventually run on unsupported versions. At the same time, new capability investment is going into Maia, not METL, which means the gap between what METL can do and what the platform is capable of will only widen.
None of this makes migration urgent in the sense of a hard shutoff date, but it does make migration inevitable, and the customers who plan for it now have far more control over timing, scope, and risk than those who wait for a support deadline to force the decision.
The risk most teams underestimate is not the technology — it’s the sequencing. A METL environment accumulates years of custom pipelines, shared jobs, variables, credentials, driver configurations, and pipeline dependencies that aren’t obvious from the orchestration canvas alone. Thus, migrating without first inventorying that footprint, identifying opportunities to deprecate, and without a defined testing, cut-over and change management processes tend to delay projects, increase operational costs, and reduce trust.
That’s the gap CloudEQS’s framework is built to close.
CloudEQS METL Migration Framework
CloudEQS runs every Matillion ETL–to–Maia engagement through the same five-step process so customers can see exactly what’s being delivered at each stage and where the engagement stands at any point.
Step 1 — Inventory Discovery & Workload Prioritization
We start by finalizing a complete inventory of METL pipelines, jobs, connectors, schedules, and integrations. During inventory, we also flag pipelines that are candidates for deprecation rather than migration, so the team isn’t re-platforming workloads the business no longer needs.
From there we sequence the remaining workloads from least to most critical, so the migration approach is proven on lower-risk pipelines before it’s applied to anything the business depends on daily. The step closes by formally initiating change management, so stakeholders and downstream consumers know what’s coming and when.
Step 2 — Environment Set Up
Next, we stand up the target Maia Foundation environments — both Dev and Prod — and connect them to source control with Git integration from day one.
We configure the access layer that every migrated pipeline will rely on: roles, API profiles, and JDBC drivers, along with password managers, so credentials are centrally managed rather than scattered across pipeline configurations.
Getting this foundation right up front is what makes the Re-platform step move quickly instead of stalling on environment issues discovered mid-migration.
Step 3 — Re-platform Following Wave Releases
With the environment ready and workloads sequenced, we migrate pipelines in the priority order established during Discovery.
Wherever possible, we leverage Matillion’s own migration tooling to automatically translate METL pipelines into their Maia equivalents, which meaningfully reduces manual rework compared to rebuilding pipelines by hand.
Every migrated pipeline goes through unit testing to confirm it behaves correctly in isolation, followed by integration testing to confirm it behaves correctly inside the broader pipeline dependency chain — not just that it runs, but that it produces the same results the business already trusts.
Step 4 — UAT & Documentation
Before anything moves to production, business and technical stakeholders validate the migrated workloads through user acceptance testing against real use cases.
In parallel, we document the migrated environment — configurations, dependencies, and any logic that changed in translation — so the team inheriting the platform has a clear record of what moved and how.
This step ends with a formal UAT sign-off.
Step 5 — Cutover
The final step is the production release of the migrated workloads to go live on Maia as the system of record. Any additional development for this workload is transitioned to Maia.
Once production is confirmed stable, the customer sunsets the corresponding Matillion ETL workload — retiring legacy infrastructure in step with each wave rather than letting old and new environments run in parallel indefinitely.
Summary
CloudEQS has developed a structured five-step framework to help Matillion ETL customers migrate to Maia with lower risk, predictable scope, and minimal business disruption.
Ready to move from METL to Maia? Connect with CloudEQS to assess your environment and build a low-risk, predictable migration plan.
Comments are closed