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Legacy ETL migration
Automated, end-to-end data modernisation that gets you on an AI-ready data platform in weeks.
Many enterprises are trying to run AI projects on data infrastructure built for the past. Informatica, Talend, SSIS, and stored procedures were good enough in a world where data moved slowly and queries could wait.
Now, AI agents need governed, trustworthy data that only a modern data platform can support.
To help you get there, Flowline automates the migration from legacy ETL to a modern stack on dbt and Fivetran, with dependency-aware sequencing, row-level reconciliation, and AI-driven refactoring built in from the start.
AI agents in the enterprise stack
A reliable agentic enterprise has three layers:
- An AI layer of agents that reason, retrieve, and act.
- A business context layer that gives those agents a structured understanding of your organisation's own concepts and metrics.
- A modern data platform that is fast, governed, and built for AI workloads.
The data platform is where everything starts. Without it, the business context layer has nothing reliable to sit on, and agents have no trustworthy data to query.
It is also the layer that takes the longest to get right, which is why the enterprises that move earliest have the most to gain. Flowline’s ETL migration capability is how you build that base.
End-to-end modernisation
coverage
clients
How Flowline migrates your legacy data
Every Flowline engagement follows four stages in sequence. Each one has a defined output and a clear entry point for the next. There are no open-ended timelines and no surprises mid-project.
Convert
Flowline crawls the estate, maps dependencies, converts to dbt and Fivetran with AI-driven refactoring.
Reconcile
Before anything moves to production, Flowline validates the converted workload row by row against the source, building confidence in the numbers.
Refactor
Flowline applies best practices across the modernised estate, tuning for performance and cost on Snowflake or Databricks. The output is clean, maintainable code that engineers can work with from day one.
Adopt
We train your team and hand over the platform with the first-pass ontology already in place. Flowline agents manage the estate autonomously, while your engineers spend their time building on the platform.
The advantages of automated migration
Manual migration relies on engineers reading, interpreting, and rewriting legacy code by hand. That approach is slow, inconsistent, and leaves no guarantee that the output matches the original. Flowline changes the economics of migration across every dimension that matters.
Dependency-aware
sequencing
Flowline maps the full dependency graph of your estate before conversion begins. The migration sequence is calculated automatically, so pipelines move in the right order without breaking downstream dependencies.
Deterministic coverage
99% of your estate is converted reliably, with no gaps left to manual interpretation. Flowline handles the platforms your engineers no longer want to work with, including PowerCenter, IDMC, SSIS, and Talend, with purpose-built conversion logic for each.
Row-level validation
Every output is reconciled against the source before production cutover. The confidence you hand to your stakeholders is backed by evidence, not assumptions.
AI-driven refactoring
Conversion alone does not make a pipeline good. Flowline refactors for performance and cost as part of the same motion, applying consistent best practices across the entire estate rather than leaving quality to vary by engineer.
Business ontology
as a byproduct
Because Flowline reads the logic embedded in your legacy pipelines to convert it, it also surfaces it. The first-pass ontology it produces is a structured record of your organisation's business concepts, ready to be refined into the semantic layer your agents need.
Ongoing autonomous
management
After cut-over, Flowline agents manage the modernised estate going forward. Schema changes, pipeline updates, and ongoing optimisation are handled automatically, so the burden of maintenance does not simply transfer from one system to another.
Is your data infrastructure ready for AI?
Take a 5-minute assessment that tells you exactly where you stand, what is holding you back, and what it would take to modernise. You will get a clear picture of what the potential looks like on the other side to help you build your business case.
Free ⬩ Personalised ⬩ Actionable insights
Flowline ETL migration vs alternatives
| Capability | Manual rewrite | LLM accelerators | Flowline |
|---|---|---|---|
| Timeline | Manual rewrite12–18 months | LLM accelerators6–12 months | FlowlineWeeks |
| ETL conversion | Manual rewriteFull rewrite, line by line | LLM acceleratorsTrial-and-error, pipeline by pipeline | FlowlineComprehensive, holistic: entire legacy system |
| Validation | Manual rewriteManual, error-prone | LLM acceleratorsNeeds additional tooling | FlowlineRow-level, accelerated, assured |
| Sequencing | Manual rewriteManual, error-prone | LLM acceleratorsNot handled | FlowlineAutomatic: avoids breaking changes and bottlenecks |
| Code quality | Manual rewriteVaries, inconsistent | LLM acceleratorsTranslated, inconsistent | FlowlineBest practices baked in via proprietary agent skills |
| Multi-platform | Manual rewritePer-platform, manual | LLM acceleratorsLLM-training dependent | FlowlinePurpose-built for PowerCenter, IDMC, SSIS, Talend |
| Semantics for AI/BI | Manual rewriteYes (manual) | LLM acceleratorsNo | FlowlineProto-ontology + full semantic model |
| Ongoing management | Manual rewriteHumans, indefinitely | LLM acceleratorsNo | FlowlineAutonomous: Flowline agents manage it forever |
AstraZeneca migrating a global data estate with Flowline
Get in touch
We start with a Flowline crawl of your existing estate to map what you have, identify what needs to move, and give you a clear picture of the migration scope.
Request a demo and we will show you what Flowline finds on a legacy estate like yours.
Need more details?
Explore the path from your ETL system to the modern stack.
