Migrate off Fivetran, Airbyte, Python scripts 90% faster
Move your data stack forward without the vendor tax
Data teams are now expected to ship faster with fewer people, and to catch problems before stakeholders do. Legacy ingestion tools hold them back from the code-first, AI-native workflow that makes that possible. But rebuilding years of pipelines in-house is six figures of engineering time, so most teams renew instead, even with a tool they have outgrown. We remove that constraint. Our engineers rebuild your Fivetran, Airbyte, and hand-rolled Python pipelines as dltHub code, validate each one against your live output before cutover, and hand them over running. Your team works alongside us and keeps the AI-forward workflow.
One month, 60+ pipelines, code you own afterwards.
- 60+
- Pipelines migrated in one month, including parity validation and production cutover.
- 90%
- Faster than a manual rebuild with our AI tooling
- 100%
- Control: dltHub code in your repository, your warehouse. No hidden fees.
THE PROBLEM
The reason you haven't left yet
Most teams we talk to decided to move off their ingestion vendor about a year before they actually did. These are the four things that usually sit in the way.
Renewal sets your roadmap, not you
Volume-based pricing means the invoice grows with the business while the product stays the same. And the contract date, rather than your priorities, decides when ingestion gets attention.
Every change queues behind the same two engineers
Ingestion lives in a UI with no git history, no local run and no tests, so nobody outside the core team can safely touch it, and none of it can be handed to an agent. Analysts wait, and the queue grows.
The connector you need is paywalled, deprecated, or missing
Internal APIs and long-tail sources turn into roadmap requests to a vendor. When a source breaks or an API changes, you file a ticket and wait.
And the migration itself looks like a quarter of work
Which is exactly why you renewed last time. This is the one we solve: our engineers do the rebuild, validate it against your live output, and hand it over running.
PROOF
A 20-person team replaced a hiring plan with a migration
“We weren't looking for a custom rebuild, and we didn't want to staff up a data team to run something we couldn't sustain at our size. dltHub's toolkit took what we already had, structured the parts that needed cleaning up, and gave us back a stack we can confidently run.”
Martin Miodownik · CTO & Co-Founder, Navit
Read the Navit case study- 85% to 99%+
- pipeline SLA improvement after applying the ontology toolkit
- days to hours
- time-to-new-metric, driven by a cleaner semantic model
- dltHub Forward Deployed Engineers ran the migration off a first-generation dlt and Airflow stack
- The alternative quote was three hires: a senior data engineer, a data modeler, and an analytics engineer
- A generalist maintains the stack today, with the semantic model versioned instead of living in one contractor's head
From kickoff to cutover in weeks, not quarters
This is the shape of a typical migration: a mid-sized team with a few dozen pipelines on Fivetran or Airbyte. Each phase below shows what we do, what your team does, and what you get at the end of it.
Week 0
Scope the estate
We match what you actually run against what you pay for. Most teams find pipelines nobody owns and connectors still billing after the use case died.
dltHub does
- Full pipeline and schema inventory from your current tools
- A cost baseline built from your current vendor bill
- A complexity rating for every pipeline, and we flag the risky ones
Your team does
- A two-hour discovery session with one engineer
- Read access to your current tool
Closes with a fixed-scope SoW with a named pipeline list and a price
Week 1
Get the environment and your team ready
This week is about preparation. dltHub workspace, credentials, network, warehouse permissions, CI, all of it in place before the first pipeline gets rebuilt. Your engineers get trained on the dltHub agentic workflows in parallel.
dltHub does
- Set up your dltHub workspace, environments and secret handling
- Wire up the destination and CI, plus the agent tooling your team will use
- Train your engineers on dltHub and the agentic workflows
- Rebuild one pipeline end to end, as the pattern for everything that follows
Your team does
- Provision accesses and credentials
- Sign off on naming and environment conventions
- Free up your data engineers for a training session
Closes with one production-shaped pipeline running in your environment, and a team that can read, review and ship dltHub code
Week 2-3
Active rebuild phase
Our engineers work through the inventory with our internal migration tooling and validate the output. For every pipeline you get more than code running in production: a comparison against your existing data and schema, and a note on anything worth improving that we found on the way.
dltHub does
- Rebuild each pipeline as dltHub code, agent-run and human-reviewed
- Preserve destination table and column names, so your dbt models and dashboards resolve unchanged
- Schema and row-count parity checks, incremental and CDC behaviour reproduced, not approximated
Your team does
- Review the parity reports
- Sign off on the pipelines your business reports depend on
Closes with every in-scope pipeline running in parallel with the old one
Cutover
A non-event by design
By this point both stacks have been running side by side and the numbers match. The destination tables are the same shape they always were, so your dbt models and BI keep pointing at them. Nothing downstream needs reworking.
dltHub does
- Parity sign-off on every pipeline before anything is switched
- A dbt and BI smoke run against the new tables
- The cutover itself: schedules and alerting moved onto dltHub, with your old stack kept warm as a rollback path
Your team does
- Confirm the reports your stakeholders actually read still reconcile
- Give notice to your vendor
Closes with production ingestion on dltHub, downstream models unchanged, your old stack switched off
Handover
Your team owns it
The migration only counts if your team can change a pipeline the week after we leave. This phase is where we test that.
dltHub does
- Working sessions on the agentic workflow your team will use day to day
- A runbook, an on-call handbook, and the agent skills configured for your stack
- A shadow session while one of your engineers ships a new source, answering questions as they go
Your team does
- Name a maintainer
- Ship one new source yourself during the handover
Closes with your engineer shipping a new source without us in the room
Two to four weeks is the shape of a typical mid-market estate with ~60 pipelines. A larger or heavily customised estate runs longer, and we scope that in week 0 rather than discovering it mid-project.
The migration, per vendor
Each blueprint is the move off one specific stack: what the agent skills read, what gets rebuilt, and what parity looks like when it is done. Start from the one that matches what you run today.
One joint SoW. Then you pick who runs it.
Every engagement starts from the same scoping step. From there, three delivery models, chosen on how much of the work your team wants to own.
Enablement-led
Your team runs the migration. We supply the training, certification, and the agent skills, and we review the work as it lands. Best for teams with engineering capacity who want to own it from the start.
dltHub-led
Our Forward Deployed Engineers run the migration alongside your team and teach them to run it afterwards. Best for teams that want speed now and ownership later. This is what Navit did.
Partner-led
A certified consulting partner delivers, backed by us. Best for outsourced delivery or estates larger than a single squad can absorb.
AFTER YOU LAND
Support that keeps it running
Your team owns the pipelines after handover, but you don't have to own them alone. Each tier below covers the maintenance side, with an SLA on response and resolution for your critical pipelines.
AI assistance, included
Round-the-clock in-product answers from dhelp, on every Scale subscription. The default, self-serve experience.
Standard SLA
Slack Connect or email, with P1 in 12 hours, P2 in one day, P3 in three. 24 support hours a year and private previews of new features.
Premium SLA
A dedicated solutions engineer, P1 in four hours, on-demand expert consultation, quarterly roadmap input, and two data-strategy sessions a year.
Scope your migration
Tell us what you run today. You get back a fixed-scope SoW with a named pipeline list and a price, usually within two business days.
- A named pipeline list, not a day-rate estimate
- A cost baseline against what your incumbent bills you today
- The risky pipelines flagged before you commit, not in week 4
- No obligation: the scope is yours whether or not you go ahead
“dltHub's toolkit took what we already had, structured the parts that needed cleaning up, and gave us back a stack we can confidently run.”
Bring your pipeline list. We will scope the move.
Tell us the vendor you are on and roughly how many pipelines you run. You get back a fixed-scope statement of work with a named pipeline list and a price, not an hourly estimate.