A managed connector where it pays for itself,
Azure Data Factory where it does not.
We are a Microsoft-first team. Most ingestion we build on Azure Data Factory and Microsoft Fabric pipelines. We use Fivetran on the sources that are genuinely painful to build and maintain by hand — and we are honest about where its consumption pricing stops making sense. One source-by-source call, landing in your warehouse or OneLake.
Managed Ingestion
Connectors → OneLake · one landing
Who this is for
Fivetran consulting built around your role
Managed ELT and ingestion for data, IT, operations and finance leaders — the source each one owns, and the honest call on whether a managed connector or Azure Data Factory should move it.
Head of Data / Analytics
The problem
Half your ingestion is hand-built pipelines that break on every schema change, so the team spends its week keeping extracts alive instead of building anything new.
What we build
A source-by-source read of where a Fivetran managed connector removes real maintenance and where an Azure Data Factory pipeline is the cleaner call, landing governed Delta tables in OneLake.
Engineering time back from pipeline firefighting
IT Head / Integration Lead
The problem
You are weighing a managed connector against building it in-house, but nobody has costed the maintenance burden of the hand-built option against the subscription of the managed one.
What we build
A clear rule for what goes on Fivetran and what stays on Azure Data Factory (ADF), with owned, monitored pipelines and runbooks your team can support after handover.
A defensible connector rule, not a habit
Operations Director
The problem
A source your operations reporting depends on keeps failing silently, and the fix always waits on the one engineer who knows how that brittle pipeline was wired.
What we build
A managed connector on the painful source so schema drift is absorbed automatically, conformed into one governed model with the rest of your ingestion.
Reporting that does not wait on a broken extract
CFO
The problem
A consumption-priced connector got switched on, the active-row count climbed, and the ingestion bill moved in a direction the budget did not plan for.
What we build
A cost read that names where Fivetran pays for itself against ADF and where it does not, with active-row monitoring so the spend stays visible, not a month-end surprise.
Ingestion spend that lands where it pays back
The Problem
Patterns we see before a connector review
Fivetran is a good tool on the right source and an expensive habit on the wrong one. Most estates have both — brittle hand-built pipelines that should be managed, and managed connectors that should never have been switched on.
Hand-built connectors that break on every schema change.
A SaaS source changes a field and the extract fails overnight. The data engineer spends a morning patching a pipeline nobody documented. Multiply that across a dozen brittle sources and most of the team's week goes to keeping ingestion alive, not building anything new.
Maintenance burden nobody costed.
The pipelines that made sense to build in-house two years ago now carry a quiet tax — every API deprecation, every rate-limit change, every re-auth is unplanned work. It never shows up as a line item, so nobody weighs it against a managed connector that would remove it.
Managed connectors bolted on without a cost read.
Someone turned on a Fivetran connector for a high-change source, the active-row count climbed, and the monthly bill moved in a direction the CFO did not expect. Consumption pricing rewards the right sources and punishes the wrong ones — and nobody drew that line before switching it on.
No honest rule for what goes where.
Fivetran or Azure Data Factory is decided by whoever set up the last source, not by a rule. Some painful sources are still hand-built and bleeding maintenance time; some cheap-to-build sources are on a managed connector paying a subscription they do not need.
What we build
What we build
Four pieces of work. Each one keeps the connector choice a cost decision and the platform underneath one governed model.
Source-by-source ingestion recommendation
Replaces
The blanket “put everything on Fivetran” or “build everything ourselves” call that ignores what each source actually costs.
- Every source scored on build effort, maintenance burden and change frequency
- A clear Fivetran-versus-Azure Data Factory (ADF) call per source, with the reason stated
- The painful, high-maintenance sources flagged where a managed connector earns its keep
- The straightforward database and ERP sources kept on ADF where a subscription adds nothing
A defensible rule for what goes where — not a decision made by whoever set up the last source.
Fivetran connectors where the money is worth it
Replaces
A hand-built pipeline against a fragmented SaaS API that fails on every schema change and eats a morning to fix.
- Managed connectors set up on the sources that are genuinely painful to maintain by hand
- Schema-drift handling and automated re-sync so the connector, not your engineer, absorbs the change
- Destination wired into your warehouse or Microsoft Fabric lakehouse, landing in OneLake
- Active-row monitoring so the consumption cost stays visible, not a month-end surprise
The connector removes the maintenance it was chosen to remove — and its cost stays in view.
Azure Data Factory pipelines where they are the better call
Replaces
A managed connector switched on for a high-volume database source, quietly billing active rows it did not need to.
- ADF and Microsoft Fabric pipelines for the sources you already extract cleanly
- Change-data-capture on SAP S/4HANA and Microsoft Dynamics 365, landing governed Delta tables
- No consumption meter on straightforward, high-volume sources that build fine by hand
- Owned, monitored pipelines with runbooks your IT team can support after handover
The cheap-to-build sources stay cheap — the subscription is spent only where it pays back.
One governed landing, whatever moved the data
Replaces
Two ingestion tools landing data in two shapes, so the analytics layer has to reconcile them before it can report.
- Fivetran and ADF outputs conformed to one medallion model in OneLake
- Governance, sensitivity labels and lineage applied the same way regardless of the connector
- Power BI reads one governed Delta model, not two disagreeing copies
- The ingestion tool becomes an implementation detail, not an architecture split
The connector choice stays a cost decision — the platform underneath stays one governed model.
How we work
From cost read to first pipeline in 6 weeks
We start with the maintenance burden your current pipelines carry. That number is what a managed connector actually competes against — so it decides where Fivetran belongs and where Azure Data Factory is the better call.
01
Read — pipelines and maintenance burden
We inventory your current sources and pipelines, and put a real number on the maintenance each one carries — the schema breaks, the re-auths, the babysitting. That burden is what a managed connector is actually competing against.
02
Recommend — source by source
Each source gets a Fivetran-versus-Azure Data Factory call, with the reason stated and the consumption cost estimated where Fivetran is in play. Painful sources lean managed; straightforward high-volume sources stay on ADF.
03
Prove — first pipeline in 6 weeks
We start with the source that hurts most, land it end to end into your lakehouse or warehouse, and settle the trade-off on that source with real numbers before rolling the pattern out to the rest.
Technology stack
Managed ELT
Microsoft Ingestion
Destinations
ERP & SaaS Sources
Governance
BI & Output
Why MyData Insights
Why choose MDI for Fivetran consulting
Microsoft-first, not Fivetran-first
We build most ingestion on Azure Data Factory, Microsoft Fabric and OneLake — the stack your IT team already runs. Fivetran is a tool we reach for on the right source, not a platform we sell you into.
The money decides, not the fashion
We use a managed connector where it saves more engineering time than it costs, and Azure Data Factory where a hand-built pipeline is cheaper to run. Every source gets that call on its own numbers.
Honest about consumption cost
Fivetran is consumption-priced on active rows and can get expensive at volume. We estimate that cost per source up front and flag where a high-volume connector would be the wrong call, before you switch it on.
One governed landing underneath
Whatever moved the data, it conforms to one medallion model in OneLake with governance, sensitivity labels and lineage. Power BI reads one model, not two disagreeing copies.
Pipelines you own
The Azure Data Factory and Fabric pipelines ship with monitoring, alerting and runbooks, so ingestion does not become the orphaned job nobody can support once a contractor leaves.
First working pipeline in six weeks
We prove the ingestion on the source that hurts most and settle the Fivetran-versus-ADF question with real numbers in six weeks, on a fixed scope — not a discovery deck to review.
Common questions
What buyers ask us
Are you a Fivetran partner, or do you build everything on Fivetran?
Neither. We are a Microsoft-first team and build most ingestion on Azure Data Factory and Microsoft Fabric pipelines. We use Fivetran on the sources where a managed connector saves real engineering time — a painful API, a schema that drifts, a system nobody wants to maintain a hand-built pipeline against. We recommend it source by source, not as a default.
When does Fivetran pay for itself over Azure Data Factory?
When the source is genuinely painful to build and keep running by hand — a fragmented SaaS API, frequent schema changes, or a connector your team would otherwise babysit. There, the managed connector removes maintenance that would cost more in engineering time than the subscription. Where the source is a straightforward database or an ERP you already extract cleanly with Azure Data Factory (ADF), the managed connector rarely earns its place.
Is Fivetran expensive at high data volume?
It can be. Fivetran is consumption-priced on active rows, so a high-change, high-volume source can get costly as it scales. That is exactly the case we test in the review — a connector that pays for itself on a low-volume, high-maintenance source can become the wrong call on a high-volume one. We name where the line sits for your sources rather than assume it.
Can Fivetran land data in Microsoft Fabric and OneLake?
Yes. Fivetran can land data into a warehouse or lakehouse destination, and we wire it to sit alongside the ADF and Fabric pipelines already landing data in OneLake. The medallion pattern, governance and Power BI model stay the same regardless of which tool moved the data — the connector is an ingestion choice, not an architecture change.
How quickly can you get a first pipeline running?
First working output in 6 weeks is our standard cadence. We start with the source that hurts most, prove the ingestion end to end into your lakehouse or warehouse, and settle the Fivetran-versus-Azure Data Factory question on that source with real numbers before rolling on to the rest.
Engagement
Free Ingestion Cost Review
Thirty minutes with Amit on your actual ingestion estate: the pipelines that break, the maintenance they carry, and where a Fivetran managed connector would pay for itself against an Azure Data Factory build. No slides, no obligation.
What you get
- A read of your current pipelines and the maintenance burden they carry
- Where a Fivetran managed connector pays for itself against Azure Data Factory
- A source-by-source recommendation, with the consumption cost flagged where it matters
- A 6-week plan to prove the first pipeline end to end into your lakehouse or warehouse
Ready to move
Book a 30-minute ingestion cost review
30 minutes with Amit. No slides. No pitch deck. No obligation to proceed. We walk through your sources and settle where Fivetran pays for itself and where Azure Data Factory is the better call.