Pick a replacement by who'll keep the pipelines running. A managed service handles connectors and schedules for you, an open source tool gives your engineers full control, and a no-code platform suits analysts. Check that your most important sources are supported before anything else, because connector coverage decides most switches.
Stitch's homepage now greets people who want to start syncing data to a warehouse, data lake or lakehouse with a suggestion to try Qlik Talend Cloud instead. If you're weighing up your options anyway, here are eight data pipeline and ELT tools, in no particular order, for teams that move data from apps into a warehouse.
Why the move question is bigger than it looks
When a vendor steers newcomers towards a different product, it's a fair signal to plan your next step rather than wait. That doesn't mean your current syncs will stop, and nothing here says they will. It does mean it's sensible to know what you'd move to and how long the move would take.
The real work in any switch sits in three places: which sources and destinations are supported, how syncs are scheduled, and who fixes a connector when an app changes its API. That last one matters most. A managed service takes that job off your hands, while an open source tool hands it to your engineers. Tools in this field split by who they're built for and how much you set up yourself, so run one real pipeline through each before you decide.
Eight pipeline tools, alphabetically
Listed A to Z. Each line is the vendor's own description, put in plain words.
Airbyte
Best for: Engineering teams wanting open source ELT they can run in their own cloud or on the vendor's hosted service; the vendor also pitches it as data for AI agents.
dlt
Best for: Python developers who'd rather write pipelines in code; the vendor's open source library builds them and its dltHub platform deploys and monitors them. A free plan is named.
Fivetran
Best for: Analytics teams that want data centralised automatically with little upkeep; the vendor names a free start and now pitches connecting AI to your data too.
Hevo Data
Best for: Small data teams wanting automated loading and transformation from many sources into a warehouse; the vendor's pitch is less complex ingestion, and a free plan is named.
Keboola
Best for: Finance-led organisations that want a governed data foundation in place before AI work begins, which is how the vendor positions it, with CFOs as the audience.
Meltano
Best for: Engineers wanting a vendor-neutral, open source platform where pipelines live in code they control and run in their own cloud, according to the vendor.
RudderStack
Best for: Teams whose main data is customer behaviour; the vendor describes a warehouse-native platform to collect, unify and govern it, with a free plan named.
Skyvia
Best for: Non-engineers who want no-code integration plus cloud backup, SQL management and OData access in one cloud service, as the vendor describes it; a free plan is named.
How they line up
| Tool | Kind of tool | Good for |
|---|---|---|
| Airbyte | Open source ELT | Open source pipelines run in your cloud or theirs |
| dlt | Python pipeline library | Python developers writing pipelines as code |
| Fivetran | Managed data movement | Hands-off syncing into a central warehouse |
| Hevo Data | Managed ETL platform | Loading app data into a warehouse without code |
| Keboola | Governed data platform | Finance teams preparing governed data before AI projects |
| Meltano | Open source data platform | Engineers who want pipelines as code in their own cloud |
| RudderStack | Customer data platform | Collecting and governing customer event data in the warehouse |
| Skyvia | No-code cloud data platform | No-code integration, backup and SQL access together |
Managed, open source or something in between
Fivetran and Hevo Data are managed services: you pick sources, they run the syncs and keep connectors working. Skyvia sits close to them, with no-code integration plus backup and SQL access for people who aren't engineers.
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Airbyte, Meltano and dlt start from open source code. That gives you control over where pipelines run and what they look like, and it puts upkeep on your team. They suit companies with engineers who'd rather own the stack than rent it.
RudderStack and Keboola take wider views. One centres on customer event data kept in your warehouse, the other on a governed data foundation for finance teams. Pick either when that wider job is the one you need, not ELT alone.
Advice for data teams leaving Stitch
Start by listing every source you sync today and how often each one runs. That list is your real shopping list. A tool with a huge connector library is no help if it misses the two apps your reports depend on, so check those two first.
Don't forget transformations. If you clean and model data with SQL inside the warehouse today, check that the new tool leaves that layer alone, or that its own transformation step fits how your analysts work. Moving connectors is the easy part; rebuilding reports that broke because a column name changed is what eats the weeks.
If nobody on your team wants to maintain pipelines, stay with a managed service. If you've got engineers and want control, open source is a strong route, but plan for the hours they'll spend on upkeep. For a wider view, see my guide to ETL tools and my list of data warehouse tools.
A two-week pipeline trial
- Pick your two most important sources and the warehouse they load into.
- Set up the same sync in two shortlisted tools, using a free plan or trial where the vendor names one.
- Run both on your normal schedule for two weeks and compare row counts against the source each morning.
- Break something on purpose, such as renaming a field, and note how each tool reports and recovers.
- Keep the one your team trusts to run without babysitting, and retire the other.
Do you make data pipeline software? Tool makers can ask to be considered for a featured place on this list.
Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.
Frequently asked questions
What's the difference between ETL and ELT?
ETL transforms data before it reaches the warehouse. ELT loads raw data first and transforms it inside the warehouse, which most modern pipeline tools favour because warehouses handle the heavy lifting well.
Is open source ELT free to run?
The software can be free to use, but you'll still run servers and spend engineering time keeping connectors working. Weigh that time against a managed service before you decide.
How long does a pipeline migration take?
For a handful of sources, often a few days of setup and a couple of weeks running both side by side. Large setups with custom transformations take longer, so move them in batches.
Do I need a data warehouse to use these tools?
Most of them load into one, so in practice, yes. Some also load into data lakes, lakehouses or databases. Check that your destination is supported before anything else.
Who should own data pipelines in a small company?
Whoever owns reporting. If that's an analyst rather than an engineer, lean towards a managed, no-code tool so they aren't stuck fixing connectors.
