The right replacement depends on who'll maintain the pipelines. If your team writes Python and wants to own the code, an open source framework fits. If you'd rather connect sources with a few clicks and let the vendor fix broken connectors, a managed ELT service fits better. If customer event data is the main load, look at a warehouse-native customer data tool.
Where does your data start, and who fixes things when a sync breaks in the middle of the night? Those two questions shape this whole list. Estuary describes itself as one managed platform for change data capture, ETL and streaming pipelines, feeding warehouses, lakes and AI apps, so if you're leaving it, it helps to know which part of that bundle you really lean on.
Below are eight pipeline and ELT tools for teams that move data from apps and databases into a warehouse. They're in no particular order, grouped by the job each one does best, and every description reflects what the vendor says about its own product.
Three checks before any pipeline moves
Pipeline tools look alike on a homepage. They all promise connectors, they all mention warehouses, and most now mention AI. The differences show up in three places, and it's worth testing each of them with your own data before you sign anything.
Which sources are supported, and how well
A long connector list means little if the two sources you care about are thin, read-only or missing the tables you need. Write down the systems you sync today, the tables inside them and any custom fields, then check each one by name. If a vendor lists a source as community-built or in beta, treat it as something you may end up maintaining.
How syncs are scheduled
Streaming and change data capture were a big part of what you had before. Some replacements run on schedules, some stream changes as they happen, and some do both depending on the source. Ask how often each connector can run, what happens when a sync overlaps the next one, and how you'll know when data is late.
Who maintains broken connectors
APIs change without warning. With a managed service, the vendor usually fixes a broken connector and you wait. With open source, you can fix it yourself the same afternoon, but someone on your team has to. Neither is wrong; it's a question of whether you'd rather own the code or own the support ticket.
Grouped by the work each tool does
Open source you run yourself
Airbyte, Meltano and dlt all give you open source code you can host in your own cloud. They're the natural fit for data engineers who want pipelines under version control and don't mind owning upgrades. They differ in style: one is a full platform with a connector catalogue, one is built around pipelines as code, and one is a Python library you drop into your own scripts.
Managed connectors with less set-up
Fivetran, Hevo Data and Portable take the opposite approach: you pick a source, add credentials and the vendor keeps the connector running. Stitch belongs here too, but its homepage now steers new sign-ups towards Qlik Talend Cloud, so new users should check which product they're really buying into.
Customer data, warehouse first
RudderStack is aimed less at copying app tables and more at collecting customer event data, unifying it in the warehouse and sending it on to other tools. If most of your pipeline work is product analytics and customer profiles, this group may matter more than the other two.
Eight pipeline tools side by side
| Tool | Type | Good for |
|---|---|---|
| Airbyte | Open source ELT | Self-hosted or cloud pipelines with a large connector catalogue |
| dlt | Python pipeline library | Writing pipelines as code, then deploying them |
| Fivetran | Managed ELT | Centralising app data without building connectors |
| Hevo Data | Managed ELT platform | Loading and transforming many sources into a warehouse |
| Meltano | Open source data platform | Vendor-neutral pipelines you own and run as code |
| Portable | ELT with expert services | Very wide source coverage plus hands-on help |
| RudderStack | Warehouse-native customer data | Collecting and governing customer event data |
| Stitch | Cloud ELT (Qlik) | Warehouse syncs for existing users |
The full list, A to Z
Alphabetical and in no particular order. Each line covers what the vendor says the tool is for.
Airbyte
Best for: Engineering teams wanting open source ELT they can host in their own private cloud or run on the vendor's service, with a large connector catalogue, per the vendor.
dlt
Best for: Python developers who'd rather write pipelines as code with an open source library, then use the vendor's hosted platform to deploy and monitor them.
Fivetran
Best for: Analytics teams who want data from many apps pulled into one place without building connectors, and who plan to point AI work at it, per the vendor.
Hevo Data
Best for: Teams wanting a managed platform to load, transform and move data from many sources into a warehouse, which the vendor pitches as simpler ingestion.
Meltano
Best for: Data engineers who want vendor-neutral, open source pipelines kept as code in their own cloud and under their control from day one, says the vendor.
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Portable
Best for: Teams needing a very wide range of sources sent to a warehouse, with the vendor offering expert services alongside its ELT tool.
RudderStack
Best for: Customer data teams collecting, unifying and governing data on a warehouse-native platform, then putting it to work, as the vendor describes it.
Stitch
Best for: Existing users syncing data to a warehouse or lake; the vendor's homepage now points newcomers to Qlik Talend Cloud instead, so check that route first.
My advice for data teams leaving a managed platform
Don't migrate everything at once. Pick the two or three pipelines that matter most to the business, rebuild them in the new tool and run them next to the old ones until the numbers match. Most migration pain comes from small differences in how tools handle deleted rows, time zones and schema changes, and you'll only find those by comparing real tables. If integration has been a sore point for a while, my guide to common data integration challenges covers the usual causes.
Think about who'll be on call, too. A small analytics team without an engineer is usually better off with a managed service, even if it means less control. A team with engineers who like owning their stack will get more from open source. And whichever you choose, the end goal is the same: one dashboard you can trust.
A one-week pilot before you switch
- Choose one high-value source and one awkward source, such as a database with frequent changes or an app with custom fields.
- Set up both in each shortlisted tool, using a free plan or trial where one is offered, and point them at a test schema.
- Let the syncs run for several days, including a weekend, so you see how scheduling and retries behave without anyone watching.
- Compare row counts, deleted records and timestamps against your current pipeline, table by table.
- Break something on purpose, such as renaming a column, and note how each tool reports it and how long it takes to recover.
- Keep the tool your team could run without stress, not the one with the longest connector list.
Do you make data pipeline or ELT 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.
Related: python: guidelines and how to pitch.
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 tools prefer because warehouses handle large transformations well.
Do I need change data capture?
Only if you need database changes to show up quickly, or you want deletes and updates tracked reliably. For daily reporting, a scheduled sync is often enough.
Should we choose open source or a managed service?
Choose open source if you have engineers who want control and can maintain connectors. Choose a managed service if you'd rather the vendor keeps connectors working and your team focuses on analysis.
Can I run two pipeline tools at the same time?
Yes, and it's the safest way to migrate. Run old and new pipelines side by side into separate schemas until the results match, then switch your reports over.
Who fixes a connector when a source API changes?
With a managed service, the vendor usually does. With open source, you can fix it yourself or wait for the community, so plan who on your team owns that job.
How do I keep reports working during the move?
Point dashboards at views rather than raw tables. When the new pipeline is ready, you swap what the views read from and the reports carry on as before.
