Pick a replacement by how you want to run it: a hosted no-code service, an open source tool you operate yourself, or a service from your cloud provider. Then check your own data sources by name on each connector page, because coverage of the sources you use matters far more than any headline connector total.
Here's the field in one grid before anything else, because the first question with a pipeline tool is what kind of thing you're buying and who keeps it running.
Ten pipeline tools and how each one runs
| Tool | How it runs | Good for |
|---|---|---|
| Airbyte | Open source ELT | Engineers who want control of connectors |
| Apache NiFi | Data flow automation | Flows that need data provenance tracking |
| AWS Glue | Serverless data integration | Teams already on AWS |
| Dataddo | Managed data movement | Teams with data in many places |
| dlt (dltHub) | Pipeline library | Python teams who want pipelines as code |
| IBM StreamSets | Streaming data pipelines | Streaming rather than batch loads |
| Integrate.io | Low-code ETL and ELT | Teams wanting a dedicated solution engineer |
| Portable | ELT with long-tail connectors | Long-tail sources |
| Rivery | Managed cloud ELT | Orchestration and CDC replication |
| Skyvia | No-code data platform | SQL access alongside integration |
Ignore the connector headline and check your own sources
Hevo leads its homepage with a connector count. So do Airbyte and Portable, and the totals differ a lot from one vendor to the next. It's tempting to treat the biggest total as the safest choice, but a long connector list doesn't help if one of your sources is missing or only half supported.
The useful test is narrower. List the five or six sources your business relies on, such as your CRM, ad platforms, payment system and product database, then look each one up by name on every vendor's connector page. For each, note whether it's built and maintained by the vendor or by the community, which objects or tables it syncs, and whether it supports incremental loads. Two tools that look alike on the homepage can split clearly on that list.
Hosted, self-run or your cloud provider
Hosted no-code ELT. Closest in feel to Hevo. Skyvia, per its site, is a cloud data platform for no-code integration. Integrate.io says it covers ETL, ELT, CDC and reverse ETL. Rivery calls itself a fully managed cloud ELT tool. Dataddo says it moves data between cloud, on-premises and hybrid sources and warehouses.
Open source you run yourself. dlt describes itself as an open source Python library for building pipelines, with a dltHub platform to deploy and monitor them. Apache NiFi says it's a system to process and distribute data. Running these means someone on your team owns upgrades, monitoring and failures.
Cloud and enterprise services. AWS Glue is a cloud-provider service for ETL, per its site. IBM StreamSets, according to IBM, is a graphical pipeline tool for hybrid and multicloud setups. These make most sense when your data already lives with that provider or your company has a standing agreement with it.
Make an ELT or pipeline tool that belongs in one of these groups? Request a place in the next review of this page.
Every option, A to Z
The alphabet sets the order, so read them in no particular order of preference; what follows each name comes from the vendor.
Airbyte
Best for: Open source engine and connectors that the vendor says are free to run in your own VPC, or on its hosted cloud.
Apache NiFi
Best for: A browser-based interface, from what the project calls an easy to use, powerful and reliable system.
AWS Glue
Best for: Discovering, preparing and combining data for ETL, which the vendor says its service simplifies.
Dataddo
Best for: Moving data from ERPs, databases and SaaS apps into warehouses and AI systems securely, as the vendor puts it.
dlt (dltHub)
Best for: An ingestion foundation that the vendor says is Apache 2.0 licensed and always free to use.
IBM StreamSets
Best for: Real-time decision-making, which the vendor says its streaming pipelines support.
Integrate.io
Best for: Letting anyone on the team build pipelines with low-code or AI agents, which is the vendor's main promise.
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Portable
Best for: Hard-to-find sources, with the vendor saying its team researches, develops and troubleshoots integrations on your behalf.
Rivery
Best for: Building end-to-end ELT pipelines quickly, with the vendor offering no-code or custom code.
Skyvia
Best for: Non-coders after integration and cloud backup in one place, which the vendor calls all-in-one.
Advice for a data team with few engineers
If nobody on your team wants to be on call for pipelines, stay with a hosted tool and judge it on connector depth for your own sources, alerting when a sync fails, and how it handles schema changes. Open source is worth it when you have engineers who'll own the setup, or when data can't leave your own cloud. Cloud provider services are a good fit when the rest of your stack already sits with that provider.
Whichever route you take, move one pipeline first, ideally a source that changes often, and run it next to the old one until the row counts and timings match. My guide to data integration challenges covers the problems that tend to surface during a move, and if the next job is reporting on what lands in the warehouse, my Power BI alternatives list covers the dashboard side.
A source checklist to fill in before any demo
- Source name and owner. Who in the business relies on this data, and what breaks if it's late?
- Connector status. Built by the vendor, by the community, or custom? Is it listed as generally available?
- Objects and history. Which tables or objects sync, and how far back can the first load go?
- Sync method. Full refresh, incremental or change data capture, and how often can it run?
- Schema changes. What happens when a field is added or renamed at the source?
- Destination. Does it load into your warehouse in a shape your analysts can use without rework?
Fill in a row per source for each shortlisted vendor. The tool that answers well for your own sources is the one to trial, whatever its homepage says.
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Frequently asked questions
What's the difference between ETL and ELT?
ETL transforms data before loading it into the warehouse. ELT loads raw data first and transforms it inside the warehouse.
Is open source ELT free to run?
The software can be free, but someone has to host, monitor and upgrade it. Count the engineering time and the servers before deciding it's the lighter option.
What's change data capture?
It's a way of syncing only the rows that changed in a database, read from its change log, so pipelines can run often without reloading whole tables.
Can I run two pipeline tools at the same time?
Yes. You could use a hosted tool for common SaaS sources and an open source library for one or two custom sources.
How long does it take to move pipelines across?
It depends on the number of sources and transformations. Moving one source at a time and comparing results with the old tool keeps the risk low.
Do I need reverse ETL as well?
Only if you want to push warehouse data back into tools like your CRM. Some pipeline tools include it, others leave it to a separate product.
