TL;DR Short answer: n8n has no pre-built connector in any major ETL tool, so extracting it means working against its REST API. The deciding factor is whether you can build and maintain that connector yourself. The more urgent constraint is that n8n prunes execution history automatically , so anything you do not extract in time is gone permanently. Thirteen tools worth considering:
Airbyte Fivetran Stitch Matillion Airflow Talend Pentaho Informatica PowerCenter SSIS Singer Rivery Hevo Data Meltano Each of these extracts data from n8n and other sources, then loads it into a database, warehouse or lake. Airbyte is the one that lets you build the n8n connector yourself and run the whole thing on your own infrastructure.
What is n8n, and why extract from it? n8n is a workflow automation platform that connects applications and services so an event in one triggers an action in another. It is commonly described as open source, but n8n's own documentation is explicit that it is not: the code is released under the Sustainable Use License, a fair-code licence that permits free use, modification and distribution but restricts commercial resale, and files marked .ee are enterprise-licensed separately. In practice you can self-host it freely, which is what most teams mean by the term, but it is source-available rather than OSI open source. For ETL purposes, what matters is that every workflow run produces an execution record, and those records are where the analytical value sits: run counts, durations, failure rates, retry chains and the node-level payloads of what actually moved.
There are three reasons teams pull data out of n8n, and one of them is time-critical:
Before anything else, know that n8n deletes its own history. Execution records are pruned automatically and the API cannot recover them once they are gone. Self-hosted instances prune after 14 days by default, controlled by EXECUTIONS_DATA_MAX_AGE, and n8n Cloud retention depends on your plan, with Starter keeping roughly 7 days and Pro around 30, both with a cap on total runs stored. Whatever you have not extracted by then is lost permanently. That makes this less an analytics project than a retention one.
Business intelligence: execution records show which workflows run most, which fail most and how long each takes. Joined with data from the systems those workflows touch, that turns automation from a black box into something you can measure and cost.Data Consolidation: an n8n execution log is only half a story. Joined with the CRM, billing or ticketing systems those workflows write to, it shows what the automation actually changed rather than just that it ran.Compliance: execution logs record what your automations did to which systems. If those workflows touch regulated data, the retention window n8n gives you by default is unlikely to satisfy an auditor, so the records need to live somewhere durable.The common thread is that n8n knows things no other system does: which automations ran, how long they took, what they touched and where they failed. That only becomes analysable once it is out of n8n and next to the rest of your data.
Tool
Type
Connectors
Can you build the n8n connector yourself?
Ideal for
Airbyte Open-source ELT 700+ Yes, no-code Connector Builder Reaching APIs with no pre-built connector Fivetran Managed ELT 700+ Only via custom Function connectors Hands-off pipelines for supported sources Stitch Cloud extract and load 140+ Only by writing a Singer tap Simple loads from supported sources Matillion ELT with pushdown transformation 100+ Via custom API components Warehouse-side transformation Airflow Orchestrator, not ETL Not applicable Yes, you write it in Python Scheduling extraction you have written Talend Enterprise integration and data quality Hundreds Yes, custom components Governed enterprise estates Pentaho ETL and BI, open core 100+ Via REST client steps Batch ETL with reporting attached Informatica PowerCenter Enterprise ETL 200+ Via HTTP transformations Regulated environments, but check support dates Microsoft SSIS ETL, Microsoft stack Microsoft ecosystem Via custom script components SQL Server shops Singer Open tap and target spec Community taps, many stale Yes, you write the tap Developers building custom pipelines Rivery Cloud ELT 200+ Via REST action rivers Teams wanting orchestration bundled in Hevo Data Cloud ELT 150+ No, connectors are vendor-built No-code pipelines from supported sources Meltano Open-source DataOps, CLI-first Singer taps via Meltano Hub Yes, using the Singer SDK Engineers managing pipelines in Git
Which ETL tools work with n8n? Thirteen tools, ordered by how commonly they come up. The column that matters most for n8n is the last one: whether you can build the connector yourself.
1. Airbyte Airbyte is the leading open-source ELT platform, created in July 2020. It offers 700+ connectors and a community of more than 25,000 members. It integrates with dbt for transformation and with Airflow, Prefect and Dagster for orchestration, and offers a UI, an API and a Terraform provider. For n8n specifically, the relevant capability is the Connector Builder, since no pre-built n8n connector exists.
What's unique about Airbyte? Their ambition is to commoditize data integration by addressing the long tail of connectors through their growing contributor community. All Airbyte connectors are open-source which makes them very easy to edit. Airbyte also provides a Connector Development Kit to build new connectors from scratch in less than 30 minutes, and a no-code connector builder UI that lets you build one in less than 10 minutes without help from any technical person or any local development environment required..
Airbyte also provides stream-level control and visibility. If a sync fails because of a stream, you can relaunch that stream only. This gives you great visibility and control over your data.
You can self-host Airbyte Core, or use Airbyte Cloud, where the Standard and Plus plans are billed on volume. The Pro and Enterprise Flex plans are capacity-based and add SSO, role-based access control and column hashing, with Enterprise Flex keeping the data plane in your own VPC. Airbyte offers a 99% SLA on generally available connectors and a 99.9% SLA on the platform.
2. Fivetran Fivetran is a closed-source, managed ELT service created in 2012, with 700+ connectors. Connectors are vendor-built and vendor-maintained, and schema changes are applied automatically.
Fivetran offers some ability to edit current connectors and create new ones with Fivetran Functions, but doesn't offer as much flexibility as an open-source tool would.
What's unique about Fivetran? As the first ELT solution in the market, Fivetran is a proven and reliable choice. It bills on monthly active rows, meaning rows added or changed in a month, which is predictable for stable sources and harder to forecast for high-churn ones.
Here are more details on the differences between Airbyte and Fivetran
3. Stitch Data Stitch is a cloud extract-and-load platform with 140+ connectors, originally built on the open-source Singer specification. It has no user-defined transformations.
Qlik acquired Talend, and Stitch with it, in 2023, and now publishes a migration path from Stitch to Qlik Talend Cloud. Stitch's 140+ connectors were originally built on the open-source Singer specification, but most community Singer taps are now largely unmaintained, so check that the integration you need is actively supported before committing.
What's unique about Stitch? Since Qlik acquired Talend, and Stitch with it, in 2023, Stitch has become one product line inside a much larger portfolio, and Qlik now publishes a formal migration path from Stitch to Qlik Talend Cloud. It still works for existing users, but that direction of travel is worth weighing before building something new on it.
Here are more insights on the differentiations between Airbyte and Stitch .
4. Matillion Matillion is an ELT platform created in 2011, built around pushdown transformation that runs inside your cloud warehouse. It supports 100+ connectors and covers extract, load and transform.
What's unique about Matillion? Running in your own cloud account means n8n data stays inside your infrastructure, though a multi-cloud setup may need more than one instance. Transformation is pushed down to the warehouse, so throughput depends on compute you already pay for. Matillion also integrates with dbt, which has shipped with the product since version 1.70.
Here are more insights on the differentiations between Airbyte and Matillion .
5. Airflow Apache Airflow is an open-source workflow management tool. Airflow is not an ETL solution but you can use Airflow operators for data integration jobs. Airflow started in 2014 at Airbnb as a solution to manage the company's workflows. Airflow allows you to author, schedule and monitor workflows as DAG (directed acyclic graphs) written in Python.
What's unique about Airflow? Airflow requires you to build data pipelines on top of its orchestration tool. You can leverage Airbyte for the data pipelines and orchestrate them with Airflow, significantly lowering the burden on your data engineering team.
Here are more insights on the differentiations between Airbyte and Airflow .
6. Talend Talend is a data integration platform that offers a comprehensive solution for data integration, data management, data quality, and data governance.
What’s unique with Talend? Talend pairs integration with data quality and governance, which matters when data has to be defensible as well as delivered. Worth knowing before you shortlist it: Talend Open Studio, the free open-source edition, was retired on 31 January 2024, and Qlik acquired Talend in 2023, so it is now sold as Qlik Talend Cloud with no self-serve tier.
7. Pentaho Pentaho is an ETL and business analytics platform covering data integration, mining and business intelligence. It offers ETL rather than ELT.
What is unique about Pentaho? Pentaho has an open-core heritage, which makes it scriptable and customisable, and it bundles reporting and analytics alongside the ETL engine. Most current development goes into the enterprise edition under Hitachi Vantara.
However, Pentaho is also an Enterprise product, so hard to implement without any self-serve option.
8. Informatica PowerCenter Informatica PowerCenter is a mature enterprise ETL tool covering data profiling, cleansing and transformation, deployed in the customer's own infrastructure. Two things to check before shortlisting it: Salesforce completed its acquisition of Informatica in November 2025, and PowerCenter 10.5 left standard support in March 2026, so new investment is going into the cloud platform instead.
9. Microsoft SQL Server Integration Services (SSIS) SQL Server Integration Services is Microsoft's ETL engine, designed around SQL Server. It offers ETL rather than ELT, is Windows-bound, and reaching a REST API like n8n's means custom script components rather than a native source.
10. Singer Singer is worth knowing as the first open JSON-based tap-and-target specification, introduced in 2017 by Stitch, which Talend acquired in 2018. Investment in the community has since stopped and many taps are outdated, so most teams now reach Singer through Meltano rather than directly. For n8n you would be writing the tap yourself either way.
11. Rivery Rivery is a cloud ELT platform founded in 2018, with built-in transformation, orchestration and activation. It offers 200+ connectors. Pricing is usage-based in Rivery Pricing Units, which vary by the connectors you sync from and can be hard to estimate in advance.
12. HevoData Hevo Data is a cloud ELT platform founded in 2017 with 150+ integrations. It supports transformation before load, using Python or a drag-and-drop editor, and can sync data back out to APIs. It is cloud-only, and connectors are vendor-built, so an unsupported source like n8n is not something you can add yourself.
13. Meltano Meltano is an open-source, CLI-first DataOps platform spun out of GitLab and built on Singer taps and targets. It focuses on managing pipelines as a Git project. Connectors come from the Singer community rather than being vendor-maintained, and there is no support package with an SLA. For n8n you would write a tap using its SDK, which takes more engineering effort than Airbyte's no-code builder.
None of these tools is n8n-specific, and that is the point: you are unlikely to build a data stack around n8n alone. Pick the tool that covers the rest of your sources and can be extended to reach n8n's API.
How should you choose an n8n ETL tool? Most evaluation criteria for ETL tools are generic. These are the ones that actually decide whether a tool can handle n8n:
Can you build the connector yourself? This is the first filter and it eliminates most of the list. No major ETL vendor ships a pre-built n8n connector, so a tool with no connector-building capability simply cannot extract n8n at all. Airbyte's Connector Builder, Meltano's SDK and writing Python for Airflow are the realistic routes.Cursor pagination: n8n paginates with an opaque nextCursor token rather than numeric offsets, so you cannot jump to an arbitrary page. A tool that only supports offset or page-number pagination will not work against this API without custom code.Sync frequency against the pruning window: your sync has to run comfortably more often than n8n deletes history. On a self-hosted default of 14 days that is easy; on a Cloud Starter plan keeping roughly 7 days it leaves less room, and a weekly sync would lose data.Incremental sync: executions accumulate fast on a busy instance. You want incremental loading keyed on startedAt rather than a full refresh every run, both for speed and to stay inside rate limits.API access on your plan: n8n's public API is not available on the Cloud free trial. If you are evaluating on a trial instance, no ETL tool will connect until you upgrade, which catches people out during proofs of concept.Nested JSON handling: execution payloads are deeply nested objects containing every node's input and output. Check whether the tool flattens these into queryable columns, lands them as a JSON blob, or chokes on them.Secrets hygiene: the API exposes a Credentials resource. Whatever tool you choose, make sure stream selection is explicit rather than all-by-default, so you do not quietly replicate authentication details for every connected system into your warehouse.Rate limit tolerance: self-hosted Community instances impose no request-rate limit, but n8n Cloud throttles bursts. If you are on Cloud, the tool needs to back off and retry rather than fail the sync.n8n's public REST API exposes several resources, and the useful ones for analytics are narrower than the full list. Workflows return names, tags, active state, project and the node graph itself, which tells you what each automation does and what it connects to. Executions are the substantive dataset: each record carries an id, workflowId, mode, status, startedAt, stoppedAt, finished flag, retryOf and retrySuccessId for retried runs, and optionally the full input and output payload of every node. Tags, Projects, Users and Variables are small reference tables that give the execution data context. Audit returns a security report rather than a stream, so it suits periodic snapshots rather than continuous sync. One resource to deliberately leave alone: the Credentials endpoint. It exists, but it holds authentication details for every system n8n touches, and copying that into a warehouse turns your analytics store into a secrets store. Extract the credential names if you need an inventory, never the payloads.
How do you start pulling data from n8n? If you decide to test Airbyte, you can start analyzing your n8n data within minutes in three easy steps:
Step 1: Set up n8n as a source connector n8n has no pre-built Airbyte connector, so you build one against its REST API using the no-code Connector Builder. Point it at https://your-instance/api/v1, set authentication to an API key passed in the X-N8N-API-KEY header, and generate a key under Settings then API in your n8n instance. Add /executions and /workflows as streams. Both use cursor pagination: the response returns a nextCursor value that you pass back as the cursor parameter, so configure cursor pagination rather than offset. Page size defaults to 100 and caps at 250. Set includeData to true only if you need the full execution payload, and be aware that oversized executions are returned without their data unless you explicitly request otherwise.
Step 2: Choose a destination for your n8n data Choose where the n8n data should land. This can be a cloud data warehouse, a data lake, a database, cloud storage, or any other supported Airbyte destination.
Step 3: Configure the n8n data pipeline in Airbyte With the source and destination configured, set up the connection: pick the streams and fields you want, choose a sync frequency, and decide where in the destination the data lands. For executions, an incremental sync keyed on startedAt is usually right, and the sync needs to run more often than your pruning window so nothing is lost between runs.
That is the whole process, and it works the same way whether you run Airbyte Open Source, which you can deploy within 5 minutes , or Airbyte Cloud which you can try free for 14 days .
Which n8n ETL tool should you choose? For n8n specifically the shortlist is shorter than it looks. Because there is no pre-built n8n connector anywhere, most managed tools on this list cannot extract it at all without custom work you cannot do yourself. That narrows the practical choice to tools where you can build the connector: Airbyte through its Connector Builder, Meltano by writing a Singer tap, or Airflow by writing the extraction in Python and scheduling it. Of those, Airbyte is the least code, and it handles pagination, incremental state and schema changes for you once the connector is defined. Whichever you pick, set it up before you need the data rather than after: pruned executions cannot be recovered.
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