TL;DR Short answer: to export data from Airtable at any scale you need a tool that handles its REST API pagination and rate limits for you. Thirteen options, and the first question is whether you want a managed service or something you can self-host:
Airbyte Fivetran Stitch Skyvia Airflow Estuary Flow Dataddo Portable.io Whalesync Singer Rivery Hevo Data Meltano These ETL and ELT tools help in extracting data from Airtable and other sources (APIs, databases, and more), transforming it efficiently, and loading it into a database, data warehouse or data lake, enhancing data management capabilities. Airbyte distinguishes itself by offering both a self-hosted open-source platform and a Cloud one.
What is Airtable, and why is exporting data from it hard? Airtable is a cloud platform that combines a spreadsheet interface with database structure, which is why teams adopt it fast and then struggle to get data back out at scale. Its REST API is the route out, but it paginates at 100 records per request and rate-limits to 5 requests per second per base, so a large base takes a while to read and a naive script will hit the limit. That is the practical problem an ETL tool solves here.
For simplicity, this article uses Airtable ETL as shorthand for all data integration tools that move data out of Airtable, ETL and ELT alike.
Why Move Data Out of Airtable? Business intelligence: Airtable data may need to be loaded into a data warehouse for analysis, reporting, and business intelligence purposes.Data Consolidation: Companies may need to consolidate data with other systems or applications to gain a more comprehensive view of their business operationsCompliance: Certain industries may have specific data retention or compliance requirements, which may necessitate extracting data for archiving purposes.Tool Type Airtable Support Two-Way Sync Deployment Pricing Model Airbyte Open-source ELT Native connector No Cloud, hybrid (Flex), self-managed Free OSS; capacity-based paid tiers Fivetran Managed ELT Native connector No Cloud Monthly active rows Stitch Data Managed ELT Via Singer tap No Cloud Row volume tiers Skyvia No-code cloud platform Native connector Yes Cloud Free tier, then per record Apache Airflow Orchestration Build it yourself No Self-managed Free, infrastructure cost only Estuary Flow Real-time streaming Native connector No Cloud, private, BYOC Usage-based Dataddo No-code ELT Native connector No Cloud Free tier, from $99/month Portable.io Long-tail ELT Native connector No Cloud Flat rate Whalesync Two-way sync Airtable-native Yes Cloud Per record synced Singer Open-source framework Community tap No Self-managed Free, open source Rivery Managed ELT Native connector Via reverse ETL Cloud Usage-based credits Hevo Data Managed ELT Native connector Via activation Cloud Event-based tiers Meltano Open-source ELT Via Singer tap No Self-managed Free, open source
Which Are the Best Airtable ETL Tools? Here are the top Airtable ETL tools based on their popularity and the criteria listed above:
1. Airbyte Airbyte is the leading open data movement platform, created in July 2020. It offers one of the largest catalogues of data connectors available, with 700+ connectors including a native Airtable source. Major users include Siemens, Calendly and AngelList. Airbyte integrates with dbt for transformation and with Airflow, Prefect or Dagster for orchestration, and offers an API and Terraform Provider alongside its interface. It runs as managed Cloud, self-hosted open source, or Enterprise Flex , a hybrid model where the data plane stays inside your own environment and credentials never leave your boundary.
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.
Data professionals can either deploy and self-host Airbyte Open Source, or leverage the cloud-hosted solution Airbyte Cloud where the new pricing model distinguishes databases from APIs and files. Airbyte offers a 99% SLA on Generally Available data pipelines tools, and a 99.9% SLA on the platform.
Pros Cons Native Airtable connector plus 700+ other sources One-way extraction, so no two-way Airtable sync Connectors are open source and editable Self-managed deployment needs Kubernetes or Docker skills Enterprise Flex keeps data and keys in your own environment Transformation relies on dbt rather than being built in Capacity-based pricing does not penalise frequent edits Not an orchestrator, so pair it with Airflow or Dagster
2. Fivetran Fivetran is a closed-source, managed ELT service that was created in 2012. Fivetran has 750+ sources and over 5,000 customers.
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? Being the first ELT solution in the market, they are considered a proven and reliable choice. However, Fivetran charges on monthly active rows (in other words, the number of rows that have been edited or added in a given month), and are often considered very expensive.
Here are more critical insights on the key differentiations between Airbyte and Fivetran
Pros Cons Native Airtable connector with automatic schema handling Monthly active row pricing suits Airtable data poorly 700+ connectors for combining Airtable with other sources Closed source, so connectors cannot be edited Very low ongoing maintenance Data processed on vendor infrastructure by default Reliable support and documentation Frequently edited bases inflate the active row count
3. Stitch Data Stitch is a cloud-based platform for ETL that was initially built on top of the open-source ETL tool Singer.io. More than 3,000 companies use it.
Stitch was acquired by Talend, which was acquired by the private equity firm Thoma Bravo, and then by Qlik. These successive acquisitions decreased market interest in the Singer.io open-source community, making most of their open-source data connectors obsolete. Only their top 30 connectors continue to be maintained by the open-source community.
What's unique about Stitch? Stitch competes primarily on price rather than breadth, which suits teams with a small number of common sources and modest volumes. The trade-off is a narrower connector catalogue and less depth in transformation than the larger platforms offer.
Here are more insights on the differences between Airbyte and Stitch .
Pros Cons Among the lowest-cost managed options Airtable support comes via a Singer tap, not a native connector Simple setup with a short time to first sync Only the most used Singer taps are actively maintained Transparent row-volume pricing Limited transformation capability Extensible through the open Singer specification Ownership has changed hands several times
4. Skyvia Skyvia is a no-code cloud data platform covering ETL, ELT, reverse ETL and two-way sync in one service, with more than 200 connectors including Airtable. It suits teams without engineering support who need Airtable data in a warehouse or kept in step with another cloud application.
What's unique about Skyvia? Skyvia's bidirectional sync is the differentiator. Most tools here move Airtable data one way into a warehouse, whereas Skyvia can keep Airtable and another system aligned in both directions, which suits operational workflows rather than analytics alone. It also offers query and backup features on the same platform. The trade-offs are that it is cloud-only, with no self-hosted option, and transformation is lighter than a dedicated modelling layer.
Skyvia offers a free tier for small volumes, with paid plans scaling by records processed and features used.
Pros Cons Bidirectional Airtable sync, not just extraction Cloud only, with no self-hosted option 200+ connectors covering CRM, ecommerce and databases Transformation is lighter than a dedicated modelling layer No-code interface suits teams without engineering support Record-based pricing needs monitoring at volume Query and backup features on the same platform Less suited to large-scale warehouse engineering
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 .
Pros Cons Free, open source and very widely adopted Not an ETL tool, so it ships no Airtable connector Pipelines defined in Python and version controlled You write and maintain the Airtable API logic yourself Handles dependencies, retries and scheduling well Production operation takes real engineering effort Pairs well with a connector-based tool for movement Steep learning curve for teams new to DAGs
6. Estuary Flow Estuary Flow is a real-time data integration platform built around streaming rather than scheduled batches. It captures Airtable records continuously and delivers them to warehouses, databases and streaming destinations with sub-minute latency, which matters when Airtable is the operational system of record and dashboards need to reflect edits quickly.
What's unique about Estuary Flow? Where most tools on this list poll the Airtable API on a schedule, Estuary keeps a continuous capture running and backfills history at the same time. That combination suits teams replacing an hourly sync with something closer to live. The trade-off is a smaller connector catalogue than the established ELT platforms and a newer, less mature ecosystem.
Pros Cons Sub-minute latency rather than scheduled polling Smaller connector catalogue than established ELT tools Backfill and streaming capture run together Newer product with a less mature ecosystem Public, private and bring-your-own-cloud deployment Streaming adds complexity most Airtable bases do not need Strong fit when Airtable is an operational system of record Usage-based pricing needs modelling upfront
7. Dataddo Dataddo is a no-code cloud integration platform aimed at teams that want Airtable data in a dashboard or warehouse without writing anything. It handles extraction, basic transformation and scheduling, and is SOC 2 Type II certified.
What is unique about Dataddo? Dataddo sits between a pipeline tool and a reporting connector. As well as loading Airtable data into a warehouse, it can send it straight to Looker Studio, Power BI or Tableau without a warehouse in between, which suits smaller teams whose Airtable base is the only source they need to report on.
The limitations are a smaller connector catalogue than the leaders and no self-hosted option, so data is processed on Dataddo's infrastructure.
Pros Cons Sends Airtable data straight to BI tools without a warehouse Smaller connector catalogue than the leaders No-code setup with a generous free tier Limited transformation capability SOC 2 Type II certified with automatic schema adaptation Cloud only, so data is processed on vendor infrastructure Predictable pricing from $99 per month Outgrown quickly by engineering-led teams
8. Portable.io Portable.io specialises in long-tail SaaS connectors, the niche applications that mainstream ELT vendors tend not to prioritise. It supports Airtable alongside hundreds of less common tools, and will build a missing connector on request, usually in days. Pricing is flat rather than volume-based, which makes it predictable. The trade-off is that it is a managed cloud service only, with no self-hosting and limited transformation, so it works best as an extraction layer feeding a warehouse where modelling happens.
Pros Cons Specialises in long-tail SaaS sources alongside Airtable Less compelling if all your sources are mainstream Builds missing connectors on request, often within days Smaller vendor with a limited community Flat-rate pricing regardless of data volume Cloud only, with no self-hosted option Fully managed, with no maintenance overhead Extraction layer only, so pair it with dbt for modelling
9. Whalesync Whalesync is the most Airtable-specific option here. Rather than one-way extraction, it keeps Airtable in two-way sync with Postgres, Notion, Webflow and other tools, so edits made in either system propagate to the other. That makes it useful when Airtable is a working interface on top of a database rather than just a source to be read. It is not a general-purpose ETL platform and will not serve warehouse analytics at scale, but for keeping Airtable and an operational database aligned it does a job the others do not.
Pros Cons True two-way sync between Airtable and other systems Not a general-purpose ETL platform Purpose-built for Airtable, Notion, Webflow and Postgres Will not serve warehouse analytics at scale No-code setup measured in minutes Narrow set of supported destinations Useful when Airtable is a front end on a database Per-record pricing adds up on large bases
10. Singer Singer is the first open-source JSON-based ETL framework, introduced in 2017 by Stitch as a way to make their prebuilt connectors extensible. A community tap for Airtable exists, so it remains a viable route for teams comfortable running taps themselves. The caveat is that maintenance has largely stopped since the Talend acquisition, and many taps and targets are increasingly outdated.
Pros Cons Open JSON-based specification anyone can implement Maintenance largely stopped after the Talend acquisition A community Airtable tap exists and is free to use Many taps and targets are increasingly outdated Taps are fully editable for missing endpoints No interface, scheduling or monitoring of its own Useful as a building block inside other tools No support or SLA
11. Rivery Rivery, now part of Boomi, is a cloud-based ELT platform with built-in transformation, orchestration and activation. It offers 150+ connectors including Airtable, with SQL and Python transformations and reverse ETL in the same product. Pricing is usage-based in Rivery credits, and because the credit cost varies by connector it can be hard to estimate in advance.
Pros Cons ELT, transformation, orchestration and reverse ETL in one 150+ connectors, fewer than the leaders SQL and Python transformations supported Credit cost varies by connector, making spend hard to predict Reverse ETL can push modelled data back to Airtable Cloud only, with no self-hosted option Now backed by Boomi after acquisition Roadmap direction depends on Boomi's priorities
12. HevoData Hevo Data is a no-code ELT platform with a native Airtable connector and around 150 integrations. It applies transformations, mappings and enrichments before data reaches the destination, offers automated error recovery, and can sync results back to source APIs. Pricing is event-based with a free tier, which is more predictable than row-based models for Airtable bases that change frequently.
Pros Cons Native Airtable connector with automated error recovery Around 150 integrations, well short of the leaders Event-based pricing with a free tier Event pricing still needs forecasting at scale Transformations and mappings before data lands Cloud only, so no self-hosted or hybrid option No-code interface suits non-engineering teams Connector gaps cannot be filled by your own team
13. Meltano Meltano is an open-source orchestrator dedicated to data integration, spined off from Gitlab on top of Singer’s taps and targets. Since 2019, they have been iterating on several approaches. Meltano distinguishes itself with its focus on DataOps and the CLI interface. They offer a SDK to build connectors, but it requires engineering skills and more time to build than Airbyte’s CDK. Meltano doesn’t invest in maintaining the connectors and leave it to the Singer community, and thus doesn’t provide support package with any SLA.
All those ETL tools are not specific to Airtable, you might also find some other specific data loader for Airtable data. But you will most likely not want to be loading data from only Airtable in your data stores.
Pros Cons Free, open source and Git-native CLI-first, so no interface for non-technical users Airtable available through the Singer tap ecosystem Connector maintenance is left to the Singer community Strong DataOps and CI/CD orientation No support package with an SLA Works well alongside dbt and Airflow Building connectors takes more effort than Airbyte's CDK
How Do You Choose the Right Airtable ETL Tool? As a company, you don't want to use one separate data integration tool for every data source you want to pull data from. So you need to have a clear integration strategy and some well-defined evaluation criteria to choose your Airtable ETL solution.
Airtable's API provides access to a wide range of data types, including:
How Do You Start Pulling Data From Airtable? If you decide to test Airbyte, you can start analyzing your Airtable data within minutes in three easy steps:
Step 1: Set up Airtable as a source connector 1. Open the Airbyte dashboard and click on "Sources" on the left-hand side of the screen.
Step 2: Set up a destination for your extracted Airtable data Choose from one of 50+ destinations where you want to import data from your Airtable source. This can be a cloud data warehouse, data lake, database, cloud storage, or any other supported Airbyte destination.
Step 3: Configure the Airtable data pipeline in Airbyte Once you've set up both the source and destination, you need to configure the connection. This includes selecting the data you want to extract - streams and columns, all are selected by default -, the sync frequency, where in the destination you want that data to be loaded, among other options.
And that's it! It is the same process between Airbyte Open Source that you can deploy within 5 minutes , or Airbyte Cloud which you can try here , free for 14-days.
Which Airtable ETL Tool Should You Choose? This article outlined the criteria that you should consider when choosing a data integration solution for Airtable ETL/ELT. Based on your requirements, you can select from any of the top 10 ETL/ELT tools listed above. We hope this article helped you understand why you should consider doing Airtable ETL and how to best do it.
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