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Airbyte vs. Airflow: 2024 Deep-Dive

Airbyte vs. Airflow: Which Is The Right Tool for You?

In 2024, data engineers are automating common data pipelines by using ETL tools to replicate data from disparate business applications into their cloud data warehouse for analytics.

With more data sources than ever, you've likely already encountered two of the leading ETL solutions -- Airbyte and Airflow.

In this comparison, we'll walk you through the pros and cons of the two platforms. We'll outline the functionality and the pricing models for each platform and even offer a simple framework to understand when to use each platform for data management.

Do You Really Need A Data Integration Tool?

The two most common use cases for data integration tools are 1) analytics and 2) automation.

Data integration solutions make it simple to extract data from APIs, databases, and files to then load the data into your data warehouse for business intelligence.

When using data for analytics use cases, data engineers leverage an ETL tool to load data from SaaS applications into Snowflake, Google BigQuery, Amazon Redshift, PostgreSQL, or SQL Server. From there, teams can build dashboards for better corporate decision-making.

On the other hand, automation use cases involve replacing manual tasks with real-time, automated workflows that sync data from one data source to another business application in a low-code or no-code manner.

If you're reading this guide, you have likely already identified a use case for data, and now you're wondering - How do I get data integrated from my business applications into my data warehouse or data lake for analytics?

There are few solutions as well known as Airbyte and Airflow for easy-to-use no-code connectors.

Who Can Benefit From No-Code Data Ingestion?

The short answer? Every business intelligence team.

Historically, ETL was difficult. You would need to hire data engineers, write code, and deploy a solution on-premises. Only then, could your team centralize the various data sources from across your enterprise into an analytics environment. There were early data integration platforms like Talend and Informatica that helped, but they weren't intuitive, had to be deployed on-premises, and the pricing was entirely tailored to enterprises.

In 2024, things have changed. No-code and low-code ETL and ELT tools make it simple to orchestrate workflows that move data from APIs, SaaS applications, databases, and files to your cloud data warehouse with minimal overhead. Instead of spending countless hours writing code, data teams can now use pre-built connectors to extract and load data for analytics and automation.

It doesn't matter if you're a small business building dashboards, or a large enterprise working with big data, navigating HIPAA, implementing data governance best practices, and training machine learning models. Everything starts with finding a simple way to ETL data into your data warehouse or data lake.

So, how does your data team benefit from an ETL tool?

You save the headaches and pain of building data pipelines (goodbye python, hello SQL), and instead, tap into pre-built connectors to extract data from hundreds of sources across your enterprise.

Data from collaboration tools (Microsoft 365, Asana, ClickUp), CRM systems (Salesforce, HubSpot), ERP platforms (NetSuite, Oracle), and email service providers (MailChimp, ActiveCampaign) can all be centralized without writing a single line of code.

Does your team love to code?

Great! Spend your time writing SQL, building dashboards, running machine learning models, and implementing best-in-class data governance frameworks. With ETL tools, you can free up your team to build data products instead of re-inventing the same data pipeline that every other business intelligence team is already leveraging.

How Does An ETL Solution Help?

ETL platforms like Airbyte and Airflow help business intelligence teams in three ways:

  1. Self-service data extraction. With hundreds of pre-built data connectors to common SaaS applications and databases, both platforms make data replication simple.

  2. Ready-to-query schemas for orchestration and data transformation. By syncing data into the warehouse, no-code solutions can be integrated with open source orchestration and transformation tools like Airflow and DBT to build data models, execute DAGs, and orchestrate complex pipelines.

  3. Low maintenance data pipelines. Leveraging an out-of-the-box solution allows your data engineers to analyze data without having to worry about rate limits, errors, hardware failures, and scaling issues. Vendors like Airbyte and Airflow offer a simple, low-maintenance solution.

Now, let's first dig deeper into Airbyte.

Airbyte: Deep-Dive Summary: Pros and Cons

Airbyte is an open-source ELT tool for the modern data stack.

A Airbyte subscription includes several capabilities, including:

  1. 170+ data source connectors
  2. Open source for direct access to code
  3. Change data capture support for databases
  4. Support for data warehouses and data lakes as destinations
  5. Credit based pricing model
  6. Warehouse-native data transformation

Airbyte: Pros

Airbyte offers an open-source platform for data integration. Unlike many other ELT vendors, clients can either use Airbyte Cloud (a SaaS solution) or an open-source version of Airbyte deployed on its own.

The platform is tailored to engineers. As an engineering team, if you want to build your own data integrations, Airbyte offers a CDK to accelerate development vs. writing code from scratch.

For destinations, Airbyte supports over 20 destinations. When you need information loaded into a bespoke platform, Airbyte can be a great solution when other ELT tools don't support your destination.

Airbyte: Cons

For long-tail integrations that you build on your own with the Airbyte CDK, your team is on the hook for maintenance, support, and fixing things when they break. Unlike Portable, writing your own integration with Airbyte can lead to ongoing maintenance efforts.

The Airbyte pricing model isn't straightforward. With a credit-based, volume-based consumption model, it can be difficult to predict usage and to mitigate costs as your data volumes increase.

Many of Airbyte's connectors are still currently in alpha, or not yet production-ready. In many scenarios, users can be on the hook for maintenance, flagging issues, and working with the Airbyte team on resolutions.

Airflow: Deep-Dive Summary: Pros and Cons

Airflow is an open source framework to author, schedule and monitor workflows.

A Airflow subscription includes several capabilities, including:

  1. Python defined data pythones that can be instantiated quickly
  2. A scalable, modular, architecture to orchestrate workers
  3. The flexibility to define your own operators (integrations, etc.)
  4. An out-of-the-box UI to monitor, schedule, and manage workflows
  5. Plug-and-play operators to execute tasks on Google Cloud Platform, Amazon Web Services, Microsoft Azure, and other third-party services

Airflow: Pros

Airflow offers a robust, solution for orchestrating and managing complex data pipelines.

The product is free and open-sourced, offering the flexibility to tailor the solution to your needs.

Airflow is widely adopted and has an ecosystem of companies that have built out-of-the-box operators.

Airflow: Cons

Airflow is an orchestration tool instead of a pure-play data replication solution.

The solution is tailored to engineers and must be deployed in order to start orchestrating workloads.

While there are out-of-the-box operators to some platforms, Airflow does not have the breadth of connectors you would expect from a pure-play ETL solution.

Now that we've outlined the pros and cons of the two platforms, let's analyze Airbyte as a Airflow alternative, and Airflow as a Airbyte alternative.

Airbyte vs. Airflow - Feature Comparison

It is important to dig into the true capabilities of the platforms we are considering. Let's dive into the features, functionality and pricing of the two platforms.

Pre-Built Source Connectors

One of the most important criteria for selecting an ETL tool is whether or not the product supports the data sources you need.

Most vendors don't build many new data sources each year, so when you consider the offering, you're really purchasing access to the connectors they already have in their catalog. Breadth of connectors is a strong proxy for a vendor's ability to help your analytics team centralize data.

Airbyte's open source product includes 170+ data sources.

The cloud offering is newer and has some restrictions on what sources can be leveraged within the product; however, it's reasonable to expect that all 170+ would be available in the cloud product shortly.

Airflow has over 100 prebuilt operators. While an operator doesn't necessarily equate to an ETL connector for data extraction, these operators can help to orchestrate pipelines created within other platforms.

Custom Connector Development

When your team needs a new connector, you NEED the connector.

It's important to understand how both data integration platforms will help in these scenarios. Do they ask you to write code? To maintain the connector? To fix things when they break?

Airbyte is open source, so engineers can develop their own custom integrations.

In these scenarios, developers still need to read the source documentation, learn the Airbyte protocol / CDK framework, and set up the integration. For custom connectors, the user that built the connectors has to maintain their own connector and troubleshoot issues as they arise.

Custom connectors must be written in code and then orchestrated by Airflow.

Pricing & Plans

Let’s now compare the pricing of Airbyte vs. Airflow. There are both similarities and differences to be aware of.

Airbyte has a credit-based pricing model.

  1. Pricing increases with data volumes
  2. Credits must be purchased before consumption; however the company offers initial free credits when an account is opened
  3. Airbyte's open source product is a free alternative for engineers that want to manage infrastructure on their own

Airflow is open-source and free.

Apache Airflow is open-sourced under the Apache License Version 2.0.

Maintenance & Support

Data integrations are living, breathing organisms. They evolve, they break, and they cause chaos with your queries and dashboards when they do.

It's critical to understand how both ETL vendors will support you when things go wrong, and what functionality each platform has in place for alerting, monitoring, and connector maintenance.

Airbyte has over 12,000 users of the open source product, so users are asked to leverage the documentation, community Slack and Discourse communities to troubleshoot issues.

Because Airflow is open-source, all maintenance must be handled by the user directly.

The project is well adopted with a significant number of contributers, but if you build a custom connector, you will need to maintain it yourself.

Now that we've outlined what each brand offers, let's quickly recap the takeaways.

Airbyte or Airflow? What's The Best Option?

Choosing an ETL solution is an important decision that you need to make based on your own specific needs.

We've outlined the pros and cons of both Airbyte and Airflow to help frame out the scenarios in which each solution makes sense.

At Portable we focus our efforts on a customer-first culture, a try-before-you-buy business model, and hands on support when things go wrong.

There's no downside to exploring our connector catalog, or even requesting the connector that's at the top of your backlog.