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

In 2023, 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 -- Airflow and Snowplow Analytics.

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 Airflow and Snowplow Analytics 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 2023, 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 Airflow and Snowplow Analytics 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 Airflow and Snowplow Analytics offer a simple, low-maintenance solution.

Now, let's first dig deeper into Airflow.

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.

Snowplow Analytics: Deep-Dive Summary: Pros and Cons

Snowplow Analytics is a behavioral data creation platform to create data from websites and mobile apps.

A Snowplow Analytics subscription includes several capabilities, including:

  1. SDKs to create data from your website or mobile app
  2. Integrations with data warehouses and other downstream platforms
  3. Open-source extensibility to tailor the solution to your needs
  4. First-party management of your own data (deployed within your own environment)

Snowplow Analytics: Pros

Free, open-source solution for data creation

Robust SDKs to create data from websites and mobile apps

Support for downstream applications and warehouses out-of-the-box

Software is deployed in your own cloud environment

Snowplow Analytics: Cons

Snowplow BDP Cloud is not yet generally available

For ETL purposes, Snowplow does not support many business applications as data sources

Because software is deployed in your environment, you have to pay the cloud compute and storage bills

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

Airflow vs. Snowplow Analytics - 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.

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.

Snowplow offers 30+ prebuilt connectors - most of which are SDKs to create data from your first party properties

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?

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

You can leverage Snowplow's SDKs to create data; however, capabilities are limited when it comes to extraction data from an upstream business application (API, file, database)

Pricing & Plans

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

Airflow is open-source and free.

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

Snowplow is open source with a cloud offering on the way (currently waitlist).

  1. Snowplow is open-source and free to use
  2. There is a waitlist available for Snowplow Behavioral Data Platform (BDP) Cloud
  3. For large Snowplow implementations, you can get a custom quote

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.

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.

Snowplow is a widely adopted open-source solution with a strong community. For enterprise clients, you can get a custom quote for a more in depth implementation

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

Airflow or Snowplow Analytics? 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 Airflow and Snowplow Analytics 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.