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 -- Precog 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.
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 Precog and Airflow for easy-to-use no-code connectors.
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.
ETL platforms like Precog and Airflow help business intelligence teams in three ways:
Self-service data extraction. With hundreds of pre-built data connectors to common SaaS applications and databases, both platforms make data replication simple.
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.
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 Precog and Airflow offer a simple, low-maintenance solution.
Now, let's first dig deeper into Precog.
Precog is a no-code universal data connector platform.
A Precog subscription includes several capabilities, including:
More out-of-the-box data sources than any other platform.
Straightforward, no-surprises pricing.
AI engine that can understand new data sources without code.
SaaS and on-premise options available.
Fairly new platform with limited community.
No-code basis can be limiting for teams with technical expertise.
Connector-based pricing can be expensive for teams with a large number of sources.
Airflow is an open source framework to author, schedule and monitor workflows.
A Airflow subscription includes several capabilities, including:
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 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 Precog as a Airflow alternative, and Airflow as a Precog alternative.
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.
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.
Precog offers over 10,000 data sources.
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.
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?
Precog's uses AI to generate connectors without using code. Precog creates new data sources upon request within 48 hours.
Custom connectors must be written in code and then orchestrated by Airflow.
Let’s now compare the pricing of Precog vs. Airflow. There are both similarities and differences to be aware of.
Precog's pricing is not publicly available.
Request a trial or demo for pricing (pricing based on data source).
Airflow is open-source and free.
Apache Airflow is open-sourced under the Apache License Version 2.0.
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.
Precog offers customer support, with specific details based on your needs.
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.
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 Precog 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.