When hearing the term data orchestration, many people intuitively think of ETL and analytics. Tools like Apache Airflow are commonly used for data pipelines that extract, transform, and load data into data warehouses and data lakes. While analytics is important, we see data orchestration as a broader concept, encompassing how data moves across your entire business. Kestra automates workflows where it matters most, beyond ETL.

Data Orchestration Beyond Analytics

Today, companies rely on internal and external APIs to keep their businesses running. They need to manage critical processes and connect various applications in real time. Whether it’s sending event notifications, updating inventory, or interacting with payment gateways, data needs to flow reliably between these operational systems. Most data orchestration tools, including Airflow, are heavily focused on analytics, often overlooking the operational side.

In contrast, using Kestra, you can automate data flow across operational systems and APIs with confidence. Hundreds of companies rely on Kestra to manage complex workflows that process data across ERP, CRM, PLM, and internal systems in real time, not just in nightly ETL jobs.

When Airflow is Sufficient

Airflow is well-suited for teams focused on data pipelines and analytics workflows. If your goal is to manage ETL or ELT jobs, schedule batch data processing tasks, and load data into a data warehouse or data lake, Airflow is a strong contender. It’s a familiar tool for those working primarily with Python-based tasks.

However, what happens when your orchestration needs grow beyond data analytics?

When Kestra is the Smarter Option

Airflow works for data engineering, but it struggles when you need to automate workflows for the entire IT department with multiple teams, environments, internal systems, and external APIs.

If next to data pipelines, you’re also automating customer-facing processes, business operations, or DevOps tasks, that’s where Kestra shines.

For example, Airpaz, a travel platform, needed to orchestrate data movement between booking systems, payment gateways, and CRM tools. Their workflows extended far beyond analytics and reporting — they needed to ensure reliable coordination across multiple critical applications. Kestra allowed them to keep their operational systems in sync, providing a reliable booking experience for millions of customers.

Why Choose Kestra: Simplicity and Flexibility

One of the key advantages of Kestra is how simple it makes orchestrating complex workflows. With the built-in editor and hundreds of plugins working out of the box (without the overhead of managing Python dependencies), you can configure your orchestration logic in just a few lines of YAML. It’s like having a co-pilot for automation — offering guidance with autocompletion and syntax validation, simplifying orchestration for routine tasks while staying flexible for custom code when needed.

Unlike Airflow, which requires boilerplate Python DAGs for everything, Kestra doesn’t lock you into a single language or way of working. You can define your workflows in a declarative configuration and only introduce custom code when more complex logic is required for the problem at hand. This API-first approach allows software engineering teams to automate their workflows end-to-end using their preferred languages, including Java, Node.js, Python, R, Go, Rust, Shell, PowerShell, or simply running Docker containers.

Consider Gorgias, which chose Kestra because it fits perfectly with their Infrastructure as Code (IaC) approach. Using Kestra, they could not only orchestrate their analytical data workflows involving tools like Airbyte, dbt, and Hightouch, but also automate operational tasks like infrastructure builds, CI/CD pipelines, and event triggers across systems. They didn’t need to write repetitive code — they used a mix of YAML and Terraform configurations for the bulk of their workflows and added custom logic only when absolutely necessary.

Unified Platform from Development to Production

One of the standout features of Kestra is how it unifies Everything-as-Code with a user-friendly UI. Users can start building workflows from the embedded editor in the UI, test them, and iterate quickly. Once everything works as expected, you can easily push the underlying workflow code to Git and promote it to staging and production environments. This iterative approach helps teams move faster without being locked into a specific deployment model.

For Leroy Merlin France, combining Kestra’s user-friendly UI with its Everything-as-Code approach made it possible to use the UI for development, and integrate Terraform and GitHub Actions for production deployments. This helped Leroy Merlin to scale their operations and enable hundreds of end users to work together across development and production environments without friction.

Lower Barrier to Entry

Kestra is designed with a low barrier to entry. You don’t need to be an expert in any single programming language to start orchestrating workflows. Our system is approachable to a wide range of users, including a.o. domain experts, developers, DevOps, data engineers, and business analysts. By allowing users to mix simple YAML configurations with custom code when needed, Kestra reduces complexity and empowers teams to focus on solving business challenges instead of getting stuck in technical details.

For example, Quadis, a car dealership, transitioned from legacy systems to Kestra. In just three months, they onboarded five developers, deployed multiple instances, and began orchestrating workflows ranging from financial reporting to ERP and CRM integrations. Kestra’s simplicity helped them get up and running quickly, automating critical business operations with minimal coding.

When to Choose Kestra Over Apache Airflow

You Automate More Than Simple ETL: If your focus is solely on scheduling data pipelines and managing ETL workflows for analytics, Airflow will likely serve your needs well, especially if your development skills are only Python-oriented. However, Airflow alone may struggle to support future use cases that extend beyond analytics.

Your Workflows Interact With Critical Systems: If your workflows involve more than data pipelines, such as coordinating APIs, automating operational processes, or managing business-critical systems, Kestra’s broader capabilities are a better fit.

You Want Simplicity and Flexibility: Kestra’s intuitive YAML-based syntax and built-in UI editor simplify automation without the need for boilerplate DAGs. For teams that prefer not to be locked into Python, Kestra offers the flexibility to use whatever language best suits the task.

You Need a Unified Platform: Kestra allows you to build workflows iteratively in the UI, test them in real time, and promote their underlying code to production environments without friction. This unified approach helps teams move faster while keeping your workflow code version-controlled and aligned with your deployment practices.

The Future of Data Orchestration Beyond Analytics

When your orchestration requirements move past analytics and into real-time business operations, Kestra gives you a simpler, more flexible, and unified solution. Whether it’s managing data pipelines or automating critical workflows, Kestra helps you scale operations, connect systems, and keep things maintainable — without having to tediously write and rewrite complex DAGs.

TL;DR: If you’re looking for more than a data pipeline orchestrator, it’s time to consider Kestra.