Automate the workflows that keep your business running
Every organisation has processes that follow predictable patterns - data arrives, gets transformed, triggers decisions and produces outputs. Apache Airflow lets you define these workflows as code, schedule them reliably and monitor them from a single interface. Node deploys Airflow as the orchestration backbone of your automation platform.
What Airflow does and why it matters
Airflow is a workflow orchestration platform originally created at Airbnb and now maintained by the Apache Software Foundation. It allows you to define complex workflows as Directed Acyclic Graphs (DAGs) using Python code, meaning your automation logic is version-controlled, testable and reviewable just like any other software.
Where traditional scheduling tools operate as black boxes - cron jobs scattered across servers with no visibility into dependencies or failures - Airflow provides a complete picture. You see every task, its status, its dependencies, its execution history and its logs in a single web interface. When something fails, Airflow tells you exactly what failed, why, and lets you retry from that specific point.
Airflow is used in production by thousands of organisations including Adobe, Spotify, Twitter, Slack, Robinhood, PayPal and United Airlines. It handles everything from simple daily ETL jobs to complex multi-stage data pipelines processing petabytes.
How we deploy Airflow for business automation
We use Airflow as the central coordination layer for automated business processes. Rather than building point-to-point integrations between systems, Airflow acts as the conductor - orchestrating tasks across your CRM, ERP, data warehouse, AI models and external APIs in a defined, repeatable sequence.
A typical automation workflow might extract customer data from your CRM, run it through a segmentation model hosted on your private AI infrastructure, generate personalised content using an LLM, push results to your marketing platform and log outcomes back to your data warehouse. Airflow manages the entire chain, handling dependencies, retries, timeouts and alerting at every step.
Key capabilities we implement
Workflows as code - every workflow is defined in Python, giving you full version control, code review, testing and environment promotion. No more undocumented manual processes that only one person understands.
Intelligent scheduling - run workflows on schedules, in response to data arrival, or triggered by external events. Dependencies between tasks are handled automatically, so downstream processes only execute when their prerequisites complete successfully.
Comprehensive monitoring - the Airflow web UI provides real-time visibility into every running and historical workflow. Gantt charts show execution timing, tree views display dependency status and task logs are accessible directly from the interface.
Failure handling and alerting - configure automatic retries with exponential backoff, set SLA deadlines with alerts, and receive notifications via email, Slack or PagerDuty when workflows need attention. Failed tasks can be retried individually without rerunning the entire workflow.
Extensible operator library - connect to virtually any system through Airflow's extensive library of operators and hooks. Native support for databases, cloud services, REST APIs, SSH connections, Kubernetes pods and custom Python functions.
Scalable execution - deploy with the CeleryExecutor or KubernetesExecutor to distribute work across clusters. Airflow scales horizontally to handle hundreds of concurrent workflows with thousands of tasks.
Airflow in your automation stack
Airflow pairs naturally with the rest of the Apache ecosystem. It orchestrates Spark jobs for data processing, coordinates Kafka consumer workflows, triggers NiFi data flows and schedules Superset report refreshes. With APISIX managing API traffic and Airflow managing the orchestration, your automation platform operates as a unified, observable system.
Trusted in production worldwide - Apache Airflow orchestrates workflows at some of the most data-intensive companies on the planet. Spotify uses it to coordinate thousands of data pipelines daily, Adobe runs marketing analytics workflows through it, and Shopify depends on it for data engineering at scale. PayPal and Slack both built their data infrastructure on Airflow. Node deploys and operates Airflow with the same reliability these organisations require.
Talk to us about workflow orchestration.
Drop us a line, and our team will discuss how Airflow can automate your business processes.