Increase Efficiency with Apache Airflow Managed Service Operations

Any data-driven business needs at least one orchestration service to automate and streamline workflows, ensuring seamless coordination of tasks across diverse tools and platforms. Within larger organizations or a data mesh culture, this need arises even within the smaller units, departments or data product teams. Orchestration services like Apache Airflow enable efficient management of complex automation sequences and data pipelines, improve scalability, and ensure reliability by integrating monitoring, error handling, and dynamic resource allocation. By centralizing workflow control, orchestration services reduce operational overhead and empower businesses to focus on deriving insights and value from their data.

Apache Airflow is the leading open-source platform for defining and orchestrating workflows, and automating task sequencing in data pipelines. Its unique code-as-configuration approach enables data engineers to define workflows in Python and execute them as directed acyclic graphs (DAGs) with extensive options for logging and monitoring, failsafes and recovery, and parallelisation. With a large ecosystem and broad adoption, Airflow has become an essential tool for managing complex workflows across modern data systems.

Once an orchestration system is established, especially when you choose to run with a self-hosted open source software, outsourcing the day-to-day operations effort and switching to a professional managed service model can free up internal resources. Today, we take a look at managed service offerings for Apache Airflow specifically.

Why is Airflow Still Relevant in 2025?

Data orchestration is more important than ever, but as Apache Airflow has been on the scene for a decade now, let’s first look at why this tool is still relevant today. Despite the rise of embedded orchestration components in platforms like Microsoft Fabric and Databricks, Apache Airflow is still a viable choice due to its clarity of vision, extensibility, flexibility, and active open-source community. Airflow excels in orchestrating workflows that span multiple platforms, a necessity for organizations operating in hybrid or multi-cloud environments. 

Its extensive library of provider integrations ensures compatibility with diverse tools, enabling businesses to build pipelines across heterogeneous tech stacks. Furthermore, Airflow’s open-source nature helps organizations avoid vendor lock-in, offering greater control over their orchestration infrastructure—an important consideration in today’s evolving data landscape. Airflow DAGs are Python code which means you can apply decades worth of best practice knowledge from software engineering to guarantee a high quality development process and create any automation that you can think of. No matter how ambitious or obscure your idea is, Airflow can be the framework for implementing a custom solution without reinventing the wheel regarding the operational aspects.

With cloud-based data and business intelligence platforms becoming more mature and feature rich, you might not need Apache Airflow as a dedicated orchestration service any longer, or you might want to migrate parts of your existing Airflow ecosystem closer to where your data resides. You might as well look into orchestration services that are more specialised e.g. for data-aware processing, like Prefect or Dagster. Airflow is still a valid option to get started or to scale up and we take a look at the benefits of bringing in a managed service partner to handle the mundane day-to-day operations of the system for you.

Example of how Apache Airflow can be applied to manage ELT-style (extract-load-transform) data pipelines
to load data from a source system into a warehouse for further processing.

Airflow Operation Models: SaaS, Managed Service, and On-Prem

Airflow’s versatility is reflected in its range of operation models, catering to different organizational needs:

• SaaS (Software-as-a-Service): Fully managed services like Astronomer, the awkwardly named AWS service Amazon Managed Workflows for Apache Airflow (MWAA), or Google Cloud Composer eliminate operational overhead, making them ideal for teams seeking rapid deployment and scalability.
• Public/Private Cloud Managed Service: Managed services in private cloud environments deliver enhanced security and control, aligning with the needs of enterprises that prioritize compliance and data sovereignty.
• On-Premises: Deploying Airflow on-prem remains a viable option for organizations requiring complete control over their infrastructure. However, this model demands significant resources for setup, scaling, and maintenance, making it less practical for teams with limited operational capacity.

Each of these models addresses specific use cases, allowing organizations to choose based on their security requirements, resource availability, and operational goals. There is also a clear growth path involved: teams choosing Airflow to get started with proper workflow orchestration because it is freely available as an open source tool to dive in and build a showcase yourself. As that showcase turns into an integral part of your business-critical applications, operational effort increases and bringing in professional services support or migrating to a managed service offering can save valuable time and ultimately budget.

Effective workflow management with Apache Airflow 2.0

Benefits of Apache Airflow Managed Service Offerings

Managed Airflow services—whether in public or private cloud environments—provide numerous advantages over self-managed deployments:

1. Reduced Complexity: Managed providers take care of infrastructure setup, updates, and scaling, allowing teams to concentrate on developing workflows instead of managing systems.
2. Improved Scalability: These services automatically adjust resources to match workload demands, ensuring smooth operations during peak and off-peak periods.
3. Enhanced Reliability: Built-in high availability, disaster recovery, and monitoring features minimize downtime and ensure consistent performance.
4. Cost Efficiency: By removing the need for hardware procurement and maintenance, managed services often result in lower total cost of ownership compared to on-premises setups.
5. Security and Compliance: Providers implement robust security measures like encryption and regulatory compliance, reducing the burden on internal teams.

For organizations with sensitive workloads or strict compliance requirements, managed services in private cloud environments offer an ideal balance of operational simplicity and control.

NextLytics’ choice for Apache Airflow Managed Service

Our Airflow Professional Services team has supported customers with on-prem Apache Airflow operations and development for more than 5 years. We see that the large public SaaS offerings for Airflow are great for highest scalability requirements but work best in fully cloud-centric environments. Furthermore, these are operated by US-based companies which might become a legal issue for technical systems operation again for EU-based customers considering the brittle foundation data protection guarantees by the US government.

For most businesses and teams, a smaller yet still fully scalable private cloud Apache Airflow Managed Service might be the safest and most cost efficient way to operate the versatile orchestration platform. The German cloud provider STACKIT has recently added data and machine learning Platform-as-a-Service products to their portfolio of certified secure services. NextLytics partners with STACKIT to provide customers with a true private cloud managed service solution of Apache Airflow.

Example overview of how the Apache Airflow Managed Service offering provided by NextLytics and
STACKIT can integrate with your on-prem data pipelines.

Check out the following comparison on classic on-prem professional services around Airflow and the new managed cloud service option on STACKIT that we present:

Apache Airflow Managed Service Operations - Our Conclusion

Apache Airflow continues to thrive as a cornerstone for workflow orchestration in 2025, thanks to its flexibility, extensibility, and ability to manage cross-platform pipelines. Whether deployed as SaaS, managed services, or on-premises, Airflow adapts to diverse operational needs, making it an indispensable tool for modern data engineering. Embracing managed services allows organizations to focus on delivering value through data while benefiting from enhanced efficiency and scalability.

The STACKIT managed Airflow service described above is just one component of a larger, fully-featured business intelligence and machine learning platform-as-a-service portfolio driven by best of breed open source software. Reach out to us to learn more about NextLytics Airflow Professional Services and our partnership with the STACKIT cloud data platform.