The Top 7 Alternatives to Google Cloud Composer

Chances are have your own horror stories about an engineering team that picked the wrong tool for the job, either a legacy tool that overstayed its welcome or a once-promising new tool that never reached its full potential, forcing engineers to spend years, and thousands of hours, working around their tooling, instead of with it.

Rather than solving key business problems, engineers are constantly fighting fires, struggling to meet deadlines, and explaining to company leadership why sales dashboards don’t have up-to-date information and machine learning workloads keep failing.

Picking the right tools for your data stack depends on your exact business and engineering needs, and the choice may seem daunting. Thankfully, there are several popular tools, each with thousands of users, all with a unique approach for managing data pipelines.

In your research, you may have come across Google Cloud Composer. It’s one tool worth considering, but there are several other great options out there.

Here are seven top alternatives to Google Cloud Composer.

#1 Astro by Astronomer

Astro is the easiest way to get up and running with Airflow. A fully hosted, developer-first, cloud product, Astro is built for teams that need to move fast and don’t have time to worry about their scheduler going down. Airflow is cloud-agnostic, and Astro continues this approach by letting you pick the cloud platform of your choosing. It’s built so your data team can focus on writing DAGs, so that dashboards are always updated and machine learning models are trained and ready for production, even at enormous scale.

To see Astro in action, try out Astronomer’s 14-day free trial of Astro, which will get you a dedicated Airflow environment in under five minutes.

All of Airflow’s native features are available on Astro, including a UI for monitoring DAGs, logs, users, version upgrades, and alerts in a single place. Astro integrates directly with the rest of your stack via Airflow’s prebuilt operators, and includes an easy-to-use REST API for your custom integrations.

For teams looking to run Airflow in their private clouds, Astronomer has infrastructure options to support this use case as well.

Astronomer’s commitment to developers is possible, in part, because it employs so many of Airflow’s core developers. This means Astronomer’s customers can learn best practices from top Airflow experts, who are using customer feedback to constantly making Astro, and Airflow, even easier to use.

#2 Self Hosted Apache Airflow

Airflow, which powers GCC, is an open source workflow tool that makes it easy to programmatically design, schedule, and orchestrate pipelines in Python. The industry standard for workflow orchestration, Airflow has seen broad adoption and has grown a large community of contributors. Over 2,400 community members have contributed to the codebase, and the Slack community has over 30,000 members.

Airflow is designed to be massively scalable and reliable enough for critical workflows, with a flexible interface that is easy to extend. That allowed the community to build more than 1,300 operator modules that provide pre-made integrations with over 90 popular tools, APIs, and cloud platforms. Airflow comes with REST API support and a user interface that makes it easy to execute tasks, monitor workflow, and configure your Airflow environment.

The easiest way to get Airflow up and running locally is with the Astro CLI. Airflow also has an official Helm chart for deploying with Kubernetes.

#3 Argo Workflows

Argo is an open source tool that lets users build DAG-based workflows on Kubernetes. Designed to be cloud agnostic, each task is executed in a standalone Kubernetes pod, which allows workflows to include thousands of parallel task executions. Argo includes a UI for visualizing artifacts as well as a REST API that can be used for creating new workflows.

Argo is a solid choice for teams looking for a Kubernetes-specific workflow engine who aren’t dissuaded by defining complex pipelines in YAML. There are some drawbacks to this approach. Argo resources are defined at the individual container level, meaning Argo doesn’t have pre-existing operators with common integrations for external services. Users can accomplish this with third-party containers, but this approach may still require additional programming.

Finally, it has limited options for rescheduling failed workloads in a fault-tolerant manner, which means mission-critical workloads might not be re-executed when they’re skipped.

#4 Kubeflow

Kubeflow is a cloud-agnostic workflow manager for running machine learning jobs on Kubernetes. It’s built on top of the Argo Worfklows, and includes a set of Python libraries for defining workflows (called the Kubeflow DSL), rather than using Argo’s YAML-based workflow design. Though it was originally developed at Google, it’s now a fully open-source project.

Users report that Kubeflow typically works well once it has been configured, but that setup is difficult and takes too long. There are also complaints that the learning curve is quite steep and that the DSL can be challenging to learn. The Python interface received major complaints for the 1.X version, but the interface has improved somewhat with the 2.X version.

For teams looking for a Kubernetes-specific framework optimized for machine learning, it’s a common choice, but those looking for a more powerful, general purpose tool that doesn’t lock them into Kubernetes will be better served by other tools in this list.

#5 Apache Beam

Apache Beam takes a different approach than many of the tools in this list. Unlike the standalone orchestration tools like Airflow or Argo, Beam is an backend-agnostic interface layer that lets you plug in backends like Apache Flink or Google Cloud Dataflow. Users can program pipelines in Python, Java, Go, or Scala, and then change their backend, or use multiple, without needing to rewrite their pipelines. Beam also allows a unified interface for batch and streaming jobs.

The ability to decouple pipelines from the backend comes with a cost. Abstraction layers add inherent complexity, and when backends add new features, there can be a delay between the release and when support is added into Beam. If Beam doesn’t add support for new backend functionality, then it’s unavailable in your pipelines.

Beam doesn’t have workflow scheduling, so it’s often combined with Airflow. This can be a best-of-both-worlds option for teams who need reliable scheduling and orchestration, but also value a shared interface for writing batch and streaming jobs in a variety of languages.

You can find the provider package for connecting Beam with Astro in the Astronomer Registry.

#6 MLFlow

First developed at Databricks before joining the Linux Foundation, MLFlow is a tool for building, deploying, and managing machine learning models. Unlike Kubeflow, MLFlow isn’t tied to Kubernetes, which gives users more flexibility in determining an execution environment. It’s primarily written in Python, but also has support for Java and R, and includes a web UI for viewing completed runs. MLFlow is optimized for easy experimentation and receives praise for being easy to learn and requiring little code to integrate into a project.

On its own, MLFlow lacks some of the key features needed to run scheduled, scalable pipelines. When building production systems, it’s often paired with Apache Airflow, which handles the automation and data orchestration needed to support MLFlow. You can read more about combining MLFlow with Astro, which is powered by Airflow.

#7 Apache NiFi

Originally developed by the NSA, NiFi lets users create cross-system flows of data using a drag-and-drop interface. NiFi is built on the JVM, and has been open source since 2014, when the NSA donated it to the Apache Software Foundation. It comes with over three hundred premade functions, or “processors”, such as Base64EncodeContent, FetchS3Object, and UpdateByQueryElasticsearch.

The no-code design works well for simpler tasks, but adds complexity when workflows become more involved. This is especially problematic when users need multiple similar workflows, as they each need to be rebuilt from scratch. The pre-existing processors are well made, but users have found Nifi complicated when they need functionality that isn’t supported by existing processors.

When used strictly to control the simple flow of data between systems, users are generally pleased with NiFi’s functionality and experience. However there are better tools for more complex workflows and data management pipelines, especially when users need to monitor pipeline execution and replay failed tasks.

Which data tool is the best fit for your business?

Researching Google Cloud Composer alternatives demonstrates how different tools address different parts of the data workflow, and gives insight into how those tools will integrate with your existing data stack.

Teams looking for a tool that can manage the entire data lifecycle, while integrating with their existing data stack, should consider Astro by Astronomer. Astro takes the industry standard for data pipelines, Airflow, and delivers it in a developer friendly, managed service that lets your engineers focus on solving business priorities, while leaving management to the top Airflow experts.

Experience the best way to run Apache Airflow with Astronomer’s 14-day free trial of Astro.