The Top 7 Alternatives to MWAA

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Whether it’s keeping sales dashboards up to date or running complex machine learning workloads, every company eventually realizes they need a long-term solution for orchestrating how data workflows move through their system. Choosing the wrong tooling can mean wasted engineering time, or worse, critical pipelines failing silently without anyone knowing.

Company engineering blogs are full of examples of companies investing heavily in a data orchestration or workflow management tool, only to realize they made the wrong choice and need to spend months changing software - usually with costly downtime and performance degradation.

The right data orchestration tool depends on the specific needs of your organization, including tech stack, system scale, workflow complexity, observability needs, engineering maturity, and the tool’s community size.

That may sound daunting, but the good news is there are multiple popular tools, each with die-hard fans online, all exploring different ways of solving this common problem. One tool you may have heard of is MWAA (Managed Workflows for Apache Airflow) by Amazon Web Services.

While MWAA is one option, it’s just one of the tools you will likely hear about during your research, all of which are worth considering when deciding what’s the best fit for your organization.

Here’s a look at seven alternatives to MWAA.

#1 Astro by Astronomer

Astro by Astronomer offers a simple solution for adopting Apache Airflow and optimizing your operations. It encompassess all the inherent features of Airflow, but takes it a step further by offering convenient monitoring of Directed Acyclic Graphs (DAGs), logs, users, version upgrades, and receiving alerts — all from one centralized location. This comprehensive approach simplifies workflow management and improves operational efficiency. With Astro, data teams can focus their efforts on crafting DAGs, ensuring up-to-date dashboards, and training machine learning models for production, even at massive scales.

One of the key strengths of Astro is its seamless integration with existing tech stacks. By leveraging Airflow’s prebuilt operators, Astro establishes a direct connection, enabling easy integration with your current infrastructure. Additionally, Astro offers a user-friendly REST API, allowing for quick custom integrations into workflows and increasing their flexibility and extensibility.

Astronomer collaborates with the core developers of Airflow, providing Astro users with access to top Airflow knowledge and expertise. The continuous incorporation of customer feedback enhances the user-friendliness of both Astro and Airflow, simplifying workflows for all users.

As a cloud-based product, Astro prioritizes developers and provides a fully hosted solution, catering to teams with a need for speed and freedom from scheduler concerns. With Astro, users have the option to deploy it on any infrastructure of their choice, whether it’s on-premises, in a private cloud, or on a public cloud other than AWS. In contrast, MWAA limits users to the AWS ecosystem, restricting their options for integrations.

To experience the capabilities of Astro firsthand, you can take advantage of the 14-day free trial.

#2 Self Hosted Apache Airflow

Airflow is the industry standard for modern data orchestration. First developed at Airbnb before being adopted into Apache’s portfolio, it quickly became popular because it allowed data and software engineers to programmatically build flexible, extensible workflows with regular Python code. Airflow has since grown an avid community and industry-wide adoption. A clear sign of this adoption are the 1,300+ operator modules built by the community; these third-party plugins make it easy to interact with nearly 100 major cloud-platforms, tools, and popular APIs.

Starting with the 2.0 release, Airflow has pursued two parallel development goals: supporting large production scale by engineering high-availability and low-latency into the core systems, and making it easier to run with simplified DAG interfaces and a full-featured REST API. Airflow includes a built-in UI for monitoring or triggering workflows, as well as lineage support to track how data moves for debugging, audit trails, and data governance.

#3 Luigi

Luigi is an open source project built by Spotify to manage coordination of long-running batch processes. One of the earliest of the modern generation of data orchestration tools, Luigi lets users define workflows in Python and includes a UI for visualizing pipelines. Luigi’s fans praise its flexible scheduling and the large number of data systems it supports.

The biggest drawback of Luigi is the complexity involved as it scales. As teams get larger, workflows get more complex, and the volume of data increases, Luigi’s design choices can become an obstacle. This limitation, along with continued development of competing data orchestration approaches, are the main reasons Luigi has lost popularity over the past several years. In fact, in 2022 Spotify, where Luigi was originally developed, announced that they’re moving away from Luigi due the difficulties maintaining and supporting it at scale.

#4 Apache NiFi

NiFi is a JVM (Java)-based open source tool for automating data movement between different systems. NiFi doesn’t require programming to operate; instead, “Dataflows” are created by controlling processors using the included drag-and-drop interface. NiFi includes several hundred pre-made “processors”, which each include a single piece of functionality, from QuerySalesforceObject to DecryptContent.

Letting users control dataflows within the UI makes some tasks more simple, but comes with tradeoffs that can create more complexity. Users have praised NiFi for supporting so many well-made processors, but complain that it’s difficult to do tasks that don’t have an existing processor.

The drag-and-drop approach also makes it difficult to duplicate similar dataflows, meaning users have to manually recreate similar dataflows, rather than copy-pasting them.

NiFi was built for relatively simple cases of moving data between different systems, and users are generally pleased with its performance in this area. But in contexts where monitoring execution, replaying failed tasks, or creating complex workflows is needed, there are more powerful OSS options available.

#5 Argo Workflows

Argo is a Kubernetes-specific workflow engine currently incubating with the Cloud Native Computing Foundation. Workflows are defined in YAML files, and each task runs in its own Kubernetes pod. This architecture allows Argo to massively scale, and run thousands of tasks in parallel.

For teams already deeply invested in Kubernetes, and who relish the idea of designing complex DAGs in YAML, Argo is a solid choice. Because Argo defines resources at the container level, it doesn’t have pre-made operators for integrating with third-party services. Existing third-party containers often fill this gap, but you may need to do extra programming to make them meet your needs.

Argo also has limited support for fault-tolerant workload rescheduling, which can be problematic for mission-critical workloads.

#6 Apache Beam

Apache Beam isn’t a standalone orchestration tool like some of the other projects mentioned here. Instead, it’s an interface layer that sits on top of different backends, such as Apache Spark or Google Dataflow, that execute your pipeline. Beam then provides SDKs in different languages to define your pipeline. The goal is to decouple your pipeline definition from the details of the execution layer, which allows you to run both batch and streaming jobs via the same interface. This design also lets you change backends, or use multiple, without needing to rewrite your pipeline code.

The tradeoff with this flexibility is the inherent complexity of supporting different backends. When the interfaces of underlying backends change, those changes need to be integrated into Beam, which means there can be a delay between release and when new features are available in Beam. If new features aren’t added to Beam, then they won’t be available in your pipelines.

If the complexity isn’t a deterrent, Beam is a great choice if you’re writing pipelines in languages like Go or Java are important, or when it’s valuable to share an interface for batch and streaming jobs.

#7 Apache Oozie

Oozie is a workflow scheduler for managing Hadoop jobs, such as Pig or Hadoop Map/Reduce jobs. Workflows are defined with XML and can be executed or monitored with Hue, a popular UI extension for Oozie. Since Hadoop is written in Java, Oozie was a popular choice for teams heavily invested in the Java and Hadoop ecosystems during the 2010s. At Yahoo!, where Hadoop was developed, Oozie was used to run one million workflows per month in 2016.

Hadoop, and by extension Oozie, have waned in popularity over the past several years, in part due to competition from other tools. Users often criticize Oozie for being difficult to use and for having edge-cases that are hard to debug. Oozie ties users strongly to Hadoop, which is a turnoff for many teams. Compared to other workflow tools that have become popular in the past several years, Oozie has received criticism for its lack of flexibility and limited functionality.

Which data orchestration tool is right for your business?

Exploring these MWAA alternatives will help you understand the tradeoffs made by different data orchestration tools, and whether their strengths fit the needs of your business.

If the right balance for you is the flexibility and scale of Airflow, with the simplicity of a managed service, consider trying Astronomer’s 14-day free trial of Astro, its cloud-native service.

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