10 Best Airflow Alternatives for 2025

Apache Airflow is a widely used workflow orchestration platform that helps manage distributed applications and data pipelines using DAGs (Directed Acyclic Graphs). Over the years, it has evolved from a niche tool to a mission-critical asset, with 67% of companies having more than six people using it. Additionally, 55% of users interact with Airflow daily, and another 26% use it at least once a week. This widespread adoption highlights its significance in modern data workflows.

However, as data environments grow more complex, we’ve seen firsthand how teams struggle with challenges like testing, non-scheduled processes, parameterization, data transfer, and storage abstraction. While nearly half (46%) of users consider Airflow a “very important” tool, concerns about outages and scalability remain top challenges. Data downtime can throttle team productivity, disrupt internal systems, and even impact revenue-generating applications.

To address these challenges, we evaluated multiple Airflow alternatives based on factors such as scalability, ease of use, built-in integrations, and deployment flexibility. In this blog, we’ll share our findings on the top 10 Apache Airflow alternatives to help you find a better fit for your needs. Let’s dive in.

Quick Tabular Comparison of the Airflow Competitors

Here is a quick overview of the Apache Airflow replacement, including the features they possess, so you can make an informed decision.

Tool	Ease of Use	Scalability	Flexibility	Monitoring & Observabillity	Integration & Connectivity	Cost Efficiency
Hevo Data	No-Code, intuitive UI	Real-time auto-scaling	Pre and post-load transformations	Automated Monitoring	150+ pre-built connectors	Managed infrastructure, no cost setup
Luigi	Python based, required coding	Handles batch processing well	Custom workflows possible	Limited UI for monitoring	Good support for Hadoop, Spark	Open-source, no license cost
Apache Nifi	Drag-and-drop UI	Handles streaming data	Highly configurable	Strong data lineage tracking	Supports multiple data formats	Open-source but needs infrastructure setup
AWS Step Functions	Visual workflow designer	Serverless, scales automatically	Limited to AWS ecosystem	Integrated AWS CloudWatch	Deep AWS service integration	Can get expensive with high executions
Prefect	Python-based but simpler than Airflow	Hybrid execution	Works on-prem & cloud	Prefect Cloud for observability	Supports multiple DBs, warehouses	Self-hosted or managed service
Dagster	Developer-friendly, strong debugging	Moderate scalability	Flexible DAG design	Built-in observability, strong type checks	Supports Snowflake, Redshift, Spark	Open-source, managed version available
Kedro	Python-based, code-heavy	Designed for ML pipelines, moderate scalability	Strong for modular and reusable data pipelines	Limited built-in monitoring	Supports multiple data sources and ML frameworks	Open-source, but requires infra setup
Apache Oozie	Complex XML-based configuration	Scales well for Hadoop workloads	Limited flexibility, tightly coupled with Hadoop	Basic job tracking and logging	Primarily integrates with Hadoop ecosystem	Open-source, requires infrastructure setup
Astronomer	Managed Apache Airflow with UI enhancements	Highly scalable, cloud-native	Same flexibility as Apache Airflow	Advanced monitoring via Astronomer Cloud	Deep integration with cloud services	Subscription-based, can be costly for large-scale use
Azure Data Factory	Low-code UI, easy setup	Cloud-native, scales well	Tightly integrated with Azure services	Built-in monitoring via Azure Portal	90+ built-in connectors	Pay-as-you-go pricing, can be costly for large-scale data movement

Top 10 Apache Airflow Alternatives

Below is a comprehensive list of top Airflow competitors that can be used to manage orchestration tasks while providing solutions to overcome the above-listed problems.

1) Hevo Data

Hevo Data is a fully managed, no-code data pipeline platform designed to automate data movement in near real time. Unlike Airflow, which requires extensive Python scripting and complex DAG configurations, Hevo provides an intuitive drag-and-drop interface that simplifies ETL.

With Hevo, users can set up pipelines in just a few clicks, without the need for manual coding or infrastructure management. The platform seamlessly handles schema mapping, data transformations, and real-time data sync, eliminating the operational overhead associated with traditional workflow orchestration tools. Instead of spending time managing dependencies and troubleshooting scripts, teams can focus on deriving insights from their data.

Key Features

• Set Up in Just a Few Clicks: No programming or scripting required. Connect data sources and warehouses easily with an intuitive UI.
• Zero Maintenance, Fully Automated: Handles schema drifts automatically, intelligently recovers record failures, and sends proactive alerts—eliminating manual intervention.
• Complete Pipeline Visibility: Get real-time monitoring and granular insights into system-level operations with detailed logs.
• High-Throughput Data Replication: Move billions of records with minimal latency, even during data spikes.
• Top Grade Security & Compliance: Features end-to-end encryption, two-factor authentication, and role-based access control for secure data movement.
• Advanced Pipeline Management: Automate pipeline operations with APIs, CI/CD integration, and workflow orchestration.

Pros

• Intelligent Failure Handling: Maintains data consistency between source and destination with automated recovery mechanisms.
• Proactive Monitoring & Alerts: Real-time notifications for job failures, latency issues, and schema changes enable quick resolution.
• Granular Job Execution Insights: Provides deep visibility into pipeline performance, including job duration, latency, and object progress tracking.
• Seamless Troubleshooting: Access and download session logs for individual jobs to diagnose and resolve issues efficiently.
• Optimized Data Loading: Prevents redundant loads, reducing warehouse costs and improving overall efficiency.

Cons

• Limited Workflow Customization: No DAG-based orchestration like Airflow.
• Cloud-Only Deployment: Lacks on-premise hosting options.

Pricing

Hevo offers 4 pricing plans:

• Free: Includes 1 million events per month for up to 5 users at no cost.
• Starter: Starts at $299/month for 5 million events, with SSH/SSL security and dbt integration.
• Professional: Starts at $849/month for 20 million events, with unlimited users and advanced features.
• Business Critical: Custom pricing tailored to your specific requirements. 

What Do People Think About Hevo Data?

Users on G2 think highly of Hevo Data, and here’s what they have to say:

• Hevo – Blazing Fast and Low Cost ETL platform – “I really like the elegant UI of hevo, drag and drop transformation feature as well as code transformation as well. The errors that hevo provide at event level, load time and ingestion time metrics are good and sometimes it helps to debug the issue.

Also I really like the auto schema inference in sources like MongoDB and the great feature, `auto json columns flattening` which updates the target schema automatically and as a user we don’t need to do manual schema mapping. ”

• A quick and easy way to do difficult data transfers – “Hevo made it super simple to move data between platforms. We looked at other solutions that would have had higher time and or cost issues over Hevo. It only took a couple hours to get everything up and running. Good documentation and reactive support helped a ton.”

Final Verdict

Hevo Data is a good alternative to Apache, which is ideal for data analysts, BI teams, and fast-growing companies needing a fully managed, no-code ETL solution with real-time monitoring and automation. It’s perfect for teams that want to move data effortlessly without managing scripts or infrastructure.

2) Astronomer

Astronomer isn’t an alternative to Apache Airflow, it’s a way to make Airflow even better. If you love Airflow but don’t want to deal with the complexities of managing and scaling it, Astronomer takes care of the operational side for you. It provides a fully managed, cloud-native experience so you can focus on building and orchestrating workflows without worrying about infrastructure.

What we really liked is how seamlessly it integrates with existing Airflow setups, adding enterprise-level features like monitoring, security, and automated scaling. 

Key Features

• Managed Apache Airflow: Deploy and manage Airflow instances without worrying about infrastructure, scaling, or maintenance.
• Dynamic Scaling: Automatically scale your resources up or down based on workload demands, ensuring cost efficiency and performance.
• Real-Time Monitoring & Logging: Get detailed insights into your DAG executions with centralized logging, alerting, and performance metrics.
• Multi-Cloud Deployment: Run Airflow on AWS, Azure, GCP, or Kubernetes, giving you the flexibility to choose your preferred cloud provider.
• Enterprise Security & Compliance: Built-in role-based access control (RBAC), single sign-on (SSO), and audit logs to ensure security at every level.
• CLI & API for Automation: Easily interact with and automate Airflow environments using Astronomer’s CLI and API, improving developer efficiency.
• Continuous Integration & Deployment: Supports CI/CD pipelines, making it easier to test, deploy, and iterate on your DAGs.

Pros

• Fully Managed Airflow: No need to worry about Airflow setup, upgrades, or scaling; Astronomer takes care of everything.
• Scalability & Performance: Dynamically scales Airflow environments based on workload, preventing resource overuse or bottlenecks.
• Enhanced Observability: Advanced monitoring and logging features help troubleshoot failures quickly.
• Enterprise-Ready: Ideal for companies that require security, compliance, and high availability.
• Seamless Multi-Cloud Deployment: Deploy across different cloud providers effortlessly.
• Developer-Friendly: CLI and API tools streamline workflow management and automation.
• Reliable Support & SLAs: Dedicated support ensures uptime and minimizes operational risk.

Cons

• Pricing: Unlike open-source Airflow, Astronomer comes with a cost, making it less suitable for small teams or startups with limited budgets.
• Less Control Over Infrastructure: Since Astronomer manages Airflow for you, you rely on their infrastructure, which may limit deep customization.
• Learning Curve for Beginners: If you’re new to Airflow, Astronomer still requires Airflow knowledge, meaning you must understand DAG creation, dependencies, and scheduling.
• May Be Overkill for Small Workflows: If your workflows are simple and infrequent, self-hosted Airflow might be a more cost-effective solution.

Pricing

Astronomer follows a usage-based pricing model, which depends on the number of Airflow deployments and the compute resources used. Pricing details are not publicly available, so you’ll need to contact Astronomer’s sales team for a quote. They offer plans suitable for small teams to large enterprises, with options for SaaS or self-managed deployments.

What Do People Think About Astronomer?

Astronomer is one of the most celebrated Airflow alternatives on G2. Here’s what people are saying: 

• Great option for easy Airflow – “Easy to implement and maintain. Setup is pretty quick and the painful parts of managing Airflow are removed. Deploying and upgrading are simple. Also very easy to get a local environment up and running, which is a big win. The Docker based deployments make it easy to test out version upgrading locally before deploying. Astro CLI is great for implementing a CI/CD process.”
• Astronomer Powered Airflow – “Ease of setting up and launching Airflow in production. Suits well for teams which have limited data engineering capacity and allows to focus more on the business logic for the pipelines.”

Final Verdict

If you need a fully managed Apache Airflow solution that is scalable, secure, and enterprise-ready, Astronomer is an excellent choice. It eliminates the operational overhead of running Airflow, allowing you to focus on building and optimizing your workflows. However, if you are a small team or have simple workflow needs, the cost of Astronomer may not justify the benefits, and a self-hosted Airflow setup could be more economical.

3) Apache NiFi

Apache NiFi is an excellent tool for managing and automating data flows between systems. In our experience, its drag-and-drop interface makes building and tweaking data pipelines surprisingly easy, with no need to write endless lines of code. Nifi supports both real-time streaming data and batch processing. It keeps everything flowing smoothly across databases, cloud services, and applications.

What really stood out to us is how effortlessly NiFi handles data ingestion, transformation, and routing with its wide range of built-in processors. Plus, its security controls and data tracking features give full visibility into where your data is going and how it’s being processed. 

Key Features

• Visual Data Flow Management: You get a user-friendly, drag-and-drop interface to design and monitor workflows.
• Built-in Processors: Out-of-the-box connectors for databases, cloud storage, messaging queues, and APIs make integration seamless.
• Flexible Routing & Transformation: You can split, merge, enrich, or filter data dynamically.
• Data Provenance & Lineage: Every action is tracked, so you have full visibility into how data flows through your system.
• Scalability: Run it on a single node or scale it across a cluster for high availability and performance.
• Security & Access Control: Role-based access and encrypted data transfer keep your pipelines secure.

Pros

• No-Code/Low-Code: You don’t need to write scripts to build powerful data pipelines.
• Real-Time & Batch Processing: Handle both streaming and batch workflows effortlessly.
• Auto-Retry & Fault Tolerance: Failed processes can be retried without losing data.
• Wide Integration Support: Connects with databases, cloud services (AWS, Azure, GCP), Kafka, MQTT, and more.
• Open-Source & Free: No licensing fees make it a cost-effective choice.

Cons

• Steeper Learning Curve: The UI is easy to use, but mastering complex flows and configurations takes time.
• Memory Intensive: Large-scale data flows can be resource heavy.
• Limited Advanced Transformations: You may still need additional tools (e.g., Apache Spark, dbt) for complex data transformations.

Pricing

Apache NiFi is completely free and open-source under the Apache License 2.0. However, if you need managed services, vendors like Cloudera offer enterprise support and additional features for a cost.

What Do People Think About Apache Nifi?

Here’s what some users say about Apache Nifi on G2:

• The perfect all-rounder versatile ETL tool – “The best thing about Nifi is that the tools bar is located at convenient place for the user to acces the tools. The drag and drop feature comes handy. The grid offers a perfect measure of components. DAG is represented properly by connecting arrows.”

Final Verdict

Choose Apache NiFi if you need an easy-to-use, drag-and-drop interface for moving and transforming data in real-time. It’s great for streaming and batch workflows, quick integrations, and ensuring data traceability. If your focus is data ingestion and movement with minimal coding, NiFi is a strong choice.

4) AWS Step Functions

AWS Step Functions makes orchestrating workflows seamless, especially when working with AWS services. From our experience, its serverless, visual interface simplifies connecting services like Lambda, DynamoDB, and S3 without requiring us to manage any infrastructure. Whether it’s coordinating microservices, automating ETL pipelines, or handling event-driven workflows, Step Functions ensures everything runs in the right order, with built-in error handling and automatic retries.

One of the biggest advantages we’ve seen is its ability to break down complex workflows into manageable state transitions. The visual workflow designer makes it easy to define the flow of tasks, and its deep integration with AWS services means we can trigger functions, run parallel executions, and even integrate human approval steps when needed. Compared to Apache alternatives AWS offers, Step Functions stands out for its tight cloud integration and seamless scalability.

Key Features

• Visual Workflow Builder: Design workflows using a drag-and-drop UI.
• Built-in Error Handling: Automatic retries and fallback mechanisms.
• Seamless AWS Integration: Connects natively with AWS services.
• Parallel Execution: Run multiple tasks simultaneously.
• State Management: Keeps track of execution states and transitions.

Pros

• Fully managed: No need to maintain servers.
• Highly scalable: Handles thousands of executions efficiently.
• Pay-as-you-go pricing: Cost-effective for event-driven workloads.
• Deep AWS integration: Works effortlessly with AWS Lambda, S3, and other AWS tools.

Cons

• AWS lock-in: Limited use outside the AWS ecosystem.
• Pricing complexity: Costs can add up for high-frequency workflows.
• Limited flexibility: Not ideal for complex, long-running workflows.

Pricing

AWS Step Functions follows a pay-per-use model, charging based on state transitions in your workflow. The standard workflow costs $0.025 per 1,000 state transitions, while Express workflows are significantly cheaper but suited for high-volume, short-lived executions.

What do people think about AWS Step Functions?

Users on G2 praise AWS Step Functions. Here’s what they are saying:

• AWS Step Function Review – “I liked how AWS Step Functions have a detailed execution flow for each of my other AWS resources., also I liked the feature of how without the help of lambda I can quickly call Dynamodb and do the operation, this helps in reducing resources and saving some bucks.”

Final Verdict

If you’re deep into AWS and need a serverless orchestration tool, Step Functions is a great pick. It simplifies workflow automation, especially for microservices and event-driven applications. However, if you need cross-cloud orchestration or more control over scheduling, tools like Airflow or Prefect might be a better fit.

5) Prefect

Prefect is a modern, Python-native workflow orchestration tool that simplifies the way you build, monitor, and scale data pipelines. Unlike Apache Airflow, which requires predefined DAGs, Prefect allows you to write workflows as pure Python code, making it more dynamic and flexible. 

Whether you’re handling ETL processes, machine learning pipelines, or automated data tasks, Prefect helps you manage dependencies, track execution, and recover from failures effortlessly. It also offers a hybrid execution model, letting you keep your data and workflows secure while still benefiting from cloud-based monitoring and orchestration.

Key Features

• Python-First Approach: Write workflows in Python without needing predefined DAGs.
• Dynamic Workflows: Tasks are defined at runtime, allowing for more flexibility.
• Hybrid Execution: Run workflows anywhere (local, cloud, Kubernetes) while keeping data private.
• Automatic Retries & Error Handling: Failed tasks can be retried automatically.
• Flow Visualization & Logging: Get detailed insights into your workflow execution.
• Seamless Integrations: Works with AWS, GCP, dbt, Snowflake, and Kubernetes.
• Event-Driven Triggers: Schedule workflows based on time, conditions, or external events.

Pros

• Easy to Use & Code-Friendly: Define workflows with simple Python functions, no YAML or rigid DAGs required.
• Scalable & Cloud-Native: Can handle small workflows or large-scale enterprise pipelines.
• Better Error Handling: Automatic retries, failure alerts, and a robust UI for monitoring.
• Flexible Deployment: Run locally, on-premise, or in the cloud without vendor lock-in.
• Secure Execution: Your data stays on your infrastructure while using Prefect Cloud for orchestration.

Cons

• Smaller Community: Prefect is newer, so it lacks the extensive ecosystem of Airflow.
• Cloud Features Cost Extra: Prefect Cloud adds advanced monitoring and orchestration but comes with a price.
• Learning Curve for Beginners: While easier than Airflow, understanding workflows as Python functions can still take time.

Pricing

Prefect has a free, open-source version for self-hosted workflows, making it one of the best alternatives to Airflow, if you want full control. Prefect Cloud, which provides hosted orchestration, monitoring, and advanced enterprise features, follows a usage-based pricing model. Costs depend on execution time, API calls, and resource consumption, making it scalable for teams of all sizes.

What Do People Think About Prefect?

Here’s what users are saying about Prefect on G2:

• Best Workflow Experience I Have Found – “The developer experience is seamless. Getting started is so easy and the flexibility of the framework goes so far beyond anything else I have used. And the icing on the cake is that the community and the developers supporting it are so helpful for anything you are trying to accomplish.”
• Great product come with great support – “First of all, the speed of delivery by the Prefect team is simply amazing. They are always looking into improving the development and deployment aspects of the product. There is strong support through the Slack channel. I do like the rubber duck session to talk to the Prefect team members face-to-face.”

Final Verdict

If you’re looking for a modern, Python-friendly alternative to Airflow that offers dynamic workflows, better error handling, and a simpler user experience, Prefect is an excellent choice. It’s especially useful for data engineers, ML practitioners, and automation workflows that need flexibility. However, if you need a mature, well-established orchestration tool with a larger community, Airflow might still be the better option.

6) Dagster

Dagster is a modern data orchestrator designed to help you build, test, and monitor data pipelines with ease. Unlike traditional workflow tools like Apache Airflow, Dagster is built with a software engineering mindset, allowing you to treat data pipelines as code-first, testable, and modular systems. 

With its declarative approach, strong type-checking, and built-in development tools, Dagster makes it easy to create robust, scalable, and maintainable workflows. If you’re working on ETL jobs, machine learning pipelines, or data validation, Dagster ensures reliability at every step.

Key Features

• Declarative, Code-First Pipelines: Define workflows using Python while leveraging modern software engineering principles.
• Built-in Type Checking & Testing: Validate data inputs and outputs at every stage.
• Software-Defined Assets (SDAs): Treat data as a product with version control and lineage tracking.
• Powerful Orchestration: Schedule, monitor, and retry tasks automatically.
• Dynamic Execution: Run jobs on different environments (local, Kubernetes, or cloud).
• Graph-Based UI: Get real-time insights into your data pipeline’s execution and dependencies.
• Seamless Integrations: Connect with Snowflake, dbt, Spark, Pandas, and Airbyte.

Pros

• Strong Data Validation: Built-in type safety and testing prevent bad data from breaking your pipeline.
• Better Debugging & Monitoring: The UI makes it easy to track failures and dependencies.
• Code-Driven Pipelines: Encourages best practices like modularity, versioning, and testing.
• Flexible Deployment: Works on-premise, in the cloud, or hybrid environments.
• First-Class Support for Data Assets: Unlike traditional DAGs, Dagster treats data as a core component of the workflow.

Cons

• Steeper Learning Curve: Requires a shift in mindset if you’re used to traditional DAG-based workflow tools.
• Smaller Community: Compared to Airflow, fewer resources and third-party integrations are available.
• Overhead for Simple Workflows: If you just need basic task scheduling, Dagster may feel like overkill.

Pricing

Dagster is one of the best alternatives to Apache Airflow, since it offers a free, open-source version that provides full orchestration capabilities. For teams looking for managed infrastructure, enterprise security, and advanced collaboration features, Dagster Cloud follows a usage-based pricing model, scaling with the complexity and execution of your workflows.

Final Verdict

If you’re looking for a modern alternative to Airflow with better data validation, observability, and a software engineering approach to workflows, Dagster is a great choice. It’s particularly useful if you work with data assets, need strong lineage tracking, or want a testable, modular pipeline setup. However, if you prefer a simpler, more established tool with a larger ecosystem, Airflow might be a better fit.

7) Kedro

Kedro is an open-source Python framework designed to help you build modular, reusable, and scalable data pipelines. If you’re working on machine learning (ML) or data engineering projects, Kedro brings best practices from software engineering, such as version control, modularity, and testability, to your workflow. It’s especially useful when you want to move from Jupyter notebooks to production-ready pipelines, ensuring your code is clean, maintainable, and collaborative.

Key Features

• Modular Pipelines: Break down complex data workflows into reusable, independent components.
• Configuration Management: Store and manage parameters separately from code for easy reproducibility.
• Data Catalog: Simplifies data input and output management, keeping track of all datasets in a structured way.
• Pipeline Visualization: Generate graph-based representations of workflows for better understanding.
• Seamless Deployment: Works with Docker, Kubernetes, Airflow, MLflow, and cloud services.
• Version Control & Experiment Tracking: Integrates with Git, DVC, and MLflow to manage ML models effectively.

Pros

• Promotes Best Practices: Encourages software engineering standards like modularity, testing, and version control.
• Great for ML & Data Engineering: Built specifically for ML pipelines and production workflows.
• Reproducibility & Scalability: Configuration-driven approach ensures consistent results.
• Visual Pipeline Representation: Easily understand your pipeline structure with Kedro-Viz.
• Strong Community & Support: Backed by McKinsey & Company, with growing adoption in the ML community.

Cons

• Learning Curve: Requires a mindset shift from using ad-hoc Jupyter notebooks.
• Overhead for Simple Projects: If you only need a basic pipeline, Kedro might feel too structured.
• Not a Full Orchestration Tool: You still need Airflow or Prefect for scheduling and execution.

Pricing

Kedro is completely open-source and free, making it an excellent choice for teams looking to improve their ML workflows without additional costs.

Final Verdict

If you’re working on machine learning projects and need a structured, maintainable, and scalable pipeline, Kedro is a fantastic choice. It helps bring discipline to your workflow, making it easier to collaborate, deploy, and manage ML models. However, if you’re looking for a full-fledged workflow orchestrator, you’ll need to pair Kedro with a tool like Airflow for scheduling and execution.

8) Luigi

Luigi is an open-source Python library developed by Spotify to handle the complexities of long-running batch processes. It is a more basic tool than some others, but is a viable option for those searching for simpler airflow alternatives python based. When we tested it, we found that it works well for automating and managing workflows involving Hadoop jobs, database operations, and machine learning tasks. Instead of replacing tools like Hive or Spark, Luigi acts as the glue that connects them, ensuring tasks run in the right order and dependencies are properly managed.

One thing we liked about Luigi is its strong dependency tracking—it ensures each step in a workflow runs only when the previous steps are successfully completed.

Key Features

• Failure Handling & Retries: You don’t have to worry about broken workflows, Luigi detects failures and retries tasks automatically.
• Command Line Control: You can run, monitor, and manage workflows directly from the CLI.
• Built-in Visualizer: You get a real-time graphical view of task dependencies and progress.
• Seamless Integration: You can connect Luigi with Hadoop, Spark, Python scripts, SQL queries, and other data tools.
• Atomic File Operations: You don’t risk partial or corrupted data since file system operations are atomic.

Pros 

• Clear Dependency Management: Luigi makes it easy to define and track task dependencies, ensuring jobs execute in the right order.
• Python-Friendly: If you’re already using Python, scripting workflows in Luigi feels natural and flexible.
• Lightweight & Easy to Set Up: Unlike Airflow, there’s no need to manage a complex distributed system—it runs smoothly on a single machine.
• Batch Processing: If your use case is batch-oriented, Luigi gets the job done without unnecessary complexity.

Cons

• No Real-Time Processing: If you need streaming or event-driven workflows, Luigi won’t be the right fit.
• Basic UI & Monitoring: While it has a web dashboard, it lacks the advanced monitoring capabilities of Airflow.
• Not Ideal for Cloud-Native Setups: Luigi works best in on-premise environments rather than modern cloud workflows.

Pricing

Luigi is completely free & open source. You don’t require any licenses or paywalls. Install it with pip install luigi, and you’re good to go. 

Final Verdict

If you’re deep into batch processing and want a lightweight yet powerful workflow manager, Luigi is a fantastic choice. It’s developer-friendly, integrates smoothly with Python-based data stacks, and handles dependencies easily. Just don’t expect it to handle real-time streaming or complex workflow orchestration like Airflow.

9) Azure Data Factory

Azure Data Factory (ADF) is Microsoft’s cloud-based ETL (Extract, Transform, Load) and data integration service that helps you build, automate, and manage complex data workflows across multiple sources. Whether you’re moving data between databases, transforming it for analytics, or orchestrating machine learning pipelines, ADF provides a scalable and serverless environment to handle all your data movement and transformation needs. It seamlessly integrates with Azure services, third-party platforms, and on-premises systems, making it a powerful solution for enterprises working in hybrid and multi-cloud environments.

Key Features

• Code-Free & Code-Based Pipelines: Use drag-and-drop UI for easy workflow creation or opt for custom Python, .NET, and SQL scripts for more control.
• Data Flow & Transformation: Perform data cleansing, enrichment, and transformation using built-in Data Flow capabilities powered by Apache Spark.
• Over 90+ Built-in Connectors: Easily integrate with Azure SQL, Snowflake, AWS S3, Google BigQuery, SAP, and more.
• Hybrid & On-Premises Data Support: Use self-hosted integration runtimes to connect on-premises databases and cloud environments.
• Orchestration & Monitoring: Automate complex workflows with triggers, scheduling, and event-driven orchestration, and monitor them in real time.
• Azure Synapse & AI/ML Integration: Connect seamlessly with Azure Synapse Analytics, Databricks, and AI models to enhance data insights.
• Scalability & Serverless Execution: Automatically scale based on workload demand without managing infrastructure.
• Security & Compliance: Enterprise-grade security with RBAC (Role-Based Access Control), managed identity authentication, and encryption.

Pros

• Seamless Azure Integration: Works natively with Azure services like Synapse, Databricks, Blob Storage, and Logic Apps for a complete data pipeline solution.
• Hybrid & Multi-Cloud Support: Easily connect on-premises databases, AWS, GCP, and third-party apps, making it flexible for hybrid environments.
• No-Code & Code-Based Flexibility: Drag-and-drop interface for quick workflows, plus custom scripting options for advanced transformations.
• Scalability & Performance: Serverless execution and dynamic scaling ensure cost efficiency and high performance.
• Enterprise Security & Compliance: Supports VNet, private endpoints, encryption, and RBAC to meet compliance needs.
• Cost-Effective for Large Workloads: Since you only pay for data movement and execution, it’s affordable compared to traditional ETL tools. 

Cons

• Learning Curve: While the UI is user-friendly, complex data flow transformations require Spark knowledge and troubleshooting can be tricky.
• Limited Debugging Capabilities: Error messages can be vague, making troubleshooting difficult, especially for complex workflows.
• Azure-Centric: While it supports multi-cloud, it is best suited for Azure environments, and AWS/GCP users may find better alternatives like Glue or Dataflow.
• Slow Pipeline Execution for Small Workloads: Due to batch processing nature, smaller jobs may experience unnecessary delays.
• Costs Can Add Up: While it’s serverless, high-volume data movement, transformations, and monitoring can lead to unpredictable costs.

Pricing

Azure Data Factory follows a pay-as-you-go pricing model, which includes:

• Pipeline Execution: Charged per activity run (starting at $0.00025 per activity)
• Data Flow Execution: Charged based on vCore-hour usage (starting at $0.193 per vCore-hour)
• Data Movement & Connectivity: Charged per integration runtime and data transfer volume
• Monitoring & Debugging: Additional charges for logging, debugging, and execution metrics

Pricing can vary significantly based on workload size, so it’s best to use Azure’s pricing calculator for a more accurate estimate.

What Do People Think About Azure Data Factory?

Users on G2 laud ADF and say the following: 

• Best & Easiest way to develop data pipelines – The easiness and the UI is the best among all other of it’s competition. The UI is very easy and you create data pipeline in a a few click of buttons. The workflow allows you to perform data transformation which is again a drag-drop feature which allows new users to easily use it.

Final Verdict

If you are deeply invested in the Azure ecosystem and need a scalable, hybrid, and enterprise-ready data integration solution, Azure Data Factory is a solid choice. It works exceptionally well for orchestrating complex ETL pipelines, managing large-scale data movements, and integrating with Azure services. However, if you work primarily with AWS, GCP, or smaller workloads, you might find alternatives like AWS Glue or Google Dataflow more cost-effective and easier to debug.

10) Apache Oozie

Note: Apache Oozie moved into the Attic in February 2025.

Apache Oozie is a workflow scheduler for Hadoop, designed to help you orchestrate and manage complex data processing jobs. If you’re working with big data in a Hadoop ecosystem, Oozie lets you define, schedule, and monitor workflows consisting of multiple tasks like MapReduce, Pig, Hive, Spark, and shell scripts. It’s tightly integrated with Hadoop, making it a reliable choice for handling dependencies, retries, and time-based scheduling within your data pipelines.

Key Features

• Workflow Scheduling: Define and schedule multi-step workflows using XML-based configurations.
• Job Coordination: Supports time-based and data-triggered job execution.
• Native Hadoop Integration: Works seamlessly with HDFS, YARN, and MapReduce.
• Error Handling & Retry Mechanism: Automatically retries failed tasks to ensure reliability.
• Security & Authentication: Supports Kerberos authentication for enterprise security.
• REST API & CLI Support: Manage workflows programmatically or via command line.

Pros

• Built for Hadoop: Ideal if you’re already using Hadoop, HDFS, and YARN.
• Handles Dependencies Well: Ensures tasks execute in the correct order.
• Scalable & Reliable: Works efficiently for large-scale data processing.
• Automated Retries: Reduces manual intervention for failed jobs.
• Free & Open-Source: No licensing costs make it budget-friendly.

Cons

• Complex XML Configuration: Defining workflows can be tedious and less intuitive compared to modern tools.
• Limited Support for Non-Hadoop Jobs: Works best only within Hadoop ecosystems.
• Not as User-Friendly: Lacks a visual interface for easy workflow management.
• Better Alternatives Exist: Tools like Apache Airflow and Prefect offer more flexibility and modern UI.

Pricing

Apache Oozie is completely free and open-source, making it a cost-effective solution if you’re working within a Hadoop-based big data environment.

Final Verdict

If you’re deeply invested in Hadoop and need a reliable job scheduler, Apache Oozie is worth considering. However, if you’re looking for a more flexible, modern, and user-friendly orchestration tool, alternatives like Airflow, Prefect, or Dagster may be better suited for your needs. Go with Oozie only if your workflows are Hadoop-centric and you’re comfortable working with XML-based configurations.

Key Factors for Choosing an Apache Airflow Alternative

When selecting a better Apache Airflow competitor, you need to consider several key factors based on your use case, team expertise, and infrastructure. Here are the most important aspects to evaluate:

• Scalability: If your workloads are growing fast, you need a tool that can handle distributed execution without breaking. Some alternatives struggle with this at scale, so check how they handle large workflows.
• Ease of Use: Airflow has a steep learning curve, so a good alternative should offer a cleaner UI, simple configurations, and better debugging tools. A smoother developer experience saves hours of frustration.
• Integration with Existing Tools: Integration with Existing Tools: You don’t want to spend weeks building custom connectors. The best tools integrate seamlessly with cloud platforms, databases, and third-party services right out of the box. For those specifically looking for airflow alternatives GCP, ensure deep integration with Google Cloud services.
• Flexibility & Extensibility: If you need custom plugins or complex workflows (like branching logic), some tools make this easy, while others feel rigid. Make sure your alternative can adapt to your needs.
• Scheduling & Monitoring: Built-in schedulers, automated retries, and real-time alerts are a must. Some tools provide native monitoring, while others require third-party solutions—know what you’re getting into.
• Community & Support: A strong open-source community or dedicated support can be a lifesaver when things go wrong. If you rely on quick fixes and feature updates, this factor is non-negotiable.
• Cost & TCO: Pricing varies widely. Managed services may seem convenient, but costs can spiral quickly. Understand infrastructure, licensing, and operational costs before committing.
• Data Provenance & Lineage: If compliance or governance matters to you, visibility into how data moves through your pipelines is crucial. Some tools offer built-in lineage tracking, while others need extra effort.
• Latency & Real-Time Processing: If you’re dealing with streaming data, make sure the alternative supports low-latency, event-driven execution. Some tools are batch-oriented and won’t work well for real-time processing.
• Reliability & Fault Tolerance: No one wants to wake up to failed workflows. Automatic retries, failover mechanisms, and disaster recovery capabilities should be top priorities.

Conclusion

The right Apache Airflow alternative depends on your scalability, ease of use, and integration needs. If you need flexibility, tools like Dagster or Prefect work well, while AWS Step Functions and Azure Data Factory suit cloud-native workflows.

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Frequently Asked Questions