Finest practices for migrating from Apache Airflow 2.x to Apache Airflow 3.x on Amazon MWAA

October 7, 2025

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Apache Airflow 3.x on Amazon MWAA introduces architectural enhancements reminiscent of API-based process execution that gives enhanced safety and isolation. Different main updates embrace a redesigned UI for higher person expertise, scheduler-based backfills for improved efficiency, and assist for Python 3.12. In contrast to in-place minor Airflow model upgrades in Amazon MWAA, upgrading to Airflow 3 from Airflow 2 requires cautious planning and execution by means of a migration method because of basic breaking adjustments.

This migration presents a possibility to embrace next-generation workflow orchestration capabilities whereas offering enterprise continuity. Nevertheless, it’s greater than a easy improve. Organizations migrating to Airflow 3.x on Amazon MWAA should perceive key breaking adjustments, together with the removing of direct metadata database entry from staff, deprecation of SubDAGs, adjustments to default scheduling habits, and library dependency updates. This put up offers finest practices and a streamlined method to efficiently navigate this crucial migration, offering minimal disruption to your mission-critical knowledge pipelines whereas maximizing the improved capabilities of Airflow 3.

Understanding the migration course of

The journey from Airflow 2.x to three.x on Amazon MWAA introduces a number of basic adjustments that organizations should perceive earlier than starting their migration. These adjustments have an effect on core workflow operations and require cautious planning to attain a easy transition.

You need to be conscious of the next breaking adjustments:

Removing of direct database entry – A crucial change in Airflow 3 is the removing of direct metadata database entry from employee nodes. Duties and customized operators should now talk by means of the REST API as an alternative of direct database connections. This architectural change impacts code that beforehand accessed the metadata database straight by means of SQLAlchemy connections, requiring refactoring of current DAGs and customized operators.
SubDAG deprecation – Airflow 3 removes the SubDAG assemble in favor of TaskGroups, Property, and Information Conscious Scheduling. Organizations should refactor current SubDAGs to one of many beforehand talked about constructs.
Scheduling habits adjustments – Two notable adjustments to default scheduling choices require an affect evaluation:
- The default values for catchup_by_default and create_cron_data_intervals modified to False. This modification impacts DAGs that don’t explicitly set these choices.
- Airflow 3 removes a number of context variables, reminiscent of execution_date, tomorrow_ds, yesterday_ds, prev_ds, and next_ds. You could exchange these variables with at the moment supported context variables.
Library and dependency adjustments – A big variety of libraries change in Airflow 3.x, requiring DAG code refactoring. Many beforehand included supplier packages may want specific addition to the necessities.txt file.
REST API adjustments – The REST API path adjustments from /api/v1 to /api/v2, affecting exterior integrations. For extra details about utilizing the Airflow REST API, see Creating an online server session token and calling the Apache Airflow REST API.
Authentication system – Though Airflow 3.0.1 and later variations default to SimpleAuthManager as an alternative of Flask-AppBuilder, Amazon MWAA will proceed utilizing Flask-AppBuilder for Airflow 3.x. This implies clients on Amazon MWAA won’t see any authentication adjustments.

The migration requires creating a brand new atmosphere reasonably than performing an in-place improve. Though this method calls for extra planning and assets, it offers the benefit of sustaining your current atmosphere as a fallback possibility throughout the transition, facilitating enterprise continuity all through the migration course of.

Pre-migration planning and evaluation

Profitable migration relies on thorough planning and evaluation of your present atmosphere. This section establishes the inspiration for a easy transition by figuring out dependencies, configurations, and potential compatibility points. Consider your atmosphere and code in opposition to the beforehand talked about breaking adjustments to have a profitable migration.

Surroundings evaluation

Start by conducting an entire stock of your present Amazon MWAA atmosphere. Doc all DAGs, customized operators, plugins, and dependencies, together with their particular variations and configurations. Be certain that your present atmosphere is on model 2.10.x, as a result of this offers the very best compatibility path for upgrading to Amazon MWAA with Airflow 3.x.

Determine the construction of the Amazon Easy Storage Service (Amazon S3) bucket containing your DAG code, necessities file, startup script, and plugins. You’ll replicate this construction in a brand new bucket for the brand new atmosphere. Creating separate buckets for every atmosphere avoids conflicts and permits continued improvement with out affecting present pipelines.

Configuration documentation

Doc all customized Amazon MWAA atmosphere variables, Airflow connections, and atmosphere configurations. Assessment AWS Id and Entry Administration (IAM) assets, as a result of your new atmosphere’s execution function will want similar insurance policies. IAM customers or roles accessing the Airflow UI require the CreateWebLoginToken permission for the brand new atmosphere.

Pipeline dependencies

Understanding pipeline dependencies is crucial for a profitable phased migration. Determine interdependencies by means of Datasets (now Property), SubDAGs, TriggerDagRun operators, or exterior API interactions. Develop your migration plan round these dependencies so associated DAGs can migrate on the similar time.

Take into account DAG scheduling frequency when planning migration waves. DAGs with longer intervals between runs present bigger migration home windows and decrease danger of duplicate execution in contrast with ceaselessly working DAGs.

Testing technique

Create your testing technique by defining a scientific method to figuring out compatibility points. Use the ruff linter with the AIR30 ruleset to robotically determine code requiring updates:

ruff verify --preview --select AIR30

Then, evaluate and replace your atmosphere’s necessities.txt file to ensure bundle variations adjust to the up to date constraints file. Moreover, generally used Operators beforehand included within the airflow-core bundle now reside in a separate bundle and must be added to your necessities file.

Check your DAGs utilizing the Amazon MWAA Docker photographs for Airflow 3.x. These photographs make it potential to create and take a look at your necessities file, and ensure the Scheduler efficiently parses your DAGs.

Migration technique and finest practices

A methodical migration method minimizes danger whereas offering clear validation checkpoints. The beneficial technique employs a phased blue/inexperienced deployment mannequin that gives dependable migrations and instant rollback capabilities.

Phased migration method

The next migration phases can help you in defining your migration plan:

Part 1: Discovery, evaluation, and planning – On this section, full your atmosphere stock, dependency mapping, and breaking change evaluation. With the gathered data, develop the detailed migration plan. This plan will embrace steps for updating code, updating your necessities file, making a take a look at atmosphere, testing, creating the blue/inexperienced atmosphere (mentioned later on this put up), and the migration steps. Planning should additionally embrace the coaching, monitoring technique, rollback situations, and the rollback plan.
Part 2: Pilot migration – The pilot migration section serves to validate your detailed migration plan in a managed atmosphere with a small vary of affect. Focus the pilot on two or three non-critical DAGs with numerous traits, reminiscent of completely different schedules and dependencies. Migrate the chosen DAGs utilizing the migration plan outlined within the earlier section. Use this section to validate your plan and monitoring instruments, and regulate each based mostly on precise outcomes. Through the pilot, set up baseline migration metrics to assist predict the efficiency of the total migration.
Part 3: Wave-based manufacturing migration – After a profitable pilot, you might be prepared to start the total wave-based migration for the remaining DAGs. Group remaining DAGs into logical waves based mostly on enterprise criticality (least crucial first), technical complexity, interdependencies (migrate dependent DAGs collectively), and scheduling frequency (much less frequent DAGs present bigger migration home windows). After you outline the waves, work with stakeholders to develop the wave schedule. Embody ample validation intervals between waves to verify the wave is profitable earlier than beginning the subsequent wave. This time additionally reduces the vary of affect within the occasion of a migration difficulty, and offers ample time to carry out a rollback.
Part 4: Publish-migration evaluate and decommissioning – In any case waves are full, conduct a post-migration evaluate to determine classes realized, optimization alternatives, and another unresolved gadgets. That is additionally a very good time to supply an approval on system stability. The ultimate step is decommissioning the unique Airflow 2.x atmosphere. After stability is set, based mostly on enterprise necessities and enter, decommission the unique (blue) atmosphere.

Blue/inexperienced deployment technique

Implement a blue/inexperienced deployment technique for secure, reversible migration. With this technique, you’ll have two Amazon MWAA environments working throughout the migration and handle which DAGs function through which atmosphere.

The blue atmosphere (present Airflow 2.x) maintains manufacturing workloads throughout transition. You’ll be able to implement a freeze window for DAG adjustments earlier than migration to keep away from last-minute code conflicts. This atmosphere serves because the instant rollback atmosphere if a problem is recognized within the new (inexperienced) atmosphere.

The inexperienced atmosphere (new Airflow 3.x) receives migrated DAGs in managed waves. It mirrors the networking, IAM roles, and safety configurations from the blue atmosphere. Configure this atmosphere with the identical choices because the blue atmosphere, and create similar monitoring mechanisms so each environments could be monitored concurrently. To keep away from duplicate DAG runs, be certain a DAG solely runs in a single atmosphere. This includes pausing the DAG within the blue atmosphere earlier than activating the DAG within the inexperienced atmosphere.Preserve the blue atmosphere in heat standby mode throughout the whole migration. Doc particular rollback steps for every migration wave, and take a look at your rollback process for not less than one non-critical DAG. Moreover, outline clear standards for triggering the rollback (reminiscent of particular failure charges or SLA violations).

Step-by-step migration course of

This part offers detailed steps for conducting the migration.

Pre-migration evaluation and preparation

Earlier than initiating the migration course of, conduct an intensive evaluation of your present atmosphere and develop the migration plan:

Be certain that your present Amazon MWAA atmosphere is on model 2.10.x
Create an in depth stock of your DAGs, customized operators, and plugins together with their dependencies and variations
Assessment your present necessities.txt file to grasp bundle necessities
Doc all atmosphere variables, connections, and configuration settings
Assessment the Apache Airflow 3.x launch notes to grasp breaking adjustments
Decide your migration success standards, rollback situations, and rollback plan
Determine a small variety of DAGs appropriate for the pilot migration
Develop a plan to coach, or familiarize, Amazon MWAA customers on Airflow 3

Compatibility checks

Figuring out compatibility points is crucial to a profitable migration. This step helps builders deal with particular code that’s incompatible with Airflow 3.

Use the ruff linter with the AIR30 ruleset to robotically determine code requiring updates:

ruff verify --preview --select AIR30

Moreover, evaluate your code for situations of direct metadatabase entry.

DAG code updates

Primarily based in your findings throughout compatibility testing, replace the affected DAG code for Airflow 3.x. The ruff DAG verify utility can robotically repair widespread adjustments. Use the next command to run the utility in replace mode:

ruff verify dag/ --select AIR301 --fix –preview

Widespread adjustments embrace:

Exchange direct metadata database entry with API calls:

# Earlier than (Airflow 2.x) - Direct DB entry
from airflow.settings import Session
from airflow.fashions.taskInstance import TaskInstance
session=Session()
outcome=session.question(TaskInstance)

For Apache Airflow v3.x, make the most of  within the Amazon MWAA SDK.
Replace core assemble imports with the brand new Airflow SDK namespace:
# Earlier than (Airflow 2.x)
from airflow.decorators import dag, process

# After (Airflow 3.x)
from airflow.sdk import dag, process

Exchange deprecated context variables with their trendy equivalents:

# Earlier than (Airflow 2.x)
def my_task(execution_date, **context):
    # Utilizing execution_date

# After (Airflow 3.x)
def my_task(logical_date, **context):
    # Utilizing logical_date

Subsequent, consider the utilization of the 2 scheduling-related default adjustments. catchup_by_default is now False, that means lacking DAG runs will now not robotically backfill. If backfill is required, replace the DAG definition with catchup=True. In case your DAGs require backfill, you should take into account the affect of this migration and backfilling. Since you’re migrating a DAG to a clear atmosphere with no historical past, enabling backfilling will create DAG runs for all runs starting with the required start_date. Take into account updating the start_date to keep away from pointless runs.

create_cron_data_intervals can also be now False. With this transformation, cron expressions are evaluated as a CronTriggerTimetable assemble.

Lastly, consider the utilization of deprecated context variables for manually and Asset-triggered DAGs, then replace your code with appropriate replacements.

Updating necessities and testing

Along with potential bundle model adjustments, a number of core Airflow operators beforehand included within the airflow-core bundle moved to the apache-airflow-providers-standard bundle. These adjustments have to be included into your necessities.txt file. Specifying, or pinning, bundle variations in your necessities file is a finest follow and beneficial for this migration.To replace your necessities file, full the next steps:

Obtain and configure the Amazon MWAA Docker photographs. For extra particulars, check with the GitHub repo.
Copy the present atmosphere’s necessities.txt file to a brand new file.
If wanted, add the apache-airflow-providers-standard bundle to the brand new necessities file.
Obtain the suitable Airflow constraints file to your goal Airflow model to your working director. A constraints file is out there for every Airflow model and Python model mixture. The URL takes the next type:
https://uncooked.githubusercontent.com/apache/airflow/constraints-${AIRFLOW_VERSION}/constraints-${PYTHON_VERSION}.txt
Create your versioned necessities file utilizing your un-versioned file and the constraints file. For steerage on making a necessities file, see Making a necessities.txt file. Be certain that there aren’t any dependency conflicts earlier than shifting ahead.
Confirm your necessities file utilizing the Docker picture. Run the next command contained in the working container:
```
./run.sh test-requirements
```
Handle any set up errors by updating bundle variations.

As a finest follow, we suggest packaging your packages right into a ZIP file for deployment in Amazon MWAA. This makes positive the identical actual packages are put in on all Airflow nodes. Discuss with Putting in Python dependencies utilizing PyPi.org Necessities File Format for detailed details about packaging dependencies.

Creating a brand new Amazon MWAA 3.x atmosphere

As a result of Amazon MWAA requires a migration method for main model upgrades, you should create a brand new atmosphere to your blue/inexperienced deployment. This put up makes use of the AWS Command Line Interface (AWS CLI) for instance, you can too use infrastructure as code (IaC).

Create a brand new S3 bucket utilizing the identical construction as the present S3 bucket.
Add the up to date necessities file and any plugin packages to the brand new S3 bucket.

Generate a template to your new atmosphere configuration:

aws mwaa create-environment --generate-cli-skeleton > new-mwaa3-env.json

Modify the generated JSON file:
1. Copy configurations out of your current atmosphere.
2. Replace the atmosphere title.
3. Set the AirflowVersion parameter to the goal 3.x model.
4. Replace the S3 bucket properties with the brand new S3 bucket title.
5. Assessment and replace different configuration parameters as wanted.
Configure the brand new atmosphere with the identical networking settings, safety teams, and IAM roles as your current atmosphere. Discuss with the Amazon MWAA Person Information for these configurations.

Create your new atmosphere:

aws mwaa create-environment --cli-input-json file://new-mwaa3-env.json

Metadata migration

Your new atmosphere requires the identical variables, connections, roles, and pool configurations. Use this part as a information for migrating this data. When you’re utilizing AWS Secrets and techniques Supervisor as your secrets and techniques backend, you don’t must migrate any connections. Relying your atmosphere’s measurement, you’ll be able to migrate this metadata utilizing the Airflow UI or the Apache Airflow REST API.

Replace any customized pool data within the new atmosphere utilizing the Airflow UI.
For environments utilizing the metadatabase as a secrets and techniques backend, migrate all connections to the brand new atmosphere.
Migrate all variables to the brand new atmosphere.
Migrate any customized Airflow roles to the brand new atmosphere.

Migration execution and validation

Plan and execute the transition out of your previous atmosphere to the brand new one:

Schedule the migration throughout a interval of low workflow exercise to attenuate disruption.
Implement a freeze window for DAG adjustments earlier than and throughout the migration.
Execute the migration in phases:
1. Pause DAGs within the previous atmosphere. For a small variety of DAGs, you should use the Airflow UI. For bigger teams, think about using the REST API.
2. Confirm all working duties have accomplished within the Airflow UI.
3. Redirect DAG triggers and exterior integrations to the brand new atmosphere.
4. Copy the up to date DAGs to the brand new atmosphere’s S3 bucket.
5. Allow DAGs within the new atmosphere. For a small variety of DAGs, you should use the Airflow UI. For bigger teams, think about using the REST API.
Monitor the brand new atmosphere carefully throughout the preliminary operation interval:
1. Look ahead to failed duties or scheduling points.
2. Test for lacking variables or connections.
3. Confirm exterior system integrations are functioning accurately.
4. Monitor Amazon CloudWatch metrics to verify the atmosphere is performing as anticipated.

Publish-migration validation

After the migration, totally validate the brand new atmosphere:

Confirm that each one DAGs are being scheduled accurately in response to their outlined schedules
Test that process historical past and logs are accessible and full
Check crucial workflows end-to-end to verify they execute efficiently
Validate connections to exterior programs are functioning correctly
Monitor CloudWatch metrics for efficiency validation

Cleanup and documentation

When the migration is full and the brand new atmosphere is secure, full the next steps:

Doc the adjustments made throughout the migration course of.
Replace runbooks and operational procedures to mirror the brand new atmosphere.
After a ample stability interval, outlined by stakeholders, decommission the previous atmosphere:
```
aws mwaa delete-environment --name old-mwaa2-env
```
Archive backup knowledge in response to your group’s retention insurance policies.

Conclusion

The journey from Airflow 2.x to three.x on Amazon MWAA is a chance to embrace next-generation workflow orchestration capabilities whereas sustaining the reliability of your workflow operations. By following these finest practices and sustaining a methodical method, you’ll be able to efficiently navigate this transition whereas minimizing dangers and disruptions to your online business operations.

A profitable migration requires thorough preparation, systematic testing, and sustaining clear documentation all through the method. Though the migration method requires extra preliminary effort, it offers the security and management wanted for such a major improve.