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

October 10, 2025

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Apache Airflow 3.x on Amazon MWAA introduces architectural enhancements resembling API-based activity execution that gives enhanced safety and isolation. Different main updates embody a redesigned UI for higher consumer 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 a migration method on account of elementary breaking adjustments.

This migration presents a possibility to embrace next-generation workflow orchestration capabilities whereas offering enterprise continuity. Nonetheless, it’s greater than a easy improve. Organizations migrating to Airflow 3.x on Amazon MWAA should perceive key breaking adjustments, together with the elimination of direct metadata database entry from staff, deprecation of SubDAGs, adjustments to default scheduling habits, and library dependency updates. This publish offers greatest practices and a streamlined method to efficiently navigate this important migration, offering minimal disruption to your mission-critical information 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 elementary adjustments that organizations should perceive earlier than starting their migration. These adjustments have an effect on core workflow operations and require cautious planning to realize a easy transition.

You have to be conscious of the next breaking adjustments:

Removing of direct database entry – A important change in Airflow 3 is the elimination of direct metadata database entry from employee nodes. Duties and customized operators should now talk by the REST API as an alternative of direct database connections. This architectural change impacts code that beforehand accessed the metadata database straight by SQLAlchemy connections, requiring refactoring of present DAGs and customized operators.
SubDAG deprecation – Airflow 3 removes the SubDAG assemble in favor of TaskGroups, Belongings, and Information Conscious Scheduling. Organizations should refactor present SubDAGs to one of many beforehand talked about constructs.
Scheduling habits adjustments – Two notable adjustments to default scheduling choices require an influence evaluation:
- The default values for catchup_by_default and create_cron_data_intervals modified to False. This transformation impacts DAGs that don’t explicitly set these choices.
- Airflow 3 removes a number of context variables, resembling 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 major variety of libraries change in Airflow 3.x, requiring DAG code refactoring. Many beforehand included supplier packages may want express 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 internet 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 quite than performing an in-place improve. Though this method calls for extra planning and sources, it offers the benefit of sustaining your present atmosphere as a fallback choice throughout the transition, facilitating enterprise continuity all through the migration course of.

Pre-migration planning and evaluation

Profitable migration is determined by thorough planning and evaluation of your present atmosphere. This part establishes the muse for a easy transition by figuring out dependencies, configurations, and potential compatibility points. Consider your atmosphere and code towards the beforehand talked about breaking adjustments to have a profitable migration.

Surroundings evaluation

Start by conducting a whole stock of your present Amazon MWAA atmosphere. Doc all DAGs, customized operators, plugins, and dependencies, together with their particular variations and configurations. Ensure 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.

Establish 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. Overview AWS Id and Entry Administration (IAM) sources, as a result of your new atmosphere’s execution position 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 important for a profitable phased migration. Establish interdependencies by Datasets (now Belongings), SubDAGs, TriggerDagRun operators, or exterior API interactions. Develop your migration plan round these dependencies so associated DAGs can migrate on the similar time.

Contemplate DAG scheduling frequency when planning migration waves. DAGs with longer intervals between runs present bigger migration home windows and decrease threat of duplicate execution in contrast with continuously operating 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 mechanically establish code requiring updates:

ruff verify --preview --select AIR30 <path_to_your_dag_code>

Then, evaluate and replace your atmosphere’s necessities.txt file to verify 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 should be added to your necessities file.

Take a look at your DAGs utilizing the Amazon MWAA Docker photographs for Airflow 3.x. These photographs make it attainable to create and check your necessities file, and make sure the Scheduler efficiently parses your DAGs.

Migration technique and greatest practices

A methodical migration method minimizes threat whereas offering clear validation checkpoints. The really helpful technique employs a phased blue/inexperienced deployment mannequin that gives dependable migrations and speedy 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 part, full your atmosphere stock, dependency mapping, and breaking change evaluation. With the gathered info, develop the detailed migration plan. This plan will embody steps for updating code, updating your necessities file, making a check atmosphere, testing, creating the blue/inexperienced atmosphere (mentioned later on this publish), and the migration steps. Planning should additionally embody the coaching, monitoring technique, rollback circumstances, and the rollback plan.
Part 2: Pilot migration – The pilot migration part serves to validate your detailed migration plan in a managed atmosphere with a small vary of influence. Focus the pilot on two or three non-critical DAGs with various traits, resembling totally different schedules and dependencies. Migrate the chosen DAGs utilizing the migration plan outlined within the earlier part. Use this part to validate your plan and monitoring instruments, and modify each primarily based on precise outcomes. Through the pilot, set up baseline migration metrics to assist predict the efficiency of the complete migration.
Part 3: Wave-based manufacturing migration – After a profitable pilot, you’re prepared to start the complete wave-based migration for the remaining DAGs. Group remaining DAGs into logical waves primarily based on enterprise criticality (least important 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. Embrace enough validation intervals between waves to substantiate the wave is profitable earlier than beginning the following wave. This time additionally reduces the vary of influence within the occasion of a migration problem, and offers enough time to carry out a rollback.
Part 4: Put up-migration evaluate and decommissioning – In any case waves are full, conduct a post-migration evaluate to establish classes discovered, optimization alternatives, and every other unresolved objects. That is additionally a superb time to offer an approval on system stability. The ultimate step is decommissioning the unique Airflow 2.x atmosphere. After stability is decided, primarily based on enterprise necessities and enter, decommission the unique (blue) atmosphere.

Blue/inexperienced deployment technique

Implement a blue/inexperienced deployment technique for protected, reversible migration. With this technique, you’ll have two Amazon MWAA environments working throughout the migration and handle which DAGs function by 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 speedy rollback atmosphere if a difficulty 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 might be monitored concurrently. To keep away from duplicate DAG runs, be sure that 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 complete migration. Doc particular rollback steps for every migration wave, and check your rollback process for a minimum of one non-critical DAG. Moreover, outline clear standards for triggering the rollback (resembling 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 a radical evaluation of your present atmosphere and develop the migration plan:

Ensure 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
Overview your present necessities.txt file to know bundle necessities
Doc all atmosphere variables, connections, and configuration settings
Overview the Apache Airflow 3.x launch notes to know breaking adjustments
Decide your migration success standards, rollback circumstances, and rollback plan
Establish 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 important 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 mechanically establish code requiring updates:

ruff verify --preview --select AIR30 <path_to_your_dag_code>

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

DAG code updates

Based mostly in your findings throughout compatibility testing, replace the affected DAG code for Airflow 3.x. The ruff DAG verify utility can mechanically repair frequent adjustments. Use the next command to run the utility in replace mode:

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

Frequent adjustments embody:

Substitute 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, activity

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

Substitute deprecated context variables with their fashionable 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 mechanically backfill. If backfill is required, replace the DAG definition with catchup=True. In case your DAGs require backfill, you should take into account the influence 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. Contemplate updating the start_date to keep away from pointless runs.

create_cron_data_intervals can also be now False. With this modification, 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 attainable 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 greatest follow and really helpful for this migration.To replace your necessities file, full the next steps:

Obtain and configure the Amazon MWAA Docker photographs. For extra particulars, confer 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. Ensure that there are not any dependency conflicts earlier than shifting ahead.
Confirm your necessities file utilizing the Docker picture. Run the next command contained in the operating container:
```
./run.sh test-requirements
```
Deal with any set up errors by updating bundle variations.

As a greatest follow, we advocate packaging your packages right into a ZIP file for deployment in Amazon MWAA. This makes positive the identical precise packages are put in on all Airflow nodes. Consult 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 publish makes use of the AWS Command Line Interface (AWS CLI) for example, 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 present 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. Overview and replace different configuration parameters as wanted.
Configure the brand new atmosphere with the identical networking settings, safety teams, and IAM roles as your present atmosphere. Consult 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 info. For those who’re utilizing AWS Secrets and techniques Supervisor as your secrets and techniques backend, you don’t have to migrate any connections. Relying your atmosphere’s measurement, you may migrate this metadata utilizing the Airflow UI or the Apache Airflow REST API.

Replace any customized pool info 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 reduce 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 utilize the Airflow UI. For bigger teams, think about using the REST API.
2. Confirm all operating 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 utilize the Airflow UI. For bigger teams, think about using the REST API.
Monitor the brand new atmosphere intently 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 substantiate the atmosphere is performing as anticipated.

Put up-migration validation

After the migration, totally validate the brand new atmosphere:

Confirm that each one DAGs are being scheduled accurately in accordance with their outlined schedules
Test that activity historical past and logs are accessible and full
Take a look at important workflows end-to-end to substantiate they execute efficiently
Validate connections to exterior techniques 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 enough stability interval, outlined by stakeholders, decommission the previous atmosphere:
```
aws mwaa delete-environment --name old-mwaa2-env
```
Archive backup information in accordance with 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 greatest practices and sustaining a methodical method, you may efficiently navigate this transition whereas minimizing dangers and disruptions to your enterprise 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 protection and management wanted for such a big improve.