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

09 October 2025

51

Apache Airflow 3.x on Amazon MWAA introduces architectural enhancements resembling 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 via a migration method attributable to basic breaking modifications.

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 modifications, together with the elimination of direct metadata database entry from employees, deprecation of SubDAGs, modifications to default scheduling conduct, and library dependency updates. This publish gives greatest practices and a streamlined method to efficiently navigate this vital 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 basic modifications that organizations should perceive earlier than starting their migration. These modifications have an effect on core workflow operations and require cautious planning to realize a easy transition.

You need to be conscious of the next breaking modifications:

Removing of direct database entry – A vital change in Airflow 3 is the elimination of direct metadata database entry from employee nodes. Duties and customized operators should now talk via the REST API as a substitute of direct database connections. This architectural change impacts code that beforehand accessed the metadata database straight via 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 conduct modifications – Two notable modifications 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 modifications – A major 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 modifications – The REST API path modifications 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 a substitute 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 modifications.

The migration requires creating a brand new surroundings moderately than performing an in-place improve. Though this method calls for extra planning and sources, it gives the benefit of sustaining your current surroundings as a fallback possibility through 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 surroundings. This section establishes the muse for a easy transition by figuring out dependencies, configurations, and potential compatibility points. Consider your surroundings and code towards the beforehand talked about breaking modifications to have a profitable migration.

Surroundings evaluation

Start by conducting a whole stock of your present Amazon MWAA surroundings. Doc all DAGs, customized operators, plugins, and dependencies, together with their particular variations and configurations. Be sure your present surroundings is on model 2.10.x, as a result of this gives the perfect 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 surroundings. Creating separate buckets for every surroundings avoids conflicts and permits continued improvement with out affecting present pipelines.

Configuration documentation

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

Pipeline dependencies

Understanding pipeline dependencies is vital for a profitable phased migration. Establish interdependencies via 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.

Think about 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 routinely establish code requiring updates:

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

Then, assessment and replace your surroundings’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 have to 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 greatest practices

A methodical migration method minimizes threat whereas offering clear validation checkpoints. The beneficial technique employs a phased blue/inexperienced deployment mannequin that gives dependable migrations and rapid 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 surroundings 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 surroundings, testing, creating the blue/inexperienced surroundings (mentioned later on this publish), and the migration steps. Planning should additionally embrace the coaching, monitoring technique, rollback circumstances, and the rollback plan.
Part 2: Pilot migration – The pilot migration section serves to validate your detailed migration plan in a managed surroundings with a small vary of influence. Focus the pilot on two or three non-critical DAGs with numerous traits, resembling 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 modify each based mostly on precise outcomes. In the course of 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 might be prepared to start the complete wave-based migration for the remaining DAGs. Group remaining DAGs into logical waves based mostly on enterprise criticality (least vital 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 verify 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 gives enough time to carry out a rollback.
Part 4: Publish-migration assessment and decommissioning – In any case waves are full, conduct a post-migration assessment to establish classes discovered, optimization alternatives, and every other unresolved gadgets. That is additionally an excellent time to offer an approval on system stability. The ultimate step is decommissioning the unique Airflow 2.x surroundings. After stability is decided, based mostly on enterprise necessities and enter, decommission the unique (blue) surroundings.

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 through the migration and handle which DAGs function through which surroundings.

The blue surroundings (present Airflow 2.x) maintains manufacturing workloads throughout transition. You possibly can implement a freeze window for DAG modifications earlier than migration to keep away from last-minute code conflicts. This surroundings serves because the rapid rollback surroundings if a difficulty is recognized within the new (inexperienced) surroundings.

The inexperienced surroundings (new Airflow 3.x) receives migrated DAGs in managed waves. It mirrors the networking, IAM roles, and safety configurations from the blue surroundings. Configure this surroundings with the identical choices because the blue surroundings, and create equivalent 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 surroundings. This includes pausing the DAG within the blue surroundings earlier than activating the DAG within the inexperienced surroundings.Preserve the blue surroundings in heat standby mode throughout all the migration. Doc particular rollback steps for every migration wave, and take a look at your rollback process for at the very least 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 gives 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 surroundings and develop the migration plan:

Be sure your present Amazon MWAA surroundings is on model 2.10.x
Create an in depth stock of your DAGs, customized operators, and plugins together with their dependencies and variations
Evaluation your present necessities.txt file to know bundle necessities
Doc all surroundings variables, connections, and configuration settings
Evaluation the Apache Airflow 3.x launch notes to know breaking modifications
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 vital to a profitable migration. This step helps builders give attention to particular code that’s incompatible with Airflow 3.

Use the ruff linter with the AIR30 ruleset to routinely establish code requiring updates:

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

Moreover, assessment 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 routinely repair widespread modifications. Use the next command to run the utility in replace mode:

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

Widespread modifications embrace:

Change 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()
consequence=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

Change 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 modifications. catchup_by_default is now False, which means lacking DAG runs will not routinely backfill. If backfill is required, replace the DAG definition with catchup=True. In case your DAGs require backfill, you should contemplate the influence of this migration and backfilling. Since you’re migrating a DAG to a clear surroundings with no historical past, enabling backfilling will create DAG runs for all runs starting with the desired start_date. Think about updating the start_date to keep away from pointless runs.

create_cron_data_intervals can 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 modifications, a number of core Airflow operators beforehand included within the airflow-core bundle moved to the apache-airflow-providers-standard bundle. These modifications have to be integrated into your necessities.txt file. Specifying, or pinning, bundle variations in your necessities file is a greatest apply 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, consult with the GitHub repo.
Copy the present surroundings’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 on your goal Airflow model to your working director. A constraints file is accessible 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 sure there are not any dependency conflicts earlier than transferring 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 apply, 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 surroundings

As a result of Amazon MWAA requires a migration method for main model upgrades, you should create a brand new surroundings on your blue/inexperienced deployment. This publish makes use of the AWS Command Line Interface (AWS CLI) for instance, you too can 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 on your new surroundings configuration:

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

Modify the generated JSON file:
1. Copy configurations out of your current surroundings.
2. Replace the surroundings 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. Evaluation and replace different configuration parameters as wanted.
Configure the brand new surroundings with the identical networking settings, safety teams, and IAM roles as your current surroundings. Consult with the Amazon MWAA Person Information for these configurations.

Create your new surroundings:

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

Metadata migration

Your new surroundings requires the identical variables, connections, roles, and pool configurations. Use this part as a information for migrating this data. Should you’re utilizing AWS Secrets and techniques Supervisor as your secrets and techniques backend, you don’t must migrate any connections. Relying your surroundings’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 surroundings utilizing the Airflow UI.
For environments utilizing the metadatabase as a secrets and techniques backend, migrate all connections to the brand new surroundings.
Migrate all variables to the brand new surroundings.
Migrate any customized Airflow roles to the brand new surroundings.

Migration execution and validation

Plan and execute the transition out of your outdated surroundings to the brand new one:

Schedule the migration throughout a interval of low workflow exercise to attenuate disruption.
Implement a freeze window for DAG modifications earlier than and through the migration.
Execute the migration in phases:
1. Pause DAGs within the outdated surroundings. 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 surroundings.
4. Copy the up to date DAGs to the brand new surroundings’s S3 bucket.
5. Allow DAGs within the new surroundings. 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 surroundings carefully through the preliminary operation interval:
1. Look ahead to failed duties or scheduling points.
2. Verify for lacking variables or connections.
3. Confirm exterior system integrations are functioning accurately.
4. Monitor Amazon CloudWatch metrics to verify the surroundings is performing as anticipated.

Publish-migration validation

After the migration, completely validate the brand new surroundings:

Confirm that every one DAGs are being scheduled accurately in response to their outlined schedules
Verify that process historical past and logs are accessible and full
Check vital workflows end-to-end to verify 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 surroundings is steady, full the next steps:

Doc the modifications made through the migration course of.
Replace runbooks and operational procedures to mirror the brand new surroundings.
After a enough stability interval, outlined by stakeholders, decommission the outdated surroundings:
```
aws mwaa delete-environment --name old-mwaa2-env
```
Archive backup information 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 greatest practices and sustaining a methodical method, you’ll be able to efficiently navigate this transition whereas minimizing dangers and disruptions to what you are promoting 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 gives the protection and management wanted for such a big improve.