DevOps for DataOps: Building a CI/CD Pipeline for Apache Airflow DAGs (2022)

Build an effective CI/CD pipeline to test and deploy your Apache Airflow DAGs to Amazon MWAA using GitHubActions

In this post, we will learn how to use GitHub Actions to build an effective CI/CD workflow for our Apache Airflow DAGs. We will use the DevOps concepts of Continuous Integration and Continuous Delivery to automate the testing and deployment of Airflow DAGs to Amazon Managed Workflows for Apache Airflow (Amazon MWAA) on AWS.

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Apache Airflow

According to the documentation, Apache Airflow is an open-source platform to author, schedule, and monitor workflows programmatically. With Airflow, you author workflows as Directed Acyclic Graphs (DAGs) of tasks written in Python.

Amazon Managed Workflows for ApacheAirflow

According to AWS, Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is a highly available, secure, and fully-managed workflow orchestration for Apache Airflow. MWAA automatically scales its workflow execution capacity to meet your needs and is integrated with AWS security services to help provide fast and secure access to data.

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GitHub Actions

According to GitHub, GitHub Actions makes it easy to automate software workflows with CI/CD. GitHub Actions allow you to build, test, and deploy code right from GitHub. GitHub Actions are workflows triggered by GitHub events like push, issue creation, or a new release. You can leverage GitHub Actions prebuilt and maintained by the community.

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If you are new to GitHub Actions, I recommend my previous post, Continuous Integration and Deployment of Docker Images using GitHub Actions.



According to Wikipedia, DataOps is an automated, process-oriented methodology used by analytic and data teams to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new approach to data analytics.

DataOps applies to the entire data lifecycle from data preparation to reporting and recognizes the interconnected nature of the data analytics team and IT operations. DataOps incorporates the Agile methodology to shorten the software development life cycle (SDLC) of analytics development.


According to Wikipedia, DevOps is a set of practices that combines software development (Dev) and IT operations (Ops). It aims to shorten the systems development life cycle and provide continuous delivery with high software quality.

DevOps is a set of practices intended to reduce the time between committing a change to a system and the change being placed into normal production, while ensuring high quality. -Wikipedia

(Video) DevOps for DataOps: Building a CI/CD Pipeline for Apache Airflow DAGs - GitHub Actions

Fail Fast

According to Wikipedia, a fail-fast system is one that immediately reports any condition that is likely to indicate a failure. Using the DevOps concept of fail fast, we build steps into our workflows to uncover errors sooner in the SDLC. We shift testing as far to the left as possible (referring to the pipeline of steps moving from left to right) and test at multiple points along the way.

All source code for this demonstration, including the GitHub Actions, Pytest unit tests, and Git Hooks, is open-sourced and located on GitHub.

The diagram below represents the architecture for a recent blog post and video demonstration, Lakehouse Automation on AWS with Apache Airflow. The post and video show how to programmatically load and upload data from Amazon Redshift to an Amazon S3-based data lake using Apache Airflow.

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In this post, we will review how the DAGs from the previous were developed, tested, and deployed to MWAA using a variety of progressively more effective CI/CD workflows. The workflows demonstrated could also be easily applied to other Airflow resources in addition to DAGs, such as SQL scripts, configuration and data files, Python requirement files, and plugins.

No DevOps

Below we see a minimally viable workflow for loading DAGs into Amazon MWAA, which does not use the principles of CI/CD. Changes are made in the local Airflow developer’s environment. The modified DAGs are copied directly to the Amazon S3 bucket, which are then automatically synced with Amazon MWAA, barring any errors. Those changes are also (hopefully) pushed back to the centralized version control or source code management (SCM) system, which is GitHub in this post.

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There are at least two significant issues with this error-prone workflow. First, the DAGs are always out of sync between the Amazon S3 bucket and GitHub. These are two independent steps — copying or syncing the DAGs to S3 and pushing the DAGs to GitHub. A developer might continue making changes and pushing DAGs to S3 without pushing to GitHub or vice versa.

Secondly, the DevOps concept of fail-fast is missing. The first time you know your DAG contains errors is likely when it is synced to MWAA and throws an Import Error. By then, the DAG has already been copied to S3, synced to MWAA, and possibly pushed to GitHub, which other developers could then pull.

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GitHub Actions

A significant step up from the previous workflow is using GitHub Actions to test and deploy your code after pushing it to GitHub. Although in this workflow, code is still ‘pushed straight to Trunk’ (the main branch in GitHub) and risks other developers in a collaborative environment pulling potentially erroneous code, you have far less chance of DAG errors making it to MWAA.

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Using GitHub Actions, you also eliminate human error that could result in the changes to DAGs not being synced to Amazon S3. Lastly, using this workflow improves security by eliminating the need to provide direct access to the Airflow Amazon S3 bucket to Airflow Developers.

(Video) DevOps for DataOps: Building a CI/CD Pipeline for Apache Airflow DAGs - Git Hooks and GitHub Actions

Types ofTests

The first GitHub Action, test_dags.yml, is triggered on a push to the dags directory in the main branch of the repository. It is also triggered whenever a pull request is made for the main branch. The first GitHub Action runs a battery of tests, including checking Python dependencies, code style, code quality, DAG import errors, and unit tests. The tests catch issues with DAGs before being synced to S3 by a second GitHub Action.

name: Test DAGson: push: paths: - 'dags/**' pull_request: branches: - mainjobs: test: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - name: Set up Python uses: actions/setup-python@v2 with: python-version: '3.7' - name: Install dependencies run: | python -m pip install --upgrade pip pip install -r requirements/requirements.txt pip check - name: Lint with Flake8 run: | pip install flake8 flake8 --ignore E501 dags --benchmark -v - name: Confirm Black code compliance (psf/black) run: | pip install pytest-black pytest dags --black -v - name: Test with Pytest run: | pip install pytest cd tests || exit pytest -v
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Python Dependencies

The first test installs the modules listed in the requirements.txt file used locally to develop the application. This test is designed to uncover any missing or conflicting modules.

- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements/requirements.txt
pip check

It is essential to develop your DAGs against the same version of Python and with the same version of the Python modules used in your Airflow environment. You can use the BashOperator to run shell commands to obtain the versions of Python and module installed in your Airflow environment:

python3 --version; python3 -m pip list

A snippet of log output from DAG showing Python version and Python modules available in MWAA 2.0.2:

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The latest stable release of Airflow is currently version 2.2.2, released 2021-11-15. However, as of December 2021, Amazon’s latest version of MWAA 2.x is version 2.0.2, released 2021-04-19. MWAA 2.0.2 currently runs Python3 version 3.7.10.

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Known as ‘your tool for style guide enforcement,’ Flake8 is described as the modular source code checker. It is a command-line utility for enforcing style consistency across Python projects. Flake8 is a wrapper around PyFlakes, pycodestyle, and Ned Batchelder’s McCabe script. The module, pycodestyle, is a tool to check your Python code against some of the style conventions in PEP 8.

Flake8 is highly configurable, with options to ignore specific rules if not required by your development team. For example, in this demonstration, I intentionally ignored rule E501, which states that ‘line length should be limited to 72 characters.

- name: Lint with Flake8
run: |
pip install flake8
flake8 --ignore E501 dags --benchmark -v


Known as ‘the uncompromising code formatter,’ Python code formatted using Black (referred to as Blackened code) looks the same regardless of the project you’re reading. Formatting becomes transparent, allowing teams to focus on the content instead. Black makes code review faster by producing the smallest diffs possible, assuming all developers are using black to format their code.

The Airflow DAGs in this GitHub repository are automatically formatted with black using a pre-commit Git Hooks before being committed and pushed to GitHub. The test confirms black code compliance.

- name: Confirm Black code compliance (psf/black)
run: |
pip install pytest-black
pytest dags --black -v


The pytest framework describes itself as a mature, fully-featured Python testing tool that helps you write better programs. The Pytest framework makes it easy to write small tests yet scales to support complex functional testing for applications and libraries.

(Video) DevOps for DataOps: Building a CI/CD Pipeline for Apache Airflow DAGs - Pre-commit Git Hook

The GitHub Action in the GitHub project, test_dags.yml, calls the file, also contained in the project.

- name: Test with Pytest
run: |
pip install pytest
cd tests || exit
pytest -v

The file contains several pytest unit tests. The tests are based on my project requirements; your tests will vary. These tests confirm that all DAGs:

  1. Do not contain DAG Import Errors (test catches 75% of my errors);
  2. Follow specific file naming conventions;
  3. Include a description and an owner other than ‘airflow’;
  4. Contain required project tags;
  5. Do not send emails (my projects use SNS or Slack for notifications);
  6. Do not retry more than three times;
import os
import sys
import pytest
from airflow.models import DagBag
sys.path.append(os.path.join(os.path.dirname(__file__), "../dags"))
sys.path.append(os.path.join(os.path.dirname(__file__), "../dags/utilities"))
# Airflow variables called from DAGs under test are stubbed out
os.environ["AIRFLOW_VAR_DATA_LAKE_BUCKET"] = "test_bucket"
os.environ["AIRFLOW_VAR_SNS_TOPIC"] = "test_topic"
os.environ["AIRFLOW_VAR_REDSHIFT_UNLOAD_IAM_ROLE"] = "test_role_1"
os.environ["AIRFLOW_VAR_GLUE_CRAWLER_IAM_ROLE"] = "test_role_2"
def dag_bag(request):
return DagBag(dag_folder=request.param, include_examples=False)
def test_no_import_errors(dag_bag):
assert not dag_bag.import_errors
def test_requires_tags(dag_bag):
for dag_id, dag in dag_bag.dags.items():
assert dag.tags
def test_requires_specific_tag(dag_bag):
for dag_id, dag in dag_bag.dags.items():
assert dag.tags.index("data lake demo") >= 0
except ValueError:
assert dag.tags.index("redshift demo") >= 0
def test_desc_len_greater_than_fifteen(dag_bag):
for dag_id, dag in dag_bag.dags.items():
assert len(dag.description) > 15
def test_owner_len_greater_than_five(dag_bag):
for dag_id, dag in dag_bag.dags.items():
assert len(dag.owner) > 5
def test_owner_not_airflow(dag_bag):
for dag_id, dag in dag_bag.dags.items():
assert str.lower(dag.owner) != "airflow"
def test_no_emails_on_retry(dag_bag):
for dag_id, dag in dag_bag.dags.items():
assert not dag.default_args["email_on_retry"]
def test_no_emails_on_failure(dag_bag):
for dag_id, dag in dag_bag.dags.items():
assert not dag.default_args["email_on_failure"]
def test_three_or_less_retries(dag_bag):
for dag_id, dag in dag_bag.dags.items():
assert dag.default_args["retries"] <= 3
def test_dag_id_contains_prefix(dag_bag):
for dag_id, dag in dag_bag.dags.items():
assert str.lower(dag_id).find("__") != -1
def test_dag_id_requires_specific_prefix(dag_bag):
for dag_id, dag in dag_bag.dags.items():
assert str.lower(dag_id).startswith("data_lake__") \
or str.lower(dag_id).startswith("redshift_demo__")

If you are building custom Airflow Operators, additional unit, functional, and integration tests are recommended.

Fork andPull

We can improve on the practice of pushing directly to Trunk by implementing one of two collaborative development models, recommended by GitHub:

  1. The Shared repository model: uses ‘topic’ branches, which are reviewed, approved, and merged into the main branch.
  2. Fork and pull model: a repo is forked, changes are made, a pull request is created, the request is reviewed, and if approved, merged into the main branch.

In the fork and pull model, we create a fork of the DAG repository where we make our changes. We then commit and push those changes back to the forked repository. When ready, we create a pull request. If the pull request is approved and passes all the tests, it is manually or automatically merged into the main branch. DAGs are then synced to S3 and, eventually, to MWAA. I usually prefer to trigger merges manually once all tests have passed.

The fork and pull model greatly reduces the chance that bad code is merged to the main branch before passing all tests.

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Syncing DAGs toS3

The second GitHub Action in the GitHub project, sync_dags.yml, is triggered when the previous Action, test_dags.yml, completes successfully, or in the case of the folk and pull method, the merge to the main branch is successful.

name: Sync DAGs

- 'Test DAGs'
- completed
- closed

runs-on: ubuntu-latest
if: ${{ github.event.workflow_run.conclusion == 'success' }}
- uses: actions/checkout@master
- uses: jakejarvis/s3-sync-action@master
AWS_S3_BUCKET: ${{ secrets.AWS_S3_BUCKET }}
AWS_REGION: 'us-east-1'
SOURCE_DIR: 'dags'
DEST_DIR: 'dags'

The GitHub Action, sync_dags.yml, requires three GitHub encrypted secrets, created in advance and associated with the GitHub repository. According to GitHub, secrets are encrypted environment variables you create in an organization, repository, or repository environment. Encrypted secrets allow you to store sensitive information, such as access tokens, in your repository. The secrets that you create are available to use in GitHub Actions workflows.

(Video) Pipelines on pipelines: Agile CI/CD workflows for Airflow DAGs

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The DAGs are synced to Amazon S3 and, eventually, automatically synced to MWAA.

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To further improve your CI/CD workflows, you should consider using Git Hooks. Using Git Hooks, we can ensure code is tested locally before committing and pushing changes to GitHub. Testing locally allows us to fail-faster, catching errors during development instead of once code is pushed to GitHub.

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According to the documentation, Git has a way to fire off custom scripts when certain important actions occur. There are two types of hooks: client-side and server-side. Client-side hooks are triggered by operations such as committing and merging, while server-side hooks run on network operations such as receiving pushed commits.

You can use these hooks for all sorts of reasons. I often use a client-side pre-commit hook to format DAGs using black. Using a client-side pre-push Git Hook, we will ensure that tests are run before pushing the DAGs to GitHub. According to Git, The pre-push hook runs when the git push command is executed after the remote refs have been updated but before any objects have been transferred. You can use it to validate a set of ref updates before a push occurs. A non-zero exit code will abort the push. The test could instead be run as part of the pre-commit hook if they are not too time-consuming.

To use the pre-push hook, create the following file within the local repository,.git/hooks/pre-push:

# do nothing if there are no commits to push
if [ -z "$(git log @{u}..)" ]; then
exit 0
sh ./

Then, run the following chmod command to make the hook executable:

chmod 755 .git/hooks/pre-push

The the pre-push hook runs the shell script, The script executes nearly identical tests, locally, as the GitHub Action, test_dags.yml, does remotely on GitHub:

echo "Starting Flake8 test..."
flake8 --ignore E501 dags --benchmark || exit 1
echo "Starting Black test..."
python3 -m pytest --cache-clear
python3 -m pytest dags/ --black -v || exit 1
echo "Starting Pytest tests..."
cd tests || exit
python3 -m pytest -v || exit 1
echo "All tests completed successfully! 🥳"

Here are some additional references for testing and deploying Airflow DAGs and the use of GitHub Actions:

This blog represents my own viewpoints and not of my employer, Amazon Web Services (AWS). All product names, logos, and brands are the property of their respective owners.

(Video) How to manage Airflow Dags in Production | Dags versioning & Deployment


How do you automate CI CD pipeline Azure DevOps? ›

  1. Prerequisites.
  2. Sign in to the Azure portal.
  3. Configure access to your GitHub repo and select a framework.
  4. Configure Azure DevOps and an Azure subscription.
  5. Commit changes to GitHub and automatically deploy them to Azure.
  6. Examine the Azure Pipelines CI/CD pipeline.
  7. Clean up resources.
  8. Next steps.
6 days ago

Can Airflow be used for CI CD? ›

Since Airflow and all its components are defined in source code, it is a fitting approach to create a robust development and deployment framework with CI/CD tools.

Is Airflow a DevOps tool? ›

Apache Airflow is an open source tool that helps DevOps teams build and manage workflows programmatically. It can help drive data pipelines by using standard Python features… Share your ideas with millions of readers.

Can Airflow replace Jenkins? ›

Airflow vs Jenkins: Production and Testing

Since Airflow is not a DevOps tool, it does not support non-production tasks. This means that any job you load on Airflow will be processed in real-time. However, Jenkins is more suitable for testing builds.

Can you explain CI CD pipeline setup using Azure DevOps step by step? ›

Now I want to set up a CI-CD pipeline for this application using Azure DevOps. Please select build option from pipeline menu to set up build pipeline. Now choose a build template provided by Azure DevOps as shown below. I can also select the TFVC version control system or GitHub if I want to as source code.

What are the four steps in a CI CD pipeline? ›

There are four stages of a CI/CD pipeline 1) Source Stage, 2) Build Stage, 3) Test Stage, 4) Deploy Stage. Important CI/CD tools are Jenkins, Bambo, and Circle CI. CI/CD pipeline can improve reliability. CI/CD pipeline makes IT team more attractive to developers.

Is Airflow good for ETL? ›

Airflow ETL is one such popular framework that helps in workflow management. It has excellent scheduling capabilities and graph-based execution flow makes it a great alternative for running ETL jobs.

Can we do ETL in Airflow? ›

Apache Airflow ETL is an open-source platform that creates, schedules, and monitors data workflows. It allows you to take data from different sources, transform it into meaningful information, and load it to destinations like data lakes or data warehouses.

Which tool is best for CI CD integration? ›

Best 14 CI/CD Tools You Must Know | Updated for 2022
  • Jenkins. Jenkins is an open-source automation server in which the central build and continuous integration process take place. ...
  • CircleCI. CircleCI is a CI/CD tool that supports rapid software development and publishing. ...
  • TeamCity. ...
  • Bamboo. ...
  • GitLab. ...
  • Buddy. ...
  • Travis CI. ...
  • Codeship.

Is Airflow and Jenkins the same? ›

Airflow is more for considering the production scheduled tasks and hence Airflows are widely used for monitoring and scheduling data pipelines whereas Jenkins are used for continuous integrations and deliveries.

Is Airflow ETL or ELT? ›

Airflow is purpose-built to orchestrate the data pipelines that provide ELT at scale for a modern data platform.

Does Airflow have a REST API? ›

Airflow exposes an REST API. It is available through the webserver. Endpoints are available at /api/experimental/ .

Can we run Airflow without Docker? ›

How to install and run Airflow locally with Windows subsystem for Linux (WSL) with these steps: Open Microsoft Store, search for Ubuntu , install it then restart. Open cmd and type wsl. Update everything: sudo apt update && sudo apt upgrade.

What is the difference between CI CD and DevOps? ›

CI/CD refers to a set of development practices that enable the rapid and reliable delivery of code changes, while DevOps is a collection of ideas, practices, processes, and technologies that allow development and operations teams to work together to streamline product development.

What is CI CD process in DevOps? ›

CI/CD is a method to frequently deliver apps to customers by introducing automation into the stages of app development. The main concepts attributed to CI/CD are continuous integration, continuous delivery, and continuous deployment.

What is a CI CD pipeline in DevOps? ›

Overview. A continuous integration and continuous deployment (CI/CD) pipeline is a series of steps that must be performed in order to deliver a new version of software. CI/CD pipelines are a practice focused on improving software delivery throughout the software development life cycle via automation.

How do you create a CI and CD pipeline explain stages in pipeline? ›

A CI/CD pipeline is a series of orchestrated steps with the ability to take source code all the way into production. The steps include building, packaging, testing, validating, verifying infrastructure, and deploying into all necessary environments.

What are the first 3 elements in continuous delivery pipeline? ›

The first three elements of the pipeline (CE, CI, and CD) work together to support the delivery of small batches of new functionality, which are then released to fulfill market demand.

What are DevOps phases? ›

As mentioned earlier, the various phases such as continuous development, continuous integration, continuous testing, continuous deployment, and continuous monitoring constitute DevOps Life cycle.

What is the equivalent of Airflow in AWS? ›

Airflow can be classified as a tool in the "Workflow Manager" category, while AWS Glue is grouped under "Big Data Tools". Some of the features offered by Airflow are: Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation.

Which Python is required for Airflow? ›

Note: SQLite is used in Airflow tests. Do not use it in production. We recommend using the latest stable version of SQLite for local development. Please note that with respect to Python 3 support, Airflow 2.0.

What language does Apache Airflow use? ›

Airflow is written in Python, and workflows are created via Python scripts.

Is Airflow a data pipeline? ›

Apache Airflow is a batch-oriented tool for building data pipelines. It is used to programmatically author, schedule, and monitor data pipelines commonly referred to as workflow orchestration. Airflow is an open-source platform used to manage the different tasks involved in processing data in a data pipeline.

Which executor is best for Airflow? ›

Airflow comes configured with the SequentialExecutor by default, which is a local executor, and the safest option for execution, but we strongly recommend you change this to LocalExecutor for small, single-machine installations, or one of the remote executors for a multi-machine/cloud installation.

Is Jenkins a DevOps tool? ›

Jenkins is an open source continuous integration/continuous delivery and deployment (CI/CD) automation software DevOps tool written in the Java programming language. It is used to implement CI/CD workflows, called pipelines.

Is Docker a CI CD tool? ›

Listen on the go! Docker has become an early adopter in Continuous Integration and Continuous Deployment. By leveraging the right integration with source code control mechanism such as GIT, Jenkins can initiate a build process each time a developer commits his code.

Is Kubernetes a CI CD tool? ›

CI/CD automates many steps from when code is developed to the point it is released in production. Similarly, Kubernetes automates container deployments across various infrastructure environments and ensures efficient resource utilisation.

Does Airflow need Kubernetes? ›

Apache Airflow aims to be a very Kubernetes-friendly project, and many users run Airflow from within a Kubernetes cluster in order to take advantage of the increased stability and autoscaling options that Kubernetes provides.

Is Airflow a big data tool? ›

Summary. Airflow fills a gap in the big data ecosystem by providing a simpler way to define, schedule, visualize and monitor the underlying jobs needed to operate a big data pipeline.

Is there anything better than Jenkins? ›

GoCD is an Open source Continuous Integration server. It is one of the best alternatives to Jenkins used to model and visualize complex workflows with ease. This CI tool allows continuous delivery and provides an intuitive interface for building CD pipelines.

Which ETL tool is best? ›

8 More Top ETL Tools to Consider
  • 1) Striim. Striim offers a real-time data integration platform for big data workloads. ...
  • 2) Matillion. Matillion is a cloud ETL platform that can integrate data with Redshift, Snowflake, BigQuery, and Azure Synapse. ...
  • 3) Pentaho. ...
  • 4) AWS Glue. ...
  • 5) Panoply. ...
  • 6) Alooma. ...
  • 7) Hevo Data. ...
  • 8) FlyData.

What is the difference between pipeline and ETL? ›

How ETL and Data Pipelines Relate. ETL refers to a set of processes extracting data from one system, transforming it, and loading it into a target system. A data pipeline is a more generic term; it refers to any set of processing that moves data from one system to another and may or may not transform it.

What is the difference between Kafka and Airflow? ›

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. Airflow belongs to "Workflow Manager" category of the tech stack, while Kafka can be primarily classified under "Message Queue".

What are the 4 key components of DevOps? ›

DevOps Practices
  • Continuous Integration.
  • Continuous Delivery.
  • Microservices.
  • Infrastructure as Code.
  • Monitoring and Logging.
  • Communication and Collaboration.

What are 5 DevOps practices? ›

  • DevOps is cultural and technical. ...
  • Phase Zero: You haven't started DevOps. ...
  • Phase 1: DevOps in pockets. ...
  • Phase 2: Automation. ...
  • Phase 3: Pipeline. ...
  • The web hosting maturity scale. ...
  • Phase 4: Blended architecture. ...
  • Phase 5: Continuous deployment.

Is Microsoft getting rid of DevOps? ›

First, Microsoft is discontinuing the Azure DevOps Services Preview Program. This program allowed users to try out new features and get early feedback. With the program ending, feedback will no longer be available to help improve Azure DevOps. PRO TIP: No, Azure DevOps is not being deprecated.

How many DAGs can Airflow handle? ›

You can have as many DAGs as you want, each describing an arbitrary number of tasks. In general, each one should correspond to a single logical workflow. When searching for DAGs, Airflow will only consider files where the string “airflow” and “DAG” both appear in the contents of the . py file.

Is Airflow better than cron? ›

Using cron to manage networks of jobs will not scale effectively. Airflow offers ability to schedule, monitor, and most importantly, scale, increasingly complex workflows.

What is the best shape for Airflow? ›

If you want to speed quickly through the air, you're better off in a long, thin vehicle—something like a plane or a train—that creates as little disturbance as possible: planes and trains are tube-shaped for exactly the same reason that we swim horizontally with our bodies laid out long and thin.

How many tasks can Airflow handle? ›

You can also tune your worker_concurrency (environment variable: AIRFLOW__CELERY__WORKER_CONCURRENCY ), which determines how many tasks each Celery worker can run at any given time. By default, the Celery executor runs a maximum of sixteen tasks concurrently.

Does Airflow need a database? ›

The metadata database is a core component of Airflow. It stores crucial information such as the configuration of your Airflow environment's roles and permissions, as well as all metadata for past and present DAG and task runs. A healthy metadata database is critical for your Airflow environment.

What is the difference between Airflow and dataflow? ›

Airflow is a platform to programmatically author, schedule, and monitor workflows. Cloud Dataflow is a fully-managed service on Google Cloud that can be used for data processing. You can write your Dataflow code and then use Airflow to schedule and monitor Dataflow job.

Is Airflow better than Jenkins? ›

Airflow vs Jenkins: Production and Testing

Since Airflow is not a DevOps tool, it does not support non-production tasks. This means that any job you load on Airflow will be processed in real-time. However, Jenkins is more suitable for testing builds. It supports test frameworks like Robot, PyTest, and Selenium.

Can we run multiple DAGs in Airflow? ›

Airflow will execute the code in each file to dynamically build the DAG objects. You can have as many DAGs as you want, each describing an arbitrary number of tasks. In general, each one should correspond to a single logical workflow.

How do you automate Azure pipelines? ›

You can do this by using an Azure PowerShell task:
  1. On the Tasks tab of the release, add an Azure PowerShell task. Choose task version the latest Azure PowerShell version.
  2. Select the subscription your factory is in.
  3. Select Script File Path as the script type.
25 Oct 2022

How is CI CD automated? ›

CI/CD is a method to frequently deliver apps to customers by introducing automation into the stages of app development. The main concepts attributed to CI/CD are continuous integration, continuous delivery, and continuous deployment.

How do you automate Azure DevOps? ›

Once an account is created, sign in to Azure DevOps.
  1. Step 2: Create a new Project.
  2. Step 3: Create a Pipeline.
  3. Step 4: Link your code repo.
  4. Step 5: Select the task.
  5. Step 6: Configure Maven goals.
  6. Step 7: Pipeline run.
29 Apr 2022

How do you automate deployment process in Azure DevOps? ›

For Stage 1, select Azure App Service Deployment. Provide required details for the stage. In Add an Artifact, provide your required artifact. Finally, select Create a release, and it will deploy the artifact to the Azure app service.

What 2 types of pipelines can you create in Azure DevOps? ›

Azure DevOps supports two forms of version control - Git and Azure Repos.

Which is better Jenkins or Azure DevOps? ›

All depends on the needs of the team or the project. We know Jenkins is more flexible for building complex workflows and Azure DevOps is more scalable. A few CI tools are sufficient for all the mentioned scenarios. If we decide to use both tools, we should know that Azure Pipelines supports integration with Jenkins.

Is Azure pipelines part of Azure DevOps? ›

Azure Pipelines – being a DevOps tool, it definitely provides the Continuous Integration and Continuous Delivery (CI/CD), also known as CICD pipelines to support the build and release of the application from development to production. We will see this in action in the demo in the following sections.

Is CI CD same as DevOps? ›

DevOps is an agile development practice and mindset that uses agile principles (collaboration, communication, utilizing the right tools) to streamline software building, testing, and release. CI/CD is a DevOps tactic, which makes use of the right automated testing tools to implement agile development.

What is CI CD in DevOps agile? ›

Continuous integration, delivery and deployment are DevOps practices that aim to speed the software delivery without compromising on quality. By automating as many steps in the process as possible, CI/CD provides rapid feedback builds to shorten the time it takes to release software to users.

What is DevOps tools used for? ›

DevOps Tools

These tools automate manual tasks, help teams manage complex environments at scale, and keep engineers in control of the high velocity that is enabled by DevOps. AWS provides services that are designed for DevOps and that are built first for use with the AWS cloud.

Which programming language is best for Azure DevOps? ›

Java. Java is a high-level programming language DevOps engineers should learn. As a general-purpose programming language, Java is applicable for developing software for various platforms. DevOps teams often use this language for building web applications because they can quickly integrate it into existing codebases.

Can I able to integrate Azure DevOps with Azure? ›

Azure provides integration with popular open source and third-party tools and services – across the entire DevOps workflow. Spend less time integrating and more time delivering higher-quality software, faster.

What should I automate in DevOps? ›

DevOps Automation Tools
  • Infrastructure Automation.
  • Configuration Management.
  • Deployment Automation.
  • Performance Management.
  • Log management.
  • Monitoring.

What are the two common types of deployment strategies used in DevOps? ›

Various Types of Deployment Strategies
  • Blue/Green Deployment. In this type of deployment strategy, the new version of the software runs alongside the old version. ...
  • Canary Deployment. ...
  • Recreate Deployment. ...
  • Ramped Deployment. ...
  • Shadow Deployment. ...
  • A/B Testing Deployment.
3 May 2022

What are the different ways to integrate Azure DevOps? ›

How to set up and configure the Azure DevOps integration
  • Log into airfocus. ...
  • Enable the connection between an airfocus Workspace and a DevOps project by completing the following steps. ...
  • Log into your Azure DevOps account. ...
  • Start to configure your integration. ...
  • Configure integration mapping. ...
  • Configure additional options.

What are the 3 deployment modes that can be used for Azure? ›

Azure has several deployment models to consider when configuring a cloud solution. Specifically, there are three deployment models, and consist of what is called Public Cloud, Private Cloud, and Hybrid Cloud. Each deployment model has advantages and disadvantages based on the goals of the business use case.


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3. Managing Apache Airflow at Scale
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4. Manage Dags at scale: Dags versioning & package management
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5. Happy DAGs Happy Teammates How a little CICD can go a long way
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