Objectives
With the ever-increasing growth of IT infrastructure, Apache is becoming the number one choice for organizations across the globe. It has emerged as the leading workflow management tool in the market. Apache Airflow is used as a platform for data engineering pipelines; it was introduced in 2014 by the Airbnb Company under the principle of "configuration as code". Airflow was written in Python, so the workflows were created via Python scripts. This tool is capable of creating, organizing, and monitoring workflows. Data engineers use it for orchestrating workflows.
Which are the benefits of Apache Airflow?
- It has a large community of active users
- Its graphical UI helps in checking the status of ongoing and completed tasks
- It is an Open-source platform which is free to use
- It is based on Python
- It is highly scalable as it can execute thousands of tasks per day
- Its graphical UI helps in monitoring and managing the workflows
- It o only requires beginner-level knowledge of python
- It is based on standard Python and is easy to use
- Airflow is written in Python and used as a platform for data engineering pipelines.
- Apache Airflow was introduced in 2014 by the Airbnb Company under the principle of "configuration as code".
- Apache Airflow allows the companies in scheduling, execute, and monitoring complex workflows.
- Adobe, big fish and Adyen are real-world Apache Airflow use cases.
- Apache Airflow Course will prepare the professionals to install and configure Apache Airflow.
- The aspirants of Apache Airflow Online Training are required to carry prior work experience in programming or scripting and working experience in Python.
What problems does Airflow solve?
Apache Airflow, an open-source platform, allows companies to schedule, execute, and monitor complex workflows. Has emerged as one of the most powerful open source data pipeline platforms in the marketplace, it is designed to provide us with a long range of features for creating the architecture of complex workflows. The versatility of this platform allows the users to set up any type of workflow.
Get Apache Airflow Certified to become a master of this open-source platform!
Which are the Best Practices of Apache Airflow?
- The person in charge is notified and the event is logged if the task is not completed within the defined line. Service Level Agreements (SLA) help the companies to understand the cause of the delay.
- The priority_weight parameter is used to control the priority of workflows. It helps us in avoiding temporary workflows that can occur when multiple workflows compete for execution.
- As a context variable is passed to each workflow, usage of variables has become very important for the companies. It makes the DAG flexible.
- Workflows should be kept updated as it is based on python code. It will help the professionals to run the python language efficiently.
- A proper purpose for DAG is required. The purpose has to be defined before the DAG is created. It needs to be defined with a clear vision with a minimum complexity level.
Which Are The Main Use Cases of Apache Airflow?
Adobe, big fish and Adyen are real-world Apache Airflow use cases. Apache Airflow use cases are not valid for every single scenario. There are technical considerations needed to deal with some use cases. However, we are going to discuss seven use cases today.
- Airflow is beneficial for batch jobs.
- Organizing, monitoring, and executing workflows automatically.
- Airflow can be used efficiently when the organizing, and scheduling of data pipeline workflows is pre-scheduled for a specific time interval.
- Airflow can also be used for the ETL pipelines or getting data from multiple sources or performing data transformation.
- Airflow can be used for training the machine learning models, and also triggering jobs like a SageMaker.
- Apache airflow can be used when we need to take a backup from DevOps tasks or store results in the Hadoop cluster.
- Airflow can be used to generate reports.
Top 15 job options for the Apache Airflow professionals
- Senior Data Engineer - SQL/ Python/ Apache Airflow
- Python Developer - SQL/SSIS/Apache Airflow
- Data Engineer - ETL/Python/Airflow
- AIRFLOW TECHNICAL LEAD
- Java/j2ee Full Stack Developer
- Python Developer with Spark Experience
- Software Development Engineer - III - Data Intelligence
- Advanced Embedded System Engineering Application Developer
- Python Backend Developer
- Java Full-stack Developer
- Data – Architect
- JBOSS FUSE DEVELOPER
- Linux Cloud Engineer
- Data Engineer - Azure Databricks
- Big Data Developer/Engineer
Which are the key benefits of Apache Airflow Training?
Apache Airflow Training is a must-have course for the professionals who want to learn the techniques of learning everything they need to know for working as an Apache Airflow Expert. It is designed to teach the process of dealing with DAGs, Tasks, Operators, Workflows, and other core functionalities; use Apache Airflow in a Big Data ecosystem with the use of PostgreSQL, Hive, Elasticsearch; apply advanced concepts of Apache Airflow such as XCOMs, Branching, and SubDAGs; use Docker with Airflow and different executors; implement solutions using Airflow to real data processing problems; create plugins to add functionalities to Apache Airflow. It will prepare the professionals to install and configure Apache Airflow.
Who Can Pursue Apache Airflow Training?
- Data engineers who want to deploy their pipelines with the use of Airflow
- Engineers who want to switch their careers from conventional schedulers
- IT professionals who are intended to add demanding technology to their resume
- IT professionals who want to learn basic and advanced concepts of Apache Airflow
Is there any defined set of prerequisites for the Apache Airflow Training?
The aspirants of this course are required to carry prior work experience in programming or scripting. Working experience in Python will help you immensely. If you are interested in pursuing this course, you are supposed to have at least 8 gigabytes of memory and VirtualBox installed. A VM of 3 GB needs to be downloaded.