Admissions Open for JANUARY Batch
Learn to automate, schedule, and optimize data workflows using Airflow, making complex processes seamless and efficient for real-world projects.
Days : Tue & Thu
Duration : 12 Hours
Timings: 10 AM - 12 PM IST
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12 Hours
8 - 9 PM IST
Tue & Thu
Workflow Automation & Airflow Basics
Learn to automate, schedule, and optimize data workflows using Airflow, making complex processes seamless and efficient for real-world projects.
Online Live Instructor-Led Learning
12 Hours
10 AM - 12 PM
Sat & Sun
By end of this course
Get stronger in
Master scheduling with cron expressions, understand backfilling for historical data, and manage DAGs.
Translate Python ETL scripts into Airflow DAGs, incorporating error handling, retries, and monitoring.
Configure Airflow for email alerts on task failures and success notifications, enhancing pipeline reliability.
Get familier with
Understand the need for workflow orchestration tools and the fundamental role of Apache Airflow.
Learn to install Airflow using Docker, navigate its web interface, and create simple DAGs with Python tasks.
Become familiar with Airflow operators, passing data between tasks using XComs, and configuring connections/variables.
New Batch Starts : jan 2026
Limited seats only 15 students per batch
Who Should Enroll?
This course is for learners looking to enhance their data engineering skills by automating workflows and mastering advanced orchestration tools.
Prerequisites
A strong foundation in Python, SQL, ETL pipeline development, and basic cloud concepts from Modules 1, 2, and 3.
Experience our course risk-free
We offer a 15-day money back guarantee
Prerequisite
A strong foundation in Python, SQL, ETL pipeline development, and basic cloud concepts from Modules 1, 2, and 3.
Who Should Enroll?
This course is for learners looking to enhance their data engineering skills by automating workflows and mastering advanced orchestration tools.
By the end of this course
Get Stronger in
- 1.Master scheduling with cron expressions, understand backfilling for historical data, and manage DAGs.
- Translate Python ETL scripts into Airflow DAGs, incorporating error handling, retries, and monitoring.
- Configure Airflow for email alerts on task failures and success notifications, enhancing pipeline reliability.
Get Familiar in
- Understand the need for workflow orchestration tools and the fundamental role of Apache Airflow.
- Learn to install Airflow using Docker, navigate its web interface, and create simple DAGs with Python tasks.
- Become familiar with Airflow operators, passing data between tasks using XComs, and configuring connections/variables.
Course Contents
Content
Understand why we need orchestration tools. Learn what Apache Airflow is and its role. Explore real-world scheduling challenges Airflow solves.
Topics
Install Airflow using Docker (simplified setup). Navigate the Airflow web interface. Understand key components: DAGs, tasks, scheduler. Start the Airflow services.
Content
Create a simple DAG with Python tasks. Set up task dependencies. Configure schedule intervals (daily, hourly). Run your first DAG in Airflow.
Content
Use Python Operator to run Python functions. Use Bash Operator for shell commands. Chain multiple tasks together. View task logs in Airflow UI.
Content
Understand cron expressions for scheduling. Set start dates and intervals. Learn about backfilling for historical runs. Pause and unpause DAGs.
Content
Use XComs to share data between tasks. Understand XCom limitations. Implement simple data passing workflows. Learn when to use XComs vs external storage.
Content
Configure database connections in Airflow. Set up API connections. Use Airflow Variables for configuration. Keep credentials secure.
Content
Convert your Python ETL script into an Airflow DAG. Break pipeline into extract, transform, load tasks. Add error handling and retries. Monitor execution in UI.
Content
Configure email settings in Airflow. Set up alerts on task failure. Send success notifications. Create custom alert messages with task information.
Content
Keep DAGs simple and readable. Use meaningful task and DAG names. Add documentation strings. Avoid common mistakes. Organize DAG files properly.
Content
Read and understand Airflow logs. Use the Airflow CLI for testing. Clear failed task instances. Troubleshoot common DAG errors.
Content
Build a production-ready Airflow pipeline: Create a DAG that runs daily, extracts from API and database, transforms data, loads to cloud warehouse, sends email alerts on success/failure, includes proper logging.
Content
Understand why we need orchestration tools. Learn what Apache Airflow is and its role. Explore real-world scheduling challenges Airflow solves.
Topics
Install Airflow using Docker (simplified setup). Navigate the Airflow web interface. Understand key components: DAGs, tasks, scheduler. Start the Airflow services.
Content
Create a simple DAG with Python tasks. Set up task dependencies. Configure schedule intervals (daily, hourly). Run your first DAG in Airflow.
Content
Use Python Operator to run Python functions. Use Bash Operator for shell commands. Chain multiple tasks together. View task logs in Airflow UI.
Content
Understand cron expressions for scheduling. Set start dates and intervals. Learn about backfilling for historical runs. Pause and unpause DAGs.
Content
Use XComs to share data between tasks. Understand XCom limitations. Implement simple data passing workflows. Learn when to use XComs vs external storage.
Content
Configure database connections in Airflow. Set up API connections. Use Airflow Variables for configuration. Keep credentials secure.
Content
Convert your Python ETL script into an Airflow DAG. Break pipeline into extract, transform, load tasks. Add error handling and retries. Monitor execution in UI.
Content
Configure email settings in Airflow. Set up alerts on task failure. Send success notifications. Create custom alert messages with task information.
Content
Keep DAGs simple and readable. Use meaningful task and DAG names. Add documentation strings. Avoid common mistakes. Organize DAG files properly.
Content
Read and understand Airflow logs. Use the Airflow CLI for testing. Clear failed task instances. Troubleshoot common DAG errors.
Content
Build a production-ready Airflow pipeline: Create a DAG that runs daily, extracts from API and database, transforms data, loads to cloud warehouse, sends email alerts on success/failure, includes proper logging.
What is covered: Vectors, matrices, operations like addition and multiplication.
Application: Data representation, image processing, neural networks.
Example:
1. Image as Matrix: A grayscale image is a matrix of pixel values. Neural networks process these matrices to recognize objects.
2. Matrix Multiplication: Used to combine weights and inputs in every layer of a neural network.
What is covered: Studying change (derivatives), finding minimum/maximum values.
Application: Training models by minimizing error (loss), adjusting weights.
Example:
1. Gradient Descent: The process of finding the best model parameters by moving in the direction that reduces error, like rolling a ball downhill.
2. Backpropagation: Calculating how much each weight in a neural network should change to improve predictions.
What is covered: Measuring uncertainty, analyzing data, making predictions.
Application: Predicting outcomes, evaluating models, handling randomness.
Example:
1. Spam Detection: Using probability to decide if an email is spam based on words it contains.
2. Model Evaluation: Calculating accuracy, precision, and recall to see how well a model performs.
What is covered: Logic, graphs, counting, combinations.
Application: Social networks, recommendation systems, logical reasoning.
Example:
1.Friend Recommendations: Using graph theory to suggest new friends on social media.
2.Counting Possibilities: Calculating how many ways a password can be formed.
This section includes a comprehensive evaluation covering all course topics, designed to measure understanding and mastery of key mathematical concepts presented throughout the course
Phase 1: From Coders to Creators
You’ll set up your professional coding environment by installing VS Code and Jupyter, introduce ChatGPT as a coding co-pilot, and learn to build effective prompts to generate code, establishing a productivity mindset for modern development.
Learn to reframe coding as building blocks for real applications by working with CSV, JSON, and image datasets from relatable domains like YouTube, food, and books, developing a system-level thinking approach.
Master abstraction, reusability, and clarity in logic by breaking down real-world use cases like meal planners and birthday reminders into modular code components using functions, loops, and conditions.
Build a functional CLI project such as a task tracker or GPA calculator, solving real-world problems like smart schedulers or basic calculators while developing ownership and confidence in your coding abilities