Unlock your potential with our Advanced Data Pipeline Course using Apache Airflow! This course focuses on cloud integration, data quality management, and error handling, equipping you with the skills needed to excel in data engineering. Transform your career and create sophisticated data pipelines that meet industry standards!

Advanced Data Pipeline Course with Apache Airflow

# 🚀 Transform Your Data Engineering Skills with Our Advanced Data Pipeline Course!

Embark on a journey to revolutionize your data engineering skills with our cutting-edge course designed for intermediate data engineers. This course will empower you to master advanced data pipeline creation using Apache Airflow, focusing on seamless cloud integration and impeccable data quality management. Prepare to challenge conventional workflows and elevate your career as you delve into innovative strategies and hands-on projects that will set you apart in the data engineering landscape.


### Who is it for?

This course is designed for intermediate data engineers eager to enhance their skills and tackle real-world challenges. If you currently feel stuck with basic Airflow functionalities and want to elevate your career by mastering advanced data pipeline creation, this course is your game-changer! Imagine transforming your projects with seamless cloud integrations and robust data quality checks.

- Data Engineers seeking advanced skills
- Cloud Service Providers wanting to improve integration techniques
- Data Analysts looking to enhance data quality management
- Project Managers aiming for better workflow optimization

**Recommended skill level: Intermediate data engineers looking to advance their skills**


### Prerequisites

Before diving in, ensure you have a basic knowledge of Apache Airflow and familiarity with cloud services like AWS and Google Cloud. Understanding data engineering concepts and having experience with Python programming will set a strong foundation for your transformative journey.

- 🛠️ Basic knowledge of Apache Airflow
- ☁️ Familiarity with cloud services like AWS and Google Cloud
- 📊 Understanding of data engineering concepts
- 🐍 Experience with Python programming

### What's inside

This course is packed with hands-on projects and modules designed to equip you with the skills needed for real-world applications.

- Cloud Service Integration Unleashed
- Elevating Data Quality Standards
- Crafting Robust Error Handling Mechanisms
- Performance Optimization for Big Data
- Mastering Advanced DAG Features
- Final Integration and Testing

**Interactive Quizzes**

Engage in self-assessment quizzes at the end of each module to reinforce your learning and ensure comprehension of key concepts.

**Homework**

Get ready for exhilarating challenges that will propel your growth! Each assignment is crafted to mimic real-world scenarios, ensuring you apply what you learn. Here’s a sneak peek:
- Develop a working DAG that integrates with both AWS S3 and Google Cloud Storage.
- Showcase a DAG that includes comprehensive data quality checks.
- Present a DAG that incorporates advanced error handling strategies.

**Practical Project**

Develop an advanced data pipeline using Apache Airflow that integrates with AWS S3 and Google Cloud Storage, implementing data quality checks and error handling mechanisms over 4-8 weeks.

**Before You Start**

Before you start, ensure you have the necessary prerequisites and a conducive environment to practice your new skills. Familiarize yourself with Apache Airflow's interface and features to maximize your learning experience.

**Books to Read**

Recommended readings include 'Data Pipelines with Apache Airflow' and 'Cloud Data Engineering'. These will provide you with additional insights and deepen your understanding of the course material.

**Glossary**

A comprehensive glossary will be provided to help you navigate technical terms and concepts throughout the course.


### What will you learn

By the end of this course, you will confidently create sophisticated data pipelines integrated with AWS S3 and Google Cloud Storage, significantly enhancing your employability.

- Create advanced data pipelines using Apache Airflow
- Implement robust data quality checks
- Develop advanced error handling strategies
- Optimize performance for large datasets
- Employ advanced DAG features for complex workflows

**Time to complete this course: This course is designed to be completed in 8-10 weeks, with just 15-20 hours of dedicated study per week.**


### 🚀 Enroll Now and Transform Your Career!

Familiarity with Apache Airflow's core concepts and functionalities is crucial for understanding advanced features and integrations, enabling you to build efficient data pipelines.

Basic Knowledge of Apache Airflow

Understanding platforms like AWS and Google Cloud is essential for integrating these services into your workflows, ensuring seamless data handling and storage.

Familiarity with Cloud Services

A solid grasp of data engineering principles will help you appreciate the complexities of data pipelines and the importance of data quality and error handling.

Understanding of Data Engineering Concepts

Proficiency in Python is vital, as it is the primary language used in Apache Airflow for task automation and data manipulation.

Experience with Python Programming

## Transform Your Data Engineering Skills with Our Advanced Data Pipeline Course!
Embark on a journey to revolutionize your data engineering skills with our cutting-edge course designed for intermediate data engineers. This course will empower you to master advanced data pipeline creation using Apache Airflow, focusing on seamless cloud integration and impeccable data quality management. Prepare to challenge conventional workflows and elevate your career as you delve into innovative strategies and hands-on projects that will set you apart in the data engineering landscape.

### Who is it For?
This course is designed for intermediate data engineers eager to enhance their skills and tackle real-world challenges. If you currently feel stuck with basic Airflow functionalities and want to elevate your career by mastering advanced data pipeline creation, this course is your game-changer! Imagine transforming your projects with seamless cloud integrations and robust data quality checks.

#### Skill Level
Intermediate data engineers looking to advance their skills.

#### Audience
- Data Engineers seeking advanced skills
- Cloud Service Providers wanting to improve integration techniques
- Data Analysts looking to enhance data quality management
- Project Managers aiming for better workflow optimization

### Prerequisites
Before diving in, ensure you have a basic knowledge of Apache Airflow and familiarity with cloud services like AWS and Google Cloud. Understanding data engineering concepts and having experience with Python programming will set a strong foundation for your transformative journey.

#### Requirements
- Basic knowledge of Apache Airflow
- Familiarity with cloud services like AWS and Google Cloud
- Understanding of data engineering concepts
- Experience with Python programming

### What's Inside?
This course is packed with hands-on projects and modules designed to equip you with the skills needed for real-world applications.

#### Modules
- Cloud Service Integration Unleashed
- Elevating Data Quality Standards
- Crafting Robust Error Handling Mechanisms
- Performance Optimization for Big Data
- Mastering Advanced DAG Features
- Final Integration and Testing

#### Quizzes
Engage in self-assessment quizzes at the end of each module to reinforce your learning and ensure comprehension of key concepts.

#### Assignments
Get ready for exhilarating challenges that will propel your growth! Each assignment is crafted to mimic real-world scenarios, ensuring you apply what you learn. Here’s a sneak peek:
- Develop a working DAG that integrates with both AWS S3 and Google Cloud Storage.
- Showcase a DAG that includes comprehensive data quality checks.
- Present a DAG that incorporates advanced error handling strategies.

#### Practical Project
Develop an advanced data pipeline using Apache Airflow that integrates with AWS S3 and Google Cloud Storage, implementing data quality checks and error handling mechanisms over 4-8 weeks.

#### Before You Start
Before you start, ensure you have the necessary prerequisites and a conducive environment to practice your new skills. Familiarize yourself with Apache Airflow's interface and features to maximize your learning experience.

#### Books to Read
Recommended readings include 'Data Pipelines with Apache Airflow' and 'Cloud Data Engineering'. These will provide you with additional insights and deepen your understanding of the course material.

#### Glossary
A comprehensive glossary will be provided to help you navigate technical terms and concepts throughout the course.

### What Will You Learn?
By the end of this course, you will confidently create sophisticated data pipelines integrated with AWS S3 and Google Cloud Storage, significantly enhancing your employability.

#### Skills
- Create advanced data pipelines using Apache Airflow
- Implement robust data quality checks
- Develop advanced error handling strategies
- Optimize performance for large datasets
- Employ advanced DAG features for complex workflows

### Time to Complete
This course is designed to be completed in 8-10 weeks, with just 15-20 hours of dedicated study per week.

### Enroll Now and Transform Your Career!

An open-source platform to programmatically author, schedule, and monitor workflows, enabling complex data pipeline management.

The process of connecting applications and services in the cloud to streamline data flow and enhance functionality.

The overall utility of a dataset, determined by factors like accuracy, completeness, consistency, and reliability.

Techniques used to manage and respond to errors in data processing, ensuring workflows remain operational.

A representation of tasks in a workflow where each task is a node, and edges indicate dependencies, ensuring no cycles.

Amazon Web Services Simple Storage Service, a scalable storage solution for data in the cloud, commonly used in data pipelines.

A service for storing and accessing data in the Google Cloud, providing high availability and scalability.

A series of data processing steps that involve moving data from one system to another, often involving transformation.

The process of ensuring data meets specific criteria or standards before it is processed or used.

User-defined tasks in Apache Airflow that encapsulate specific logic or functionality, enhancing workflow capabilities.

A method of executing multiple tasks simultaneously to improve performance and reduce processing time.

Mechanisms that alert users or systems about errors or important events in a data pipeline.

The process of optimizing a system to improve its efficiency and speed, especially for large datasets.

Relationships between tasks in a workflow that determine the order in which they must be executed.

Strategies for automatically re-executing failed tasks in a workflow to enhance reliability.

A comprehensive testing method that verifies the complete functionality of a data pipeline from start to finish.

The capability of a system to handle a growing amount of work or its potential to accommodate growth.

A self-assessment method where students document their learning experiences and insights throughout the course.

A collaborative evaluation process where students assess each other's work based on defined criteria.

The process of converting data from one format or structure into another to meet specific requirements.

A set of rules and protocols for building and interacting with software applications, essential for cloud integrations.

The execution of a series of jobs in a program on a computer without manual intervention.

The process of combining data from different sources into a unified view, crucial for analytics and reporting.

The coordination of tasks and processes in a workflow to ensure efficient execution and tracking.

Understanding cloud service APIs is crucial for any data engineer. APIs (Application Programming Interfaces) allow different software applications to communicate with each other. In the context of cloud services, APIs provide a way to access and manage resources such as storage, compute, and more. For example, AWS S3 APIs enable you to upload, download, and manage files in the cloud.

Before you can interact with cloud APIs, you need to authenticate your requests. AWS and Google Cloud offer several authentication methods, including:
- **Access Keys**: Used primarily in AWS, consisting of an Access Key ID and a Secret Access Key.
- **OAuth 2.0**: Commonly used in Google Cloud, allowing users to grant limited access to their resources without sharing credentials.

When transferring data between your local environment and cloud storage, understanding the data transfer protocols is essential. Common protocols include:
- **HTTP/HTTPS**: The foundation of data transfer on the web, used for RESTful APIs.
- **S3 Transfer Acceleration**: A feature in AWS S3 that speeds up uploads by routing data through Amazon's network.

To connect Apache Airflow with AWS S3 or Google Cloud Storage, follow these steps:
1. **Install Required Libraries**: Ensure you have the necessary Python libraries installed, such as `boto3` for AWS and `google-cloud-storage` for Google Cloud.
2. **Configure Airflow Connections**: In Airflow, navigate to the Admin tab and set up connections for both AWS and Google Cloud, entering the required authentication details.
3. **Create a Basic DAG**: Write a simple Directed Acyclic Graph (DAG) that includes tasks for uploading and downloading files from the cloud.

🔑 **Tip:** Always keep your API keys and access credentials secure and do not hard-code them in your scripts. Use environment variables or secret management tools instead.

While working with cloud APIs, it's easy to run into issues. Here are some common pitfalls:
- **Incorrect Permissions**: Ensure that your API keys have the necessary permissions to access the resources you need.
- **API Rate Limits**: Be aware of the limits imposed by cloud providers on API requests to avoid throttling.

Consider a scenario where a data engineer needs to automate the process of backing up data from a local server to AWS S3. By understanding the AWS S3 API, the engineer can write a script that uses the `boto3` library to upload files to S3, ensuring that backups are performed regularly and efficiently.

To solidify your understanding, complete the following exercise:
1. Set up an AWS S3 bucket and configure access permissions.
2. Write a Python script using `boto3` to upload a file from your local machine to the S3 bucket.
3. Test the script to ensure that the file is successfully uploaded.

Homework: Cloud API Integration

Navigating Cloud APIs: Your Gateway to Integration

Cloud API Navigation Quiz

To begin, let’s understand the significance of configuring Apache Airflow for cloud integration. As data engineers, our ability to connect various cloud services can dramatically enhance the efficiency and reliability of our data pipelines.

Configuring connections in Apache Airflow is essential for enabling communication between your workflows and cloud services. Here’s how to do it:

Access the Airflow UI and navigate to the 'Admin' tab.

Click on 'Create' to add a new connection.

Fill in the required fields such as Connection ID, Connection Type (e.g., S3, Google Cloud), and authentication details.

Test the connection to ensure it works as expected.

By following these steps, you can easily configure connections for various cloud services.

Authentication is crucial for securing access to cloud services. Here are common methods:

Applying OAuth 2.0 for Google Cloud Storage.

Setting up API keys for service accounts.

Make sure to choose the method that best fits your organization’s security protocols.

Once your connections and authentication are set up, the next step is to create your first DAG.

from airflow import DAG\nfrom airflow.operators.s3_operator import S3CreateObjectOperator\nfrom airflow.operators.dummy_operator import DummyOperator\nfrom datetime import datetime\n\ndefault_args = {\n    'owner': 'airflow',\n    'start_date': datetime(2023, 1, 1),\n}\n\ndag = DAG('my_first_s3_dag', default_args=default_args, schedule_interval='@daily')\n\nstart = DummyOperator(task_id='start', dag=dag)\ncreate_s3_object = S3CreateObjectOperator(\n    task_id='create_s3_object',\n    bucket_name='my_bucket',\n    object_name='my_file.txt',\n    data='Hello World!',\n    aws_conn_id='my_aws_connection',\n    dag=dag\n)\n\nstart >> create_s3_object

In this code snippet, we create a simple DAG that uploads a file to an S3 bucket. This serves as a foundational step for more complex workflows.

When setting up Airflow for cloud integration, consider the following best practices:

Always use environment variables for sensitive information.

Regularly update your Airflow and cloud SDKs.

Monitor your DAGs for performance issues.

🔑 Best Practice Tip: Use Airflow's built-in logging to monitor task execution and troubleshoot issues effectively.

Homework: Cloud Integration Setup

Setting Up Airflow for Cloud Integration

Cloud Integration Quiz

A Directed Acyclic Graph (DAG) is a fundamental concept in Apache Airflow, representing a collection of tasks with defined relationships. Each task in a DAG must be executed in a specific order, which is crucial for maintaining the integrity of your data processing pipeline.

DAGs are composed of nodes (tasks) and edges (dependencies).

They must not contain cycles, meaning no task can depend on itself indirectly.

DAGs can be scheduled to run at specified intervals, making them ideal for periodic data processing.

📝 Key Insight: Understanding DAG structure is essential for building efficient data pipelines.

Integrating cloud services into your DAG allows you to leverage the power of scalable storage and processing capabilities. In this section, you will learn how to implement cloud tasks, such as uploading to AWS S3 or downloading from Google Cloud Storage.

from airflow import DAG
from airflow.providers.amazon.aws.transfers.s3_to_gcs import S3ToGCSOperator
from datetime import datetime

default_args = {
    'owner': 'airflow',
    'start_date': datetime(2023, 10, 1),
}

with DAG('my_cloud_dag', default_args=default_args, schedule_interval='@daily') as dag:
    upload_to_s3 = S3ToGCSOperator(
        task_id='upload_to_s3',
        bucket='my-bucket',
        object_name='data/file.csv',
        gcp_conn_id='google_cloud_default',
        aws_conn_id='aws_default'
    )
    upload_to_s3

Once your DAG is implemented, it's crucial to test its functionality. This ensures that all tasks execute as expected and that dependencies are respected.

Use the Airflow UI to trigger your DAG manually.

Monitor task execution and check logs for any errors.

Verify that data is correctly transferred between cloud services.

🔍 Tip: Always test your DAG in a development environment before deploying to production to catch potential issues early.

Task dependencies dictate the order in which tasks are executed in your DAG. Understanding this concept is vital to ensure that your data flows correctly through the pipeline.

Dependencies can be defined using the bitwise operators '>>' and '<<'.

Ensure that tasks that require outputs from previous tasks are correctly linked.

"The key to a successful data pipeline is not just the tasks you perform, but the order in which you perform them."

⚠️ Common Mistake: Failing to define task dependencies can lead to data integrity issues and unexpected results.

Homework: Create Your First Cloud-Based DAG

Crafting Your First Cloud-Based DAG

DAG Creation Quiz

Cloud connectivity is crucial for data engineers working with Apache Airflow. It allows your data pipelines to interact with cloud services like AWS S3 and Google Cloud Storage. A solid understanding of how connectivity works will help you avoid common pitfalls.

🔍 **Tip:** Always check your cloud service's API documentation for the latest updates on connectivity methods and requirements.

Testing Methodologies for Cloud Connectivity

To ensure that your Airflow tasks can communicate effectively with cloud services, you need to implement robust testing methodologies. Here are some common approaches:

Ping the cloud service to check basic connectivity.

Use Airflow's built-in connections to validate cloud service access.

Perform data upload and download tests to confirm functionality.

Log detailed error messages for troubleshooting.

Validation checks are essential to ensure that your Airflow tasks can reliably connect to cloud services. Here’s how to implement them:

Create a connection in Airflow for your cloud service.

Use the Airflow UI to test the connection.

Develop a simple DAG that performs a read and write operation to the cloud service.

Log the results of these operations to monitor connectivity.

Even with proper setup, issues can arise. Here are some common problems and their solutions:

**Authentication Errors:** Ensure your credentials are correctly configured.

**Timeouts:** Check your network settings and cloud service status.

**Permission Denied:** Verify that your IAM roles and permissions are correctly set.

Documentation is key to maintaining robust cloud connectivity. Here’s what to include:

Common issues encountered and their resolutions.

Logs of successful and failed connectivity tests.

Hands-On Exercise: Validate Cloud Connectivity

Now it’s time to put your skills to the test! Follow these steps:

Create a simple DAG that uploads a test file to S3.

Log the results of your upload and troubleshoot any issues.

Document your process for future reference.

"Good documentation is key to maintaining a successful data pipeline." - Data Engineering Expert

Real-World Application: Ensuring Robustness in Data Pipelines

In real-world scenarios, ensuring cloud connectivity is vital for operational success. Organizations rely on data pipelines to move data between various services, and any disruption can lead to significant issues. By mastering connectivity testing, you equip yourself with the skills necessary to ensure that your data workflows remain functional and reliable.

Homework: Validate Cloud Connectivity

Testing Cloud Connectivity: Ensuring Robustness

Testing Cloud Connectivity Quiz

Cloud service integration is a critical skill for data engineers, especially when working with platforms like Apache Airflow. The ability to connect to services like AWS S3 and Google Cloud Storage seamlessly can significantly improve the performance and reliability of your data pipelines.

Security Considerations for Cloud Integration

When integrating cloud services, security should be a top priority. Here are some key considerations:
- **Authentication**: Use robust authentication methods such as OAuth or IAM roles to secure access to cloud services.
- **Data Encryption**: Ensure that data is encrypted both in transit and at rest to protect sensitive information.

🛡️ Security Tip: Always use least privilege access principles when granting permissions to your cloud services.

Optimizing Performance in Cloud Workflows

Performance optimization is essential when dealing with large datasets. Here are some strategies:
- **Batch Processing**: Instead of processing data one record at a time, consider batch processing to reduce overhead.
- **Parallel Processing**: Implement parallel task execution in Airflow to speed up data processing.

Data integrity is crucial in any data pipeline. Here’s how to ensure it:
- **Validation Checks**: Implement validation checks in your DAGs to verify data quality at each stage of processing.
- **Logging and Monitoring**: Use logging to keep track of data changes and monitor for any anomalies.

"Data integrity is the cornerstone of any successful data pipeline." - Data Engineering Expert

Real-World Applications of Best Practices

Applying best practices in cloud service integration can lead to significant improvements in project outcomes. For example, a financial services company improved its data processing speed by 40% after implementing parallel processing and robust error handling in its Airflow workflows.

Hands-On Exercise: Implementing Best Practices

To solidify your understanding, create a mini-project that integrates AWS S3 with Apache Airflow. Focus on implementing security measures, optimizing performance, and ensuring data integrity. Document your process and outcomes.

This exercise will help you apply the best practices discussed and prepare you for the practical project ahead.

Homework: Cloud Integration Best Practices

Best Practices for Cloud Service Integration

Cloud Integration Best Practices Quiz

In the realm of data engineering, the ability to present your work effectively is just as crucial as the technical skills you possess. This lesson focuses on the art of showcasing your cloud integration DAG, which is a culmination of your hard work and learning throughout the course.

Presenting your work is not merely about displaying your DAG; it's about telling a story. Here are some key strategies to enhance your presentation skills:

Start with a clear introduction that outlines the objectives of your presentation.

Use visual aids to help explain complex concepts.

Practice your delivery to ensure smooth transitions between topics.

Engage your audience with questions and encourage interaction.

Conclude with a summary that reinforces the key points of your presentation.

🌟 **Tip:** Practice in front of a mirror or record yourself to identify areas for improvement.

Feedback is essential for growth. When presenting your DAG, be open to receiving constructive criticism. Here’s how to make the most of feedback:

Encourage your peers to provide specific feedback on both strengths and areas for improvement.

Take notes during the feedback session to reflect on later.

Ask clarifying questions if you don’t understand a piece of feedback.

Remember, the goal is to improve your work and presentation skills.

As you present your cloud integration DAG, consider the following aspects to evaluate its effectiveness:

How well does the DAG integrate with cloud services like AWS S3 and Google Cloud Storage?

Are the data quality checks implemented effectively?

Is the error handling robust and well-documented?

Does the DAG perform efficiently with large datasets?

Listening to your peers can provide valuable insights. Here’s how to leverage their perspectives:

Engage in discussions post-presentation to explore different approaches.

Consider their feedback and how it may apply to future projects.

Build a network of peers who can support each other in future endeavors.

"Feedback is the breakfast of champions." — Ken Blanchard

Hands-On Exercise: Prepare Your Presentation

Before the class, prepare a 10-minute presentation of your cloud integration DAG. Focus on the key aspects discussed in this lesson. Use the following checklist to ensure you cover all necessary points:

Prepare visual aids (slides, diagrams, etc.).

Gather any necessary documentation to support your presentation.

Prepare to answer questions from your peers.

Homework: Refine Your Presentation

Showcase: Your Cloud Integration DAG

Presentation Skills Quiz

Dive deep into the world of cloud service integration as you learn to seamlessly connect Apache Airflow with leading cloud platforms. This module will equip you with the skills to navigate the complexities of cloud services, ensuring your data pipelines are robust and scalable.

Cloud Service Integration Unleashed

Data quality issues can manifest in various forms, impacting the reliability of your data pipeline. Here are some common types:

**Data Duplication**: When the same data is recorded multiple times, leading to inflated metrics.

**Data Inconsistency**: Discrepancies in data values across different sources.

**Missing Values**: Lack of data in critical fields, which can skew analysis.

**Outliers**: Data points that deviate significantly from the norm, potentially indicating errors.

**Data Type Mismatches**: When the data type does not match the expected format, causing processing errors.

Understanding the impact of these issues is crucial for data engineers. Poor data quality can lead to:

**Misleading Insights**: Inaccurate data can result in faulty analytics and decision-making.

**Increased Costs**: Time and resources spent on correcting errors can be significant.

**Reputational Damage**: Organizations relying on faulty data can lose credibility.

**Operational Disruptions**: Data quality issues can cause system failures or delays.

Methods for Identifying Data Quality Problems

There are several effective methods to identify data quality problems:

**Data Profiling**: Analyze data to understand its structure, content, and quality.

**Automated Data Quality Checks**: Implement automated tests in your data pipeline to catch errors early.

**Regular Audits**: Schedule periodic reviews of data quality metrics and issues.

**User Feedback**: Encourage users to report data issues they encounter.

Hands-On Exercise: Identifying Data Quality Issues

To solidify your understanding, let's conduct a hands-on exercise. You'll analyze a sample dataset and identify potential data quality issues.

Perform data profiling to summarize the dataset.

Identify at least three data quality issues based on the criteria discussed.

Document your findings and suggest potential solutions.

🔍 **Tip**: Always validate your data sources before integration to minimize the risk of data quality issues.

Data Quality Analysis Homework

Identifying Common Data Quality Issues

Data Quality Issues Quiz

Apache Airflow is a powerful tool for orchestrating workflows, but its true potential is unlocked when you customize it to suit your specific needs. Custom operators allow you to define unique tasks that can validate data quality, ensuring that your data meets the required standards before it moves through the pipeline.

Airflow operators are the building blocks of your workflows. Each operator represents a single task in a Directed Acyclic Graph (DAG). Understanding how to leverage existing operators and create your own is crucial for building robust data pipelines.

- PythonOperator: Calls Python functions.

- Custom Operators: Tailored to your specific use cases.

🔑 Tip: Always start by exploring existing operators before creating custom ones. You might find that an existing operator meets your needs!

Custom operators in Airflow can be created by extending the BaseOperator class. This allows you to define specific validation logic that can be reused across multiple DAGs.

Creating Custom Airflow Operators for Validation

This module focuses on the critical aspect of data quality within your Airflow tasks. You'll learn to implement effective data validation techniques, ensuring that your data pipelines produce reliable and accurate outputs.

Data Engineering on Azure

Designing Data-Intensive Applications

Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing

The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling

Building Data Streaming Applications with Apache Kafka

Airflow in Action

Data Quality: The Accuracy Dimension

Cloud Data Management and Storage

Python for Data Analysis

The Pragmatic Programmer: Your Journey To Mastery

Dive into these transformative reads and let their insights guide your journey in mastering data engineering. Your future self will thank you!