Big Data

Airflow

Airflow Training: Apache Airflow is a powerful workflow automation tool that helps teams schedule, manage, and monitor…

accentfuture
25
45h
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Airflow Training:

Apache Airflow is a powerful workflow automation tool that helps teams schedule, manage, and monitor data pipelines efficiently. It allows users to define workflows as code, making them dynamic, scalable, and easy to maintain. With Airflow, businesses can automate ETL processes, orchestrate machine learning models, and streamline data operations.

It provides a web-based UI to track, troubleshoot, and manage tasks in real time. Built-in scheduling and dependency management ensure tasks run in the correct order without failures. Airflow easily integrates with databases, APIs, cloud platforms (AWS, GCP, Azure), and big data tools like Spark and Kafka.

Widely adopted by enterprises, startups, and cloud service providers, Apache Airflow is a must-have skill for data engineers, analysts, and DevOps professionals. It supports parallel execution, making workflows faster and more efficient. With Airflow, teams can reduce manual work, optimize performance, and scale workflows effectively

In this Airflow course, you’ll learn Airflow from scratch, including how to set up and use Apache Airflow to automate and manage workflows. You’ll discover how to schedule tasks, connect with cloud services, and work with big data tools. Through hands-on practice, you’ll build real-world ETL (Extract, Transform, Load) pipelines, automate workflows, and improve performance.By the end of the Airflow Training, you’ll be able to use Airflow to simplify data workflows and automate repetitive tasks with ease.

What Will You Learn?

Module 1: Introduction to Apache Airflow: What is Apache Airflow?, Why Use Airflow for Workflow Automation?, Key Features and Benefits, Understanding DAGs (Directed Acyclic Graphs), Airflow Architecture: Web Server, Scheduler, Workers, and Metadata Database
Module 2: Setting Up Apache Airflow: Installing Airflow on Local Machine, Setting Up Airflow on Cloud (AWS, GCP, Azure), Understanding Airflow Configuration Files, Running the Airflow Web UI and CLI Commands
Module 3: Creating and Managing Workflows: Writing Your First DAG in Python, Understanding Tasks, Operators, and Dependencies, Using Built-in Operators (Python, Bash, SQL, Email), Handling Dependencies Between Tasks, Scheduling and Triggering Workflows
Module 4: Working with Airflow Operators & Hooks: What are Operators, Sensors, and Hooks?, Working with Python, Bash, and Email Operators, Database Operators (PostgreSQL, MySQL, Snowflake, etc.), Cloud Hooks: AWS S3, GCP BigQuery, Azure Blob Storage, Using External APIs in Airflow
Module 5: Scheduling & Monitoring Workflows: Setting Up Task Schedules with Cron & Timetables, Monitoring DAG Runs in the Airflow UI, Handling Task Failures and Retries, Parallel Execution and Task Queues, Using XComs to Share Data Between Tasks
Module 6: Building Real-World ETL Pipelines: Extracting Data from APIs and Databases, Transforming Data with Pandas & SQL Queries, Loading Processed Data into Data Warehouses, Automating ETL Pipelines with Airflow, Best Practices for Building Scalable Workflows
Module 7: Integrating Airflow with Big Data & Cloud Services: Connecting Airflow with Apache Spark & Kafka, Running Airflow with Databricks & Hadoop, Deploying Airflow on Kubernetes & Docker, Using Airflow with Cloud Services (AWS, GCP, Azure), Managing Airflow Connections & Secrets
Module 8: Airflow Security, Logging, and Optimization: Implementing Authentication & Role-Based Access Control, Logging and Monitoring with Airflow, Performance Tuning & Optimization Techniques, Managing Large Workflows & DAG Performance, Debugging Common Airflow Errors
Module 9: Deploying Apache Airflow in Production: Setting Up Airflow in a Production Environment, Using Airflow with CI/CD Pipelines, Deploying Airflow Workflows at Scale, Maintaining and Upgrading Airflow Clusters
Module 10: Hands-on Projects & Case Studies: Automating a Data Pipeline for Web Scraping, Scheduling a Machine Learning Workflow in Airflow, ETL Pipeline for Real-Time Streaming Data, Deploying Airflow for Data Warehouse Automation, Capstone Project: Building a Fully Automated Workflow

Course Curriculum

Module 1: Introduction to Apache Airflow

1.1 Understanding Workflow Orchestration
:: What is Workflow Orchestration?
:: The Role of Orchestration in Data Engineering
:: Common Orchestration Tools: Apache Airflow vs. Others
1.2 Introduction to Apache Airflow
:: What is Apache Airflow?
:: Key Features of Airflow
:: Real-world Use Cases of Airflow in Data Engineering
1.3 Installing and Setting Up Airflow
:: System Requirements and Dependencies
:: Installing Airflow Locally (Linux, Windows, MacOS)
:: Setting Up Airflow with Docker and Docker Compose
:: Overview of Managed Airflow Services (AWS MWAA, Google Cloud Composer)
1.4 Airflow UI and Core Concepts
:: Navigating the Airflow Web UI
:: DAGs, Tasks, and Operators
:: Task Instances, DAG Runs, and Schedulers
:: Understanding Airflow’s Directed Acyclic Graph (DAG)

Module 2: Building Your First Airflow DAG

2.1 Anatomy of an Airflow DAG
:: Creating a Simple DAG in Python
:: Understanding DAG Structure: Start Date, Schedule Interval, Default Arguments
:: Writing and Triggering Your First DAG
2.2 Working with Airflow Operators
:: Core Operators: BashOperator, PythonOperator, EmailOperator
:: Using Sensors for Event-Driven Workflows: TimeSensor, FileSensor, ExternalTaskSensor
:: Understanding Operator Dependencies and Task Flow
2.3 Managing Dependencies in Airflow
:: Setting Task Dependencies: Upstream, Downstream, and Cross-DAG Dependencies
:: Trigger Rules: All Success, One Success, All Failed, etc.
:: Using the TriggerDagRunOperator for Chained Workflows
2.4 Scheduling and Triggering DAGs
:: Understanding Scheduling Intervals: Cron Expressions and Presets
:: Catchup and Backfill Concepts
:: Triggering DAGs Manually vs. Automatically
:: Managing DAG Runs and Task Instances in the UI

Module 3: Advanced Airflow Concepts

3.1 XComs and Data Passing Between Tasks
:: What are XComs (Cross-Communication)?
:: Passing Data Between Tasks Using XComs
:: Advanced XComs Usage: Custom XCom Backends, Serialization
3.2 Airflow Task Flow API
:: Introduction to TaskFlow API (Introduced in Airflow 2.0+)
:: Simplifying DAGs with the TaskFlow API
:: Implementing Task Dependencies and Task Inputs/Outputs
3.3 Airflow Variables and Connections
:: Using Airflow Variables for Parameterizing Workflows
:: Managing and Securing Connections to External Systems
:: Integrating with AWS, Google Cloud, and Other External Services
3.4 Dynamic DAGs and Task Generation
:: Creating Dynamic Workflows with Python Code
:: DAG Factories: Generating Multiple DAGs from a Single Script
:: Dynamic Task Generation: Scaling Workflows Programmatically

Module 4: Data Pipelines with Airflow

4.1 ETL Pipelines Using Airflow
:: Designing and Building ETL Pipelines in Airflow
:: Extracting Data from APIs, Databases, and Files
:: Transforming Data Using Python, SQL, and External Tools
:: Loading Data into Data Warehouses, Data Lakes, and Databases
4.2 Integrating Airflow with Data Warehouses
:: Working with Airflow’s Database Operators: MySqlOperator, PostgresOperator, etc.
:: Loading Data into AWS Redshift, Google BigQuery, and Snowflake
:: Handling Data Transformations in SQL vs. Python
4.3 Data Validation and Quality Checks
:: Implementing Data Validation with Sensors and Custom Operators
:: Using Great Expectations with Airflow for Data Quality Assurance
:: Building Alerts for Data Quality Issues
4.4 Real-time Scenario: Building a Data Pipeline
:: End-to-End Data Pipeline: Ingestion to Reporting
:: Scheduling Regular ETL Jobs with Airflow
:: Monitoring and Optimizing Pipeline Performance

Module 5: Airflow for Big Data and Machine Learning

5.1 Orchestrating Big Data Workflows
:: Integrating Airflow with Apache Hadoop, Spark, and Flink
:: Using Airflow to Orchestrate Distributed Data Processing Jobs
:: Managing Dependencies and Data Flow in Big Data Workflows
5.2 Machine Learning Pipelines with Airflow
:: Using Airflow to Schedule and Manage ML Training Jobs
:: Data Preparation, Model Training, and Deployment as DAGs
:: Integrating Airflow with ML Frameworks (TensorFlow, Scikit-learn)
5.3 Airflow with Cloud-Based ML Services
:: Orchestrating ML Workflows with AWS SageMaker, Google AI Platform
:: Managing Model Deployment and Monitoring with Airflow
:: Automated Retraining and Model Validation Pipelines
5.4 Real-time Scenario: ML Model Training Pipeline
:: Building a Pipeline for Data Preprocessing, Model Training, and Evaluation
:: Scheduling and Automating Model Retraining Jobs
:: Monitoring Model Performance and Managing Model Drift

Module 6: Airflow Performance Optimization and Scaling

6.1 Scaling Airflow for Large Workloads
:: Scaling Airflow with the Celery Executor and Kubernetes Executor
:: Distributed Task Execution and Worker Management
:: Load Balancing and High Availability for Airflow
6.2 Performance Tuning for Airflow
:: Optimizing DAGs for Performance and Reliability
:: Reducing Task Duration and Managing Resource Utilization
:: Tuning Airflow’s Scheduler and Workers for Large-Scale Deployments
6.3 Monitoring and Debugging Airflow Workflows
:: Using Airflow’s Monitoring Tools: Logs, Metrics, and Alerts
:: Integrating with Prometheus and Grafana for Advanced Monitoring
:: Debugging Common Airflow Issues: DAG Failures, Task Retries, and Deadlocks
6.4 Real-time Scenario: Optimizing a Large-Scale Workflow
:: Scaling a Workflow for a High-Volume Data Pipeline
:: Implementing Best Practices for Large-Scale Airflow Deployments
:: Monitoring and Troubleshooting in Production Environments

Module 7: Airflow Security and Best Practices

7.1 Securing Airflow Deployments
:: Authentication and Authorization in Airflow
:: Configuring Role-Based Access Control (RBAC)
:: Securing Airflow Connections and Variables
7.2 Compliance and Data Governance
:: Implementing Compliance Controls in Airflow (e.g., GDPR, HIPAA)
:: Auditing and Logging for Compliance Purposes
:: Data Lineage and Impact Analysis with Airflow
7.3 Best Practices for Airflow Development
:: Structuring Airflow Projects for Maintainability
:: Version Control and CI/CD for Airflow DAGs
:: Testing Airflow Workflows: Unit Testing, Integration Testing
7.4 Real-time Scenario: Securing an Airflow Deployment
:: Implementing Security Best Practices for a Production Airflow Instance
:: Ensuring Compliance in Data Pipelines
:: Auditing Airflow Logs and Managing Access Controls

Module 8: Advanced Airflow Use Cases and Integrations

8.1 Custom Plugins and Operators
:: Developing Custom Operators and Hooks for Airflow
Creating Custom Sensors for Specialized Use Cases
:: Building and Distributing Airflow Plugins
8.2 Airflow and Data Lakes
:: Orchestrating Data Ingestion and Processing in Data Lakes (e.g., AWS S3, Azure Data Lake)
:: Integrating Airflow with Apache Hive, Presto, and Delta Lake
:: Automating Data Lake Management with Airflow
8.3 Real-time Data Processing with Airflow
:: Orchestrating Real-time Data Streams with Airflow
:: Integrating Airflow with Apache Kafka and Spark Streaming
:: Managing Real-time ETL Pipelines with Airflow
8.4 Real-time Scenario: Implementing a Custom Solution
:: Developing a Custom Operator for a Specific Use Case
:: Integrating Airflow with a Complex Data Architecture
:: Deploying and Testing Custom Solutions in Production

Module 9: Final Project and Certification

9.1 Project: End-to-End Data Pipeline
:: Designing and Implementing a Comprehensive Data Pipeline
:: Incorporating ETL, Data Validation, and Reporting in Airflow
:: Optimizing, Securing, and Monitoring the Pipeline
9.2 Final Assessment
:: Multiple-Choice Exam Covering Key Airflow Concepts
:: Practical Assignment: Debugging and Optimizing a Complex DAG
:: Certification of Completion
9.3 Career Guidance
:: Preparing for Data Engineer Interviews with a Focus on Airflow
:: Building a Portfolio of Airflow Projects
:: Networking and Continued Learning in the Data Engineering Field

New Video Release: Automated SFTP File Transfers in Apache Airflow

By accentfuture

2 weeks ago

Our newest educational video describes how to execute automated SFTP file transfers with Apache Airflow. The video explains file transfer automation by SFTP through the powerful Apache Airflow platform.

Learning users at any level can follow this tutorial to implement complete automated file transfer solutions which optimizes their workflow alongside its expansion capabilities.

What you’ll learn:

Procedure to initialize and configure Apache Airflow with SFTP capabilities
Automating file transfers between systems
You need to apply the correct error handling strategies and retry guidelines for Apache Airflow operations.
Workflows that use Apache Airflow must include the setup for file transfer scheduling and the system for monitoring these transfers.
This chance enables you to develop your automation skills while improving your file transfer operations beyond their current state.

The video is available for viewing at this YouTube URL.

Watch the video now: https://youtu.be/fiD1bZ88Af8

Stay tuned for more tutorials and updates!

Airflow

Airflow Training:

What Will You Learn?

Course Curriculum

Module 1: Introduction to Apache Airflow

1.1 Understanding Workflow Orchestration

:: What is Workflow Orchestration?

:: The Role of Orchestration in Data Engineering

:: Common Orchestration Tools: Apache Airflow vs. Others

1.2 Introduction to Apache Airflow

:: What is Apache Airflow?

:: Key Features of Airflow

:: Real-world Use Cases of Airflow in Data Engineering

1.3 Installing and Setting Up Airflow

:: System Requirements and Dependencies

:: Installing Airflow Locally (Linux, Windows, MacOS)

:: Setting Up Airflow with Docker and Docker Compose

:: Overview of Managed Airflow Services (AWS MWAA, Google Cloud Composer)

1.4 Airflow UI and Core Concepts

:: Navigating the Airflow Web UI

:: DAGs, Tasks, and Operators

:: Task Instances, DAG Runs, and Schedulers

:: Understanding Airflow’s Directed Acyclic Graph (DAG)

Module 2: Building Your First Airflow DAG

2.1 Anatomy of an Airflow DAG

:: Creating a Simple DAG in Python

:: Understanding DAG Structure: Start Date, Schedule Interval, Default Arguments

:: Writing and Triggering Your First DAG

2.2 Working with Airflow Operators

:: Core Operators: BashOperator, PythonOperator, EmailOperator

:: Using Sensors for Event-Driven Workflows: TimeSensor, FileSensor, ExternalTaskSensor

:: Understanding Operator Dependencies and Task Flow

2.3 Managing Dependencies in Airflow

:: Setting Task Dependencies: Upstream, Downstream, and Cross-DAG Dependencies

:: Trigger Rules: All Success, One Success, All Failed, etc.

:: Using the TriggerDagRunOperator for Chained Workflows

2.4 Scheduling and Triggering DAGs

:: Understanding Scheduling Intervals: Cron Expressions and Presets

:: Catchup and Backfill Concepts

:: Triggering DAGs Manually vs. Automatically

:: Managing DAG Runs and Task Instances in the UI

Module 3: Advanced Airflow Concepts

3.1 XComs and Data Passing Between Tasks

:: What are XComs (Cross-Communication)?

:: Passing Data Between Tasks Using XComs

:: Advanced XComs Usage: Custom XCom Backends, Serialization

3.2 Airflow Task Flow API

:: Introduction to TaskFlow API (Introduced in Airflow 2.0+)

:: Simplifying DAGs with the TaskFlow API

:: Implementing Task Dependencies and Task Inputs/Outputs

3.3 Airflow Variables and Connections

:: Using Airflow Variables for Parameterizing Workflows

:: Managing and Securing Connections to External Systems

:: Integrating with AWS, Google Cloud, and Other External Services

3.4 Dynamic DAGs and Task Generation

:: Creating Dynamic Workflows with Python Code

:: DAG Factories: Generating Multiple DAGs from a Single Script

:: Dynamic Task Generation: Scaling Workflows Programmatically

Module 4: Data Pipelines with Airflow

4.1 ETL Pipelines Using Airflow

:: Designing and Building ETL Pipelines in Airflow

:: Extracting Data from APIs, Databases, and Files

:: Transforming Data Using Python, SQL, and External Tools

:: Loading Data into Data Warehouses, Data Lakes, and Databases

4.2 Integrating Airflow with Data Warehouses

:: Working with Airflow’s Database Operators: MySqlOperator, PostgresOperator, etc.

:: Loading Data into AWS Redshift, Google BigQuery, and Snowflake

:: Handling Data Transformations in SQL vs. Python

4.3 Data Validation and Quality Checks

:: Implementing Data Validation with Sensors and Custom Operators

:: Using Great Expectations with Airflow for Data Quality Assurance

:: Building Alerts for Data Quality Issues

4.4 Real-time Scenario: Building a Data Pipeline

:: End-to-End Data Pipeline: Ingestion to Reporting

:: Scheduling Regular ETL Jobs with Airflow

:: Monitoring and Optimizing Pipeline Performance

Module 5: Airflow for Big Data and Machine Learning

5.1 Orchestrating Big Data Workflows

:: Integrating Airflow with Apache Hadoop, Spark, and Flink

:: Using Airflow to Orchestrate Distributed Data Processing Jobs