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Airflow: Syllabus
Mastering Apache Airflow: Scalable Data Orchestration and Workflow Automation
Apache Airflow is the industry standard for data workflow orchestration, allowing users to create, schedule, and monitor data pipelines efficiently. This book provides a hands-on approach to implementing Apache Airflow, covering DAG development, workflow scheduling, automation, and real-world integrations with PostgreSQL, MinIO, and Apache Spark.
Module 1: Introduction to Apache Airflow
- What is Apache Airflow? Why is it important for data engineering?
- Understanding Directed Acyclic Graphs (DAGs)
- Installing Apache Airflow locally and in a cloud environment
- Key components: Scheduler, Executor, Workers, and Web UI
Module 2: Designing DAGs (Directed Acyclic Graphs)
- Writing your first Airflow DAG
- Understanding DAG structure and dependencies
- Managing dynamic DAG generation
- Implementing best practices for maintainable DAGs
Module 3: Task Execution and Scheduling
- Understanding operators (PythonOperator, BashOperator, SQLOperator, etc.)
- Scheduling workflows: cron expressions vs. Airflow schedules
- Triggering DAGs manually and event-driven executions
- Monitoring and debugging task failures
Module 4: Integrating Apache Airflow with PostgreSQL
- Connecting Airflow to PostgreSQL using the PostgresOperator
- Running SQL queries from Airflow
- Managing ETL pipelines with PostgreSQL as a backend
- Storing Airflow metadata in PostgreSQL
Module 5: Managing Data with MinIO and Apache Airflow
- Introduction to MinIO as an S3-compatible object storage
- Uploading and retrieving data from MinIO using Airflow
- Using MinIO with Airflow’s S3Hook for file management
- Automating MinIO-based data processing workflows
Module 6: Orchestrating Apache Spark Jobs with Airflow
- Connecting Apache Spark with Airflow using the SparkSubmitOperator
- Running batch processing jobs with Spark and Airflow
- Integrating Airflow with Spark on Kubernetes
- Optimizing Spark workflows for efficiency
Module 7: Scaling Airflow for Large Workflows
- Configuring Airflow Executors: Local, Celery, Kubernetes, and Dask
- Running distributed workflows on Kubernetes
- Using Airflow Sensors for event-driven workflows
- Managing task parallelism and dependencies
Module 8: Monitoring, Logging, and Alerting in Airflow
- Setting up logging and monitoring for Airflow tasks
- Using Airflow’s built-in monitoring tools
- Configuring email and Slack alerts for task failures
- Implementing real-time logging with Prometheus and Grafana
Module 9: Deploying Airflow in Production
- Running Airflow in Docker and Kubernetes
- Deploying Airflow on AWS, GCP, and Azure
- Implementing CI/CD pipelines for DAG deployment
- Securing Apache Airflow with authentication and role-based access control (RBAC)
Hands-On Projects
Project 1: Building an ETL Pipeline with Apache Airflow and PostgreSQL
- Extracting data from APIs and storing it in PostgreSQL
- Running transformations using PythonOperator
- Automating daily ingestion jobs with Airflow scheduling
Project 2: Data Processing Pipeline with MinIO and Airflow
- Uploading and retrieving files from MinIO
- Automating file-based ETL workflows with Airflow and MinIO
- Managing MinIO lifecycle policies from Airflow
Project 3: Running Apache Spark Jobs with Airflow
- Submitting Apache Spark jobs using SparkSubmitOperator
- Running distributed processing tasks using Spark on Kubernetes
- Logging and monitoring Spark jobs through Airflow UI
Project 4: Implementing a Real-Time Data Pipeline with Airflow and Sensors
- Implementing event-driven DAGs using Airflow Sensors
- Automating real-time processing workflows
- Integrating Airflow with Kafka for real-time streaming
Project 5: Deploying a Scalable Airflow Cluster with Kubernetes
- Configuring Airflow with Celery/KubernetesExecutor
- Deploying Airflow DAGs using Helm charts
- Implementing CI/CD pipelines for Airflow DAG management