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Introducing Apache Airflow and how we are using it

1. Apache Airflow Data Pipelines Made Easy

2. Table of contents 01 What is Airflow? Introduction to Apache Airflow 02 Airflow Architecture Understanding Airflow's Components 03 DAGs and Tasks Building Blocks of Airflow Pipelines 04 Scheduling and Monitoring Managing Airflow Workflows 05 Extensibility and Integrations Customizing Airflow for Your Needs 06 Use Cases and Benefits Why Airflow is Valuable

3. 1 What is Airflow? Introduction to Apache Airflow

4. What is Airflow? • Apache Airflow is an open-source workflow management platform. • It helps to author, schedule, and monitor data pipelines. • Airflow allows you to define your workflows as Directed Acyclic Graphs (DAGs). • It is written in Python and is highly extensible.

5. 2 Airflow Architecture Understanding Airflow's Components

6. Airflow Architecture • Airflow has a modular architecture with four main components: - Web Server: User interface to monitor and trigger workflows - Scheduler: Schedules and monitors DAG executions - Workers: Execute tasks defined in the DAGs - Metadata Database: Stores DAG definitions and execution history

7. 3 DAGs and Tasks Building Blocks of Airflow Pipelines

8. DAGs and Tasks • DAGs (Directed Acyclic Graphs) define the workflow as a collection of tasks. • Tasks are individual units of work, such as data transformations or API calls. • Tasks can have dependencies on other tasks, defining the execution order. • Airflow provides many built-in operators for common tasks.

9. 4 Scheduling and Monitoring Managing Airflow Workflows

10. Scheduling and Monitoring • Airflow allows you to schedule DAGs to run at specific intervals or times. • You can monitor the status of DAG runs and individual tasks through the Web UI. • Airflow provides detailed logs and metrics for troubleshooting and performance analysis. • Alerts can be set up to notify you of failures or SLA breaches.

11. 5 Extensibility and Integrations Customizing Airflow for Your Needs

12. Extensibility and Integrations • Airflow is highly extensible, allowing you to create custom operators and plugins. • It integrates with various data sources, processing engines, and cloud platforms. • Airflow has a growing ecosystem of providers and third-party integrations. • You can extend Airflow's functionality with custom hooks, sensors, and executors.

13. 6 Use Cases and Benefits Why Airflow is Valuable

14. Use Cases and Benefits • Airflow is widely used for data engineering and ETL pipelines. • It simplifies the management of complex, interdependent workflows. • Airflow promotes code reusability and collaboration. • It provides visibility and control over your data pipelines. • Airflow is scalable and fault-tolerant, ensuring reliable execution.