Maintaining data pipelines and data warehouses

Learning pipeline orchestrations with Airflow

Apache Airflow is an open-source platform used for orchestrating complex data workflows. It allows users to schedule, monitor, and manage data pipeline tasks through a programmable interface.

Key Concepts

• DAGs (Directed Acyclic Graphs): Understanding the structure of Airflow workflows, consisting of tasks organized in a directed acyclic graph.
• Operators: Exploring the various types of operators provided by Airflow for executing different tasks within a workflow.
• Sensors: Understanding how sensors are used to wait for external conditions before triggering downstream tasks.
• Executors: Overview of different executor types in Airflow and their role in task execution.

Workflow Management

• Task Dependencies: Defining dependencies between tasks to ensure proper sequencing and execution order.
• Scheduling: Configuring scheduling options for running workflows at specified intervals or in response to events.
• Error Handling: Implementing error handling mechanisms to handle failures and retries gracefully.
• Monitoring and Logging: Leveraging Airflow’s monitoring and logging features to track workflow progress and troubleshoot issues.

Set up Data Pipeline for Batch Processing with Mage AI and Airflow

Overview of Batch Processing:

Batch processing involves processing large volumes of data at regular intervals. It is well-suited for scenarios where data latency is acceptable, and processing can be performed in batches.

Integration with Mage AI

Mage AI is a powerful data processing library that complements Apache Airflow for batch processing tasks. It provides pre-built components and utilities for common data processing operations, such as data transformation, aggregation, and analysis.

Setting up the Data Pipeline

• Data Source Configuration: Configuring data sources and defining extraction mechanisms for retrieving data.
• Data Transformation: Performing data transformation tasks using Mage AI operators within Airflow DAGs.
• Data Loading: Loading transformed data into target data warehouses or storage systems.
• Workflow Orchestration: Defining DAGs in Airflow to orchestrate the execution of batch processing tasks.

Summary

In the Summary phase of the Data Engineering Bootcamp, participants will apply the concepts and techniques learned to real-world scenarios. This phase focuses on assessing participants’ understanding and proficiency in maintaining data pipelines and data warehouses.

Hands-On Projects

Participants will work on hands-on projects that involve setting up and managing data pipelines, performing batch processing tasks, and troubleshooting common issues.

Assessments

Assessments will be conducted to evaluate participants’ knowledge, skills, and problem-solving abilities in maintaining data pipelines and data warehouses. These assessments may include quizzes, practical assignments, and project presentations.