Introduction

Orchestration in ETL pipelines is the process of coordinating and managing the various tasks the pipeline executes. The more complex our pipelines are the more crucial it is to add some orchestration layer.

As we have seen throughout the course, the extract, transform, and load tasks of ETL pipelines can vastly differ from one pipeline to the other, and each pipeline is used for a different purpose. Also, pipelines are usually comprised of multiple tasks that must run sequentially for the whole pipeline to succeed. If one task fails, we should know about it and perhaps run the pipeline again.

In a real-world environment, we might have to manage and maintain tens of pipelines simultaneously. Each runs on a different schedule interval and serves data to different parts of the organization.

The solution to successfully managing this is orchestration.

Benefits of orchestration

Let’s go over some of the benefits we can get from orchestrating our pipelines:

1. Task scheduling: ETL pipelines can involve multiple tasks that depend on each other or have specific time constraints. Orchestration tools can schedule these tasks efficiently, ensuring dependencies are met and resources are optimally utilized.

2. Fault tolerance and error handling: When working with large volumes of data, failures can occur at various stages of the ETL process. Orchestration frameworks allow for error handling, retries, and fault tolerance, ensuring data integrity and reliability.

3. Monitoring: One of the main reasons to add orchestration is to track the progress of the active pipelines in the system and identify and troubleshoot issues in real-time.

4. Logging: Log data is essential for troubleshooting issues and unexpected failures in our pipelines. When something goes wrong, the first thing we should do is check the logs.

5. Metadata: Metadata (i,e., data about the pipeline) grants us a higher-level view of what goes on behind the scenes regarding our pipeline. Metadata can answer questions like:

   1. How many times did the pipeline run last week?

   2. How many runs have been successful?

   3. Which tasks have failed the most and why?

6. Dependency management: Orchestration allows better control over the sequence of tasks each pipeline must execute. Some pipelines can have very complex dependencies between tasks.

Apache Airflow

There are many tools for orchestrating pipelines. Some popular ones include Apache NiFi, Luigi, Apache Oozie, Dagster, Apache Airflow, and more.

In this section, we’ll practice using Apache Airflow to orchestrate our pipelines.

Airflow is an open-source Python platform and a great tool for orchestrating batch and micro-batch pipelines. In Airflow, we represent pipelines as directed acyclic graphs (DAGs), where each node represents a task and edges represent dependencies between tasks. This allows for easy visualization and understanding of the workflow.

For example, if we build an ETL pipeline containing five tasks. These five tasks might have a specific logical order, like this: