Data engineering pipelines orchestration migration from Kubernetes to Airflow

About project:

Client overview

A leading data analytics company faced significant challenges with their Kubernetes-based script management system, which required constant DevOps intervention for basic operations and offered limited visibility into logs and errors. They needed a more efficient, self-service solution to streamline their script orchestration processes and reduce operational overhead.

Tech Stack:

Kubernetes, Custom Script Management, Basic Logging, Manual Error Handling

Tech stack after migration:

Apache Airflow, Secure Credential Storage (e.g., HashiCorp Vault), Enhanced Logging (e.g., ELK Stack), Automated Error Handling

Time to deliver project:

6-8 Weeks

Problem

  • Managing scripts through Kubernetes was causing issues. We had to involve DevOps to start or stop syncs, transfer credentials when writing new scripts, and couldn’t easily access logs or fix errors.

Inspection

  • We decided to move to Airflow, allowing us to manage scripts independently. With Airflow, we can easily access logs, control script execution, and improve fault tolerance and data quality. Its idempotence ensures predictable results during data transfers.

Recommendation

  • Consider using specialized tools for pipeline orchestration. Airflow is a popular choice, known for its large community support and user-friendly documentation, but other tools may also be suitable.

Resolution

We migrated all script orchestration from Kubernetes to Airflow. This included configuring Airflow to handle script management, setting up secure credential storage, and enabling easy log access. We also implemented error-handling mechanisms and scheduled automated script runs. As a result, the time to deploy new scripts was reduced by 70%, and script errors are now resolved in less than 10 minutes, compared to over an hour previously. Overall system stability improved, with a 30% reduction in sync failures.

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