Data Engineering for Bioinformatics Leaders
Tools and technologies for automating and scaling high-end bioinformatics pipelines
What is Apache Airflow?
Apache Airflow is an open-source platform that helps orchestrate and schedule workflows—pipelines of tasks that move data from one step to the next.
It is an incredibly useful tool for organizing repeated operations like transforming data from public database endpoints like PubMed, or on top of internal data lakes where raw data assets are collected.
Key Features:
- DAG-based Scheduling: Define workflows as Directed Acyclic Graphs (DAGs), making them clear, repeatable, and easy to track.
- Extensibility with Custom Operators: Airflow’s modular structure allows users to create custom tasks tailored to specific bioinformatics processes, for instance parsing specific file formats like VCF or FASTA
- Built-in Monitoring & Logging: Gain real-time visibility into the progress and performance of each task, and immediately monitor
- Scalability: Airflow can scale from a small lab setup to a large-scale cluster, and cloud providers like AWS provide serverless access to Airflow.
Real-World Applications:
- Automate the pipeline for DNA sequence analysis, integrating tools like FastQC, STAR and HTSeq Count.
- Schedule periodic ETL (Extract, Transform, Load) jobs to clean, normalize, and store experimental assay data into a database.
- Trigger machine learning model training once new experimental data arrives, update and deploy production models.
Learning Curve:
Getting started with Airflow requires familiarity with Python, as workflows are defined in Python code. New users often need to grasp the concept of DAGs and how to structure tasks in a logical order.
While it’s straightforward to install and begin running small pipelines, scaling up to more complex workflows can take time and experience. Cloud-hosted solutions can take care of scaling applications, of course not for free.
However, there’s a robust community and ample documentation to support learning, making it a highly accessible tool for those willing to invest a bit of effort upfront.
A good starting point: https://theaisummer.com/apache-airflow-tutorial/