Description:

Building a Scalable and Reliable Healthcare Data Pipeline with Airflow:

In this beginner-friendly session, we will explore how to design and implement a robust data pipeline using Apache Airflow, focusing on scalability and reliability .

Scalability: To ensure scalability, we will adopt modular task design principles. By breaking down data processing stages (such as ingestion, cleaning, anonymization, and analysis) into smaller, independent Python functions.

Parallelization will be another key focus. We'll demonstrate how to parallelize independent tasks within each stage using Airflow's PythonOperator or BashOperator. This approach optimizes performance by concurrently processing multiple patient records, utilizing tools like multiprocessing for efficient task distribution.

Reliability: In terms of reliability, error-handling mechanisms will be crucial. We'll cover best practices for implementing robust error handling within Python functions, ensuring exceptions are caught, errors are logged, and failed tasks are retried with exponential backoff to manage transient issues without disrupting the pipeline.

Monitoring and validation will also be addressed. Additionally, we'll emphasize the importance of data validation checks at each stage to maintain data integrity and detect inconsistencies early in the process.

Demo and Practical Use: ( Sample data pipeline) During the session, we'll conduct practical demonstrations using Python operators in Airflow to showcase how to configure and execute tasks effectively. This hands-on approach will help participants understand the workflow orchestration capabilities of Airflow and how to apply them in building reliable data pipelines.

Leveraging Amazon Q for Code Generation: Furthermore, we'll explore how leveraging tools like Amazon Q can simplify code generation within integrated development environments (IDEs).

By the end of this session, attendees will gain foundational knowledge in designing scalable and reliable data pipelines with Airflow, equipped with practical insights and tools to enhance their data engineering capabilities in healthcare and beyond.

SLIDES : https://docs.google.com/presentation/d/1vdmn45t-_1i56Khly1SqrN5x2L3PDsoTAlPQWtzgPG0/edit?usp=sharing

Intro : https://drive.google.com/file/d/1YijgUA2nnET_v-HfTrU8hOZAVUTPCkBL/view?usp=sharing

Video URL:

https://drive.google.com/file/d/1YijgUA2nnET_v-HfTrU8hOZAVUTPCkBL/view?usp=sharing

Content URLs:

Medium-->https://medium.com/@abinayasv Linkedin --> https://www.linkedin.com/in/abinayasv/ Spotify-->https://open.spotify.com/show/0dhRfTXjhWAOzbgEBFKujz

Speaker Info:

My name is Abinaya, and I bring with me two years of experience in the field. Currently, I'm employed at FIS Global, where I'm deeply passionate about data engineering. I also share my insights through blog posts on Medium, although my writing endeavors have been temporarily paused. Additionally, I indulge in hosting and co-producing a podcast with my friends, where we share bite-sized discussions on various topics.

Speaker Links:

https://medium.com/@abinayasv/apache-airflow-011e9b41fd61

SPEAKER: https://www.linkedin.com/posts/aws-cloud-club-sjit_awsabrstudentabrcommunityabrdayabr2024-awsabrcloudabrclubs-activity-7167018159680475136-o6Ku?utm_source=share&utm_medium=member_desktop