Presentation: Building (Better) Data Pipelines with Apache Airflow

Track: Predictive Data Pipelines & Architectures

Location: Cyril Magnin I

Duration: 2:25pm - 2:35pm

Day of week: Tuesday

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Abstract

Apache Airflow is an up-and-coming platform to programmatically author, schedule, manage, and monitor workflows. Central to Airflow’s design is that is requires users to define DAGs (directed acyclic graphs) a.k.a. workflows in Python code, so that DAGs can be managed via the same software engineering principles and practices used to manage any other code.

With more than 7600 GitHub stars, 2400 forks, 430 contributors, 150 companies officially using it, and 4600 commits, it is quickly gaining traction among data science, ETL engineering, data engineering, and devops communities at large. What makes Apache Airflow so popular? Come to this talk to get a whirlwind intro based on a real-world predictive data pipeline example.

Note: This is a short talk. Short talks are 10-minute talks designed to offer breadth across the areas of machine learning, artificial intelligence, and data engineering. The short talks are focused on the tools and practices of data science with an eye towards the software engineer.

Speaker: Sid Anand

Chief Data Engineer @PayPal

Sid Anand currently serves as PayPal's Chief Data Engineer, focusing on ways to realize the value of data. Prior to joining PayPal, he held several positions including Agari's Data Architect, a Technical Lead in Search @ LinkedIn, Netflix’s Cloud Data Architect, Etsy’s VP of Engineering, and several technical roles at eBay. Sid earned his BS and MS degrees in CS from Cornell University, where he focused on Distributed Systems. In his spare time, he is a maintainer/committer on Apache Airflow, a co-chair for QCon, and a frequent speaker at conferences. When not working, Sid spends time with his wife, Shalini, and their 2 kids.

Find Sid Anand at

Tracks

  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.