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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.

Host: 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

2019 Tracks

  • Groking Timeseries & Sequential Data

    Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.