Batch data processing (also known as ETL) is time-consuming, brittle, and often unrewarding. Not only that, it’s hard to operate, evolve, and troubleshoot.
In this talk, we’ll discuss functional programming paradigm and explore how applying it to Data Engineering can bring a lot of clarity to the process. It helps solving some of the inherent problems of ETL, leads to more manageable and maintainable workloads and helps to implement reproducible and scalable practices. It empowers data teams to tackle larger problems and push the boundaries of what’s possible.
Maxime Beauchemin works as a Senior Software Engineer at Lyft where he develops open source products that reduce friction and help generate insights from data. He is the creator and a lead maintainer of Apache Airflow [incubating], a data pipeline workflow engine; and Apache Superset [incubating], a data visualization platform; and is recognized as a thought leader in the data engineering field.
Before Lyft, Maxime worked at Airbnb on the "Analytics & Experimentation Products team". Previously, he worked at Facebook on computation frameworks powering engagement and growth analytics, on clickstream analytics at Yahoo!, and as a data warehouse architect at Ubisoft.