Case Study: How Pinterest Built a Stream Processing Platform with Apache Flink
Published by O'Reilly Media, Inc.
Enabling data-driven products with stream processing
Facing rapid growth and competition in its online business, Pinterest had to evolve its data stack from offline-only ETL batch jobs to near-real-time big data applications. But stateful stream processing is a relatively new technology in the big data field, and there are many offerings to choose from, each with its own pros and cons.
Join us for this Case Study with Pinterest tech lead Chen Qin to learn how the company chose Apache Flink as the technology behind its stream processing platform. You’ll see how the platform has enabled critical use cases and a user base that scaled out and evolved along with product innovation—and hear some lessons learned while implementing and growing the platform.
O'Reilly Case Study sessions explore how organizations have overcome common challenges in business and technology through a series of one-hour interactive events. You’ll engage in a live conversation with experts, sharing your questions and challenges while hearing their unique perspectives, insights, and lessons learned.
What you’ll learn and how you can apply it
- Learn how to kickstart the process of building a stream processing platform
- Understand how to evaluate use cases for stream processing
- See the pitfalls to avoid when growing a stream processing platform
This live event is for you because...
- You’re a data infrastructure team lead or manager interested in implementing stream processing offerings in your organization.
- You’re a business executive and technology leader looking to add value and transform your current ETL-based data warehousing to near real time.
Prerequisites
- Come with your questions for Chen Qin
- Have a pen and paper handy to capture notes, insights, and inspiration
Recommended follow-up:
- Read Stream Processing with Apache Flink (book)
- Watch Stream Processing Beyond Streaming Data (video)
Your Presenter
Chen Qin
Chen Qin is a tech lead and apprentice manager for the stream processing team at Pinterest. Chen has more than 15 years of experience working on distributed systems and data analytics. He contributed to Flink open source, and in 2016 he introduced Flink to Uber, where he worked on Spark and XGBoost projects and helped the company run large-scale training.