Summary

The article discusses the challenges of correlating real-time data with historical data in data analysis, particularly in e-commerce scenarios. It presents an optimized solution using Apache Flink to join streaming order data with historical customer and product information, leveraging Alluxio for caching. The implementation details include using Hive dimension tables and Flink's temporal joins to enhance performance and reduce bottlenecks. The article also addresses state management issues in Flink applications and provides insights into improving data processing efficiency.

💭 Your Thoughts

What?! the dimension table data isn’t automatically refreshed. - this sounds a Flink internal pbm. First time hear: Detail Wide Data (DWD) table which has been used as a Flink dynamic table to perform consequence processing after a lookup join, sound a Sliver zone dataset.

Data Problems Addressed

Technologies Referenced