Realtime Compute for Apache Flink:Best Practices

Guide map of best practices

Data ingestion and CDC integration

• Best practices for data ingestion using Flink CDC

• Synchronize an entire MySQL database to Kafka using Flink CDC

Real-time data warehouse and lakehouse architectures

• Streaming Lakehouse solution based on Paimon

• Build a real-time data warehouse using Flink and Hologres

• Build a streaming lakehouse using Flink, Paimon, and StarRocks

Industry scenarios and vertical applications

• User behavior analysis for the game industry using Flink, MongoDB, and Hologres

• Implement real-time queries and a dashboard for Taobao maternal and infant orders

• Analyze real-time traffic density using Flink and Apache Sedona

• Encrypt and dynamically decrypt database passwords for Flink jobs using Alibaba Cloud KMS

Real-time computing and event processing

• Principles of processing out-of-order changelog events in Flink SQL

• Integrate vector data using Flink

• Best practices for using timers in DataStream

• Real-time alert system for e-commerce marketing based on dynamic Flink Complex Event Processing (CEP)

Performance optimization and tuning

• Guide to tuning large-state jobs

• Guide to tuning Flink batch processing

• Guide to implementing efficient Flink user-defined functions (UDFs)

• Principles of processing out-of-order changelog events in Flink SQL

Development and O&M guidelines

• Guide to developing custom Flink triggers

• Use user-defined aggregate functions (UDAFs) to sort and aggregate data

• Workflow and guidelines for job development and deployment

• Best practices for secure access