Realtime Compute for Apache Flink:Best practices

Best practices guide

Data ingestion and Flink CDC

• Best practices for data ingestion based on Flink CDC

• Synchronize an entire MySQL database to Kafka using Flink CDC

• Use Realtime Compute for Apache Flink to read data from and write data to AnalyticDB for PostgreSQL

Real-time data warehouses and lakehouses

• Use Apache Paimon to build a streaming lakehouse solution

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

• Build a streaming data lakehouse by using Realtime Compute for Apache Flink, Apache Paimon, and StarRocks

Industry-specific use cases

• User behavior analysis with Flink, MongoDB, and Hologres

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

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

• Encrypt and decrypt database passwords in Flink with Key Management Service

Real-time computing and event processing

• Handle out-of-order changelog events

• Use Realtime Compute for Apache Flink to import vector data

• Best practices for using timers in DataStream

• Real-time early warning system for e-commerce marketing based on dynamic FlinkCEP

• Guide to exception handling

Performance optimization

• Performance tuning for large-state deployments

• Optimize batch processing of Realtime Compute for Apache Flink

• Write efficient user-defined functions

• Handle out-of-order changelog events

Development and O&M

• Develop custom triggers

• Real-time data aggregation with Flink

• Use a UDAF to sort and aggregate data

• Process and specifications of draft development and deployment

• Best practice of secure access