Yoda Response: Address the lag in real-time data processing, we must. Several factors, consider we should.

First, check for data failures. If many messages are failing and pushed to the Dead Letter Queue (DLQ), time-consuming this can be. Ensure proper handling of failures, we must.

Next, examine any external API calls or database interactions. Optimal time they should take. From logs, identify latency, we can.

Also, consider the volume of incoming data. If too large, overwhelmed the system may be. Increase the number of parallel executors, we can, whether using PySpark, Apache Beam, or Apache Flink. This approach, faster data processing, it ensures.

Check the format and size of incoming data, we must. Ensure correct and manageable, it is.

Thus, by identifying failures, checking external interactions, managing data volume, and increasing executors, address the lag in real-time data processing, we can.