Use Apache Spark's batch processing capabilities to process the data at regular intervals, as real-time processing is not required.

Use Apache Spark Streaming to create a real-time ETL pipeline, with appropriate data sources, transformations, and sinks to handle the data efficiently.

Use a traditional database system to store and process the data, as it can handle high velocity and volume more effectively than Apache Spark.

Only process a subset of the data to reduce the volume and velocity, as real-time processing of the entire dataset is not feasible.