Use Apache Spark's batch processing capabilities to process the time-series 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 time-series data efficiently, considering time-window operations and data aggregation.

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

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