To perform real-time anomaly detection using Apache Spark, you would first ingest the time series data into Spark Streaming, which allows for real-time data processing. Then, you would use Spark's machine learning libraries, such as MLlib, to train an anomaly detection model on historical data. Finally, you would apply the trained model to the incoming data stream in real-time, using Spark's window functions to update the model's state and detect anomalies as they occur.

To perform real-time anomaly detection using Apache Spark, you would first ingest the time series data into Spark Streaming, which allows for real-time data processing. Then, you would use Spark's machine learning libraries, such as MLlib, to train an anomaly detection model on historical data. However, you would not apply the trained model to the incoming data stream in real-time.

To perform real-time anomaly detection using Apache Spark, you would first ingest the time series data into a traditional database system, which allows for real-time data processing. Then, you would use Spark's machine learning libraries, such as MLlib, to train an anomaly detection model on historical data and apply it to the incoming data stream in real-time.

To perform real-time anomaly detection using Apache Spark, you would first ingest the time series data into Spark Streaming, which allows for real-time data processing. Then, you would use a single machine to train an anomaly detection model on historical data and apply it to the incoming data stream in real-time, without leveraging the distributed computing power of Spark.