Tackling Missing Data in Time Series

Working with time series data can be challenging, especially when gaps appear due to issues such as network interruptions or faulty devices with low battery life. These missing data points disrupt analysis and decision-making, leaving data consumers unsure about the reliability of insights.

To make matters worse, delayed data — sometimes arriving days after the fact — or completely missing records can compound the problem. Addressing these issues effectively requires thoughtful planning and robust techniques.

A Roadmap to Address Missing Data

Here are three key strategies to tackle missing data in time series and ensure your datasets remain reliable:

1. Reprocess Recent Data

Design your incremental processing logic to reprocess the last X days of data, depending on the latency and characteristics of your data pipeline. This approach ensures that delayed data arrivals are captured and incorporated seamlessly into your analysis. By revisiting recent data in each batch or streaming job, you reduce the risk of overlooking late records.

2. Use Upsert for Accuracy

To maintain an accurate and up-to-date target table, implement Upsert (update and insert) logic. This approach…