What is the best strategy for 'Data Cleaning' in real-time streaming analytics?

All answers to this question.

Real-time cleaning is all about "Windowing." You should use a "Sliding Window" to calculate a Z-score or Interquartile Range (IQR) for the incoming stream. If a value falls outside the threshold, you can flag it as an outlier. For nulls, "Forward Fill" is the most common low-latency imputation method—simply using the last known good value. However, if the sensor stays dead for too long, you should trigger a "Data Quality Alert" rather than continuing to impute. The key is to keep the logic simple; complex statistical models for cleaning will inevitably introduce lag that makes "real-time" insights obsolete.

   Answered 2025-09-12 by Patricia Williams

If we use "Forward Fill," don't we risk creating "Artificial Trends" that could trigger false alarms in our predictive maintenance models?

   Answered 2025-09-14 by Susan Taylor

• You've hit on the major risk, Susan. To prevent this, we usually implement a "Max Imputation Limit." For example, we only forward-fill for three consecutive nulls. If the fourth value is still null, we stop the imputation and mark the data as "Missing." This forces the downstream model to treat it as a gap rather than a flat line. It’s better to have a hole in your data than a lie. Combining this with a simple "Kalman Filter" can also help smooth out the noise without creating the fake trends you are worried about, keeping the alerts accurate.

     Commented 2025-09-16 by Christopher Davis

We use "Delta Lake" to store the stream and run a "Medallion Architecture." We clean the data in the Silver layer before it hits the Gold layer.

   Answered 2025-09-18 by Jennifer Harris

• Totally agree. The Medallion Architecture is great for traceability. David, Patricia's Z-score approach is perfect for that Silver layer cleaning phase.

     Commented 2025-09-20 by David Carter