This paper develops an incremental learning algorithm based on quadratic infer- ence function (QIF) to analyze streaming datasets with correlated outcomes such as longitudinal and clustered data. We propose a renewable QIF (RenewQIF) method within a paradigm of renewable estimation and incremental inference, in which parameter estimates are recursively renewed with current data and summary statistics of historical data, but with no use of any historical subject-level raw data. We compare our renewable estimation method with the oracle generalized estimating equations (GEE) approach that processes the entire cumulative subject-level data, and show theoretically and numerically that our renewable procedure enjoys statistical and computational efficiency. We also consider checking the homogeneity assumption of regression coefficients via a sequential goodness-of-fit test as a screening procedure on occurrences of abnormal data batches. We implement the proposed methodology by expanding existing Spark’s Lambda architecture to accommodate the screening tool box, which is examined via extensive simulation studies. Also, we illustrate the proposed method by an analysis of streaming car crash datasets from the National Automotive Sampling System-Crashworthiness Data System (NASS CDS).