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It is essential to process real-time data service requests such as stock quotes and trade transactions in a timely manner using fresh data, which represent the current real-world phenomena such as the stock market status. Users may simply leave when the database service delay is excessive. Also, temporally inconsistent data may give an outdated view of the real-world status. However, supporting the desired timeliness and freshness is challenging due to dynamic workloads. To address the problem, we present new approaches for 1) database backlog estimation, 2) fine-grained closed-loop admission control based on the backlog model, and 3) incoming load smoothing. Our backlog estimation and control-theoretic approaches aim to support the desired service delay bound without degrading the data freshness, critical for real-time data services. Specifically, we design, implement, and evaluate two feedback controllers based on linear control theory and fuzzy logic control theory, to meet the desired service delay. Workload smoothing, under overload, helps the database admit and process more transactions in a timely fashion by probabilistically reducing the burstiness of incoming data service requests. In terms of the data service delay and throughput, our closed-loop admission control and probabilistic load smoothing schemes considerably outperform several baselines in the experiments undertaken in a stock trading database testbed.