Dynamic data is data which varies rapidly and unpredictably. This kind of data is generally used in on-line decision making and hence needs to be delivered to its users comforming to certain time or value based application-specific requirements. The main issue in the dissemination of dynamic web data such as stock prices, sports scores or weather data is the maintenance of {\em temporal coherency} within the user specified bounds. Since most of the web servers adhere to the HTTP protocol, clients need to frequently {\em pull} the data depending on the changes in the data and user's coherency requirements. In contrast, servers that possess {\em push} capability maintain state information pertaining to user's requirements and push only those changes that are of interest to a user. These two canonical techniques have complementary properties. In pure pull approach, the level of temporal coherency maintained is low while in pure push approach it is very high, but this is at the cost of high state space at the server which results in a less resilient and less scalable system. Communication overheads in pull-based schemes are high as compared to push-based schemes, since the number of messages exchanged in the pull approach are higher than in push based approach. Based on these observations, this paper explores different approaches to combining the two approaches so as to harness the benefits of both approaches.