Sliding window analytics is often used in distributed data-parallel computing for analyzing large streams of continuously arriving data. When pairs of consecutive windows overlap, there is a potential to update the output incrementally, more efficiently than recomputing from scratch. However, in most systems, realizing this potential re-quires programmers to explicitly manage the intermediate state for overlapping windows, and devise an application-specific algorithm to incrementally update the output. In this paper, we present self-adjusting contraction trees, a set of data structures and algorithms for transparently updating the output of a sliding window computation as the window moves, while reusing, to the extent possible, results from prior computations. Self-adjusting contraction trees structure sub-computations of a data-parallel computation in the form of a shallow (logarithmic depth) balanced data dependence graph, through which input changes are efficiently propagated in asymptotically sub-linear time. We implemented self-adjusting contraction trees in a system called Slider. The design of Slider incorporates several novel tech-niques, most notably: (i) a set of self balancing trees tuned for dif-ferent variants of sliding window computation (append-only, fixed-width, or variable-width slides); (ii) a split processing mode, where a background pre-processing stage leverages the predictability of input changes to pave the way for a more efficient foreground pro-cessing when the window slides; and (iii) an extension of the data structures to handle multiple-job workflows such as data-flow query processing. We evaluated Slider using a variety of applications and real-world case studies. Our results show significant performance gains without requiring any changes to the existing application code used for non-incremental data processing.