The output is a fourth-rank elastic tensor corresponding to the input structure, with different crystal systems possessing different numbers of independent components according to the symmetry in the crystal. Many other isotropic and anisotropic elastic properties can be directly derived from the elastic tensor, such as Young's modulus, shear modulus, bulk modulus, Poisson's ratio, linear compressibility, and sound velocity. Numerical values of these derived properties can be obtained via, e.g. pymatgen, and visual exploration is made possible via packages like elate. The present workflow is a direct adaptation of the original elastic workflow in the atomate package. Aside from default settings such as DFT pseudopotentials and energy cutoffs, the main difference is that this workflow includes the option to further optimize the input structure. The original elastic workflow has been widely employed to calculate the elastic tensor of materials. Notably, the elastic tensor data provided in the MP database are computed using this workflow. These data are driving the development of modern machinelearning models for predicting the elastic properties of materials. For instance, derived mechanical properties such as bulk modulus and shear modulus serve as key benchmarking properties in the MatBench suite. Furthermore, the data have been utilized to develop equivariant graph neural networks such as MatTen for predicting the full elastic tensors of all crystal systems.