The Data Cube Lattice approach, initially developed over 20 years ago, has become feasible with recent advancements in big data and columnar storage engines. It's a set of dependent materialized views used to answer queries, which can significantly reduce data access costs and time. The original problem is finding the optimal query set from the Data Cube Lattice that should be materialized given the query set that needs to be answered. A classic solution uses a greedy algorithm, but it has limitations, such as requiring exponential computation for large datasets. Modern approaches, like the Monte Carlo algorithm and assisting the greedy algorithm with query weights or probabilities, aim to improve performance. The Cube.js framework uses a querying history constrained greedy algorithm, which reconstructs a lattice based on querying history and drastically reduces lattice computation cost. This approach can reduce data access costs and time by 99% compared to current methods, transforming the way people work with analytics.