We utilize a lambda architecture, which incorporates the concept of precomputed tables as well. Those precomputed tables store intermediate representation of the final results. These precomputed tables are capable of providing snapshot or daily accuracy features. However, when it comes to real-time features that require point-in-time correctness, using precomputed tables may present challenges.

For the offline computations, we will reuse those intermediate results to avoid calculation from the beginning again. So the engine can actually scale sub-linearly.