Ab Initio Data Quality [repack] Guide

Ab initio (Latin for "from the beginning") means starting from first principles. In a quantum simulation, you don't patch errors later—you define the laws of physics upfront. If your initial conditions are wrong, the simulation is worthless.

Use tools like pydantic (Python), Great Expectations (with expect_column_values_to_not_be_null set to fatal ), or dbt 's constraints (enforced, not just documented). If the contract fails, the pipe breaks. Loudly. ab initio data quality

You enforce quality at the point of creation or ingestion. If a record doesn’t meet the first principles of your domain (e.g., timestamp cannot be in the future; customer_id must match a regex), it is rejected immediately. The rule: Do not allow a known violation to enter your persistent storage. Ever. 2. The "Nullable Integer" Paradox Let’s look at a classic first-principles failure: Nulls in numeric fields. Ab initio (Latin for "from the beginning") means

Go ab initio , or go home. [Your Name] writes about the intersection of rigorous engineering and practical data science. Disagree with the zero-NULL policy? [Link to comments or Twitter.] Use tools like pydantic (Python), Great Expectations (with

Most data teams focus on reactive data quality (DQ). They let data in, then scramble to fix it. But what if we borrowed a concept from theoretical chemistry and quantum physics? What if we focused on ?