ACID - Consistency
ACID - Consistency
- Write the irreducible insight. 50–120 words.
- State the claim/definition in one sentence first.
- Remove source phrasing; keep mechanism or rule.
- If you feel the need for “and,” split into another atom.
Consistency is the ACID property that ensures data remains in a valid state both before and after any transaction is executed. If a modification process fails or encounters invalid data, the system relies on mechanisms such as rollback to revert the data to its original, known good state, preventing the creation of partially completed or corrupt changes. This property is enforced by utilizing schema-on-write and validation controls, guaranteeing that transactions adhere to predetermined rules and integrity constraints.
- What decision does this change?
- What prediction does this enable?
- What failure does this prevent?
• Error Mitigation: Consistency ensures that if a process fails during a data modification, all changes are rolled back to the original state, thereby avoiding partially completed changes and maintaining data integrity.
• Data Governance: It is enforced primarily through schema enforcement and evolution (schema-on-write), which acts as a controlling process to check a writer's schema against the existing table before allowing a transaction to occur.
• Data Quality and Trust: Consistency is a key driver for successful data governance frameworks, where adherence to quality standards ensures that analytics and AI outcomes remain trustworthy.
- Does this collide/agree with an existing atom?
- Add at least one forward link to a molecule/canonical note.
- Add one tag-like topic (2–5 terms, not a dump).
- Broader topic: Data Integrity, Schema Governance, Data Quality
- Related atoms: ACID - Atomicity, ACID - Isolation, ACID - Durability
- Upstream source note:
Paste exact quote or figure caption.
Consistency | Ensures data remains in a valid state after any transaction. | Rollback of changes upon process failure; Schema enforcement/evolution. |
Delta Lake’s schema evolution and schema enforcement ensure data consistency and quality by enforcing a schema on write operations and allowing users to modify the schema without breaking existing queries. They also prevent developers from inadvertently inserting data with incorrect columns or types, which is crucial for maintaining data quality and consistency.
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