Data Validation

1 min read

Pronunciation

[day-tuh vah-luh-day-shun]

Analogy

Like a bank teller verifying that a deposit slip is properly filled out and the amount matches the cash before accepting the .

Definition

The process of checking data for correctness, completeness, and conformity to expected formats or ranges before it is used in applications.

Key Points Intro

Data validation ensures that only accurate and well‑formed data enters systems.

Key Points

Schema checks: Validates JSON, Protobuf, or CSV against predefined schemas.

Range checks: Ensures numeric values fall within acceptable bounds.

Cross-field rules: Verifies interdependent fields maintain logical consistency.

Proof verification: Checks cryptographic proofs (Merkle, SNARK) for on‑chain data.

Example

A validates that a submitted price feed includes a valid , non-zero price, and a proof-of-inclusion before updating the on‑chain .

Technical Deep Dive

Validation modules use JSON Schema validators in .js or to enforce field types, then run on‑chain require statements for proof verification via `verifyMerkleProof()`. Invalid submissions are rejected and slashed.

Security Warning

Insufficient validation can allow injection of malformed or malicious data; enforce checks both off‑chain and in smart contracts.

Caveat

Overly strict rules can reject legitimate edge-case data; balance strictness with flexibility.

Related Terms

Data Quality Metrics

Oracle

Blockchain & Cryptocurrency Glossary

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