Data integrity
Data integrity is the property that data remains accurate, complete, consistent, and valid, with changes made only through authorized actions.
Software Security for Developers
Integrity is the property that data or a message remains accurate and unaltered during transmission or storage.
- How it is used
- Protects requests, stored records, files, and service-to-service messages against accidental corruption and unauthorized modification.
- How deep it goes
- Defines integrity, contrasts hashes with HMAC and JWS, and demonstrates verification and rejection without covering cryptographic proofs.
- Examples
- Uses browser requests, database records, checksums, refunds.json, HMAC, and refunds.jws to illustrate corruption and tampering.
- What you can do after
- Evaluate integrity mechanisms and apply hashes, HMAC, or JWS according to corruption, tampering, and authenticity requirements.
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Acing the CCNA Exam: Volume 2 Advanced Networking and Security
Integrity is the property that systems and data remain trustworthy and are not altered except by authorized entities.
- How it is used
- Frames integrity as a core information-security goal for protecting systems and data from unauthorized alteration.
- How deep it goes
- Defines unauthorized alteration, explains wireless bit-flipping, and identifies checksums, but does not detail specific algorithms or configurations.
- Examples
- Uses encrypted wireless ciphertext, bit-flipping attacks, and appended checksums as concrete integrity examples.
- What you can do after
- Evaluate whether encryption alone protects message integrity and recognize checksum-based protection against wireless alteration.
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Build Financial Software with Generative AI (From Scratch)
Data integrity is the preservation of data's accuracy, consistency, completeness, and validity during storage and use.
- How it is used
- Supports reliable storage and querying of ACH files that are difficult to manage as flat files.
- How deep it goes
- Defines schema-level safeguards and examines a deletion failure that requires database and test rework.
- Examples
- Uses hierarchical ACH records, unparsed and parsed storage, and deletion of a batch with associated type 6–8 records.
- What you can do after
- Evaluate/understand how keys, constraints, and deletion behavior prevent orphan records and improve database consistency.
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Just Use Postgres!
Data integrity is the correctness, consistency, and validity of data throughout its storage and modification.
- How it is used
- Uses database rules and transactions to keep application data accurate, consistent, and complete during storage and modification.
- How deep it goes
- Explains data types and six constraint categories, then demonstrates adding and testing checks; it stops short of deeper transactional internals.
- Examples
- Demonstrates product catalogs, product reviews, category and stock checks, positive prices, required review text, and review ranks from one to five.
- What you can do after
- Apply it via PostgreSQL constraints to reject invalid prices, missing reviews, unsupported categories, and out-of-range values.
MongoDB 8.0 in Action, Third Edition
Data consistency is the property that stored data remains accurate, coherent, and compliant with defined integrity constraints.
- How it is used
- Keeps related MongoDB updates from leaving partial results and frames protection against unauthorized alteration or data loss.
- How deep it goes
- Explains single-document atomicity, demonstrates multidocument transactions, and gives operational cautions; does not develop a general integrity-constraint specification.
- Examples
- Uses a flight-and-hotel booking rollback and a Node.js transaction updating accounts, customers, and transaction records.
- What you can do after
- Apply it via the MongoDB Node.js Driver’s withTransaction method to group dependent updates and handle failures.
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Authorization in Action
Relationship integrity is the preservation of confidence that an interaction continues to involve the same authorized entity in an identity relationship.
- How it is used
- Establishes trustworthy identity context before authorization applies access-control policies.
- How deep it goes
- Explains relationship integrity through authentication, life-cycle phases, mutual authentication, attack methods, and governance rather than authorization-policy design.
- Examples
- Uses the Twitter account takeover and a deceptive certificate for twitter.com.info to illustrate broken trust.
- What you can do after
- Evaluate/understand how weak authentication corrupts otherwise valid authorization decisions and damages dependent relationships.
Architecting an Apache Iceberg Lakehouse
Data integrity is the preservation of data's correctness, completeness, consistency, and protection from unauthorized alteration or loss.
- How it is used
- Guides storage-layer evaluation by translating reliability, recovery, and consistency needs into architectural requirements.
- How deep it goes
- Explains durability and synchronized Iceberg recovery, while deferring deeper disaster-recovery treatment and detailed checksum mechanics.
- Examples
- Uses an intermittently connected industrial IoT edge deployment and storage comparisons involving MinIO, Ceph, object storage, and block storage.
- What you can do after
- Evaluate storage candidates and design recovery procedures that preserve synchronized Iceberg data files and transactional metadata.
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Real-World Cryptography
Integrity is the property that data has not been altered in an unauthorized or undetected way.
- How it is used
- Protects downloaded files, distributed chunks, messages, and public-key identities from undetected modification.
- How deep it goes
- Explains hashing behavior, second pre-image resistance, secure digest distribution, HMAC use, and replay limitations without covering replay prevention.
- Examples
- Uses OpenSSL SHA-256 checks, BitTorrent chunk verification, Tor onion addresses, and Rust HMAC message exchange.
- What you can do after
- Apply it via OpenSSL, chunk digests, or HMAC to detect altered files and messages.
Troubleshooting Java, Second Edition
Data integrity is the property that data remains accurate, consistent, complete, and trustworthy throughout its life cycle and across system boundaries.
- How it is used
- Detects corruption, drift, missing relationships, and synchronization delays across distributed system boundaries.
- How deep it goes
- Defines the property and gives implementation patterns with checksums, hashes, reconciliation jobs, and event-log correlation.
- Examples
- Uses a CRC32-verified executable, hashed invoice records, account-balance discrepancies, and Kafka event replay.
- What you can do after
- Diagnose integrity failures by comparing checksums, hashes, expected state, actual state, and event timelines.
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Java Persistence with Spring Data and Hibernate epub
Data integrity is the preservation of correctness and consistency of data and its enforced relationships.
- How it is used
- Preserves consistent object associations and evaluates relational inheritance strategies against constraint and normalization requirements.
- How deep it goes
- Explains two-sided association updates with validation and compares inheritance schemas, stopping short of provider-specific implementation details.
- Examples
- Uses Item–Bid Java classes, a bid-linking helper, auction bid amounts and timestamps, and competing inheritance table strategies.
- What you can do after
- Apply association helpers and mapping choices that keep object references, constraints, and stored data consistent.
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Statistics Slam Dunk
Data integrity is the accuracy, consistency, completeness, and validity of data throughout its collection and processing.
- How it is used
- Uses data wrangling and validation to prepare salary records for inequality analysis.
- How deep it goes
- Works through selection, factor conversion, summaries, anomaly queries, coverage filtering, and grouped ranking.
- Examples
- Uses blank team records, sparse early team-season groups, and salary rankings for Denver Nuggets and Chicago Bulls.
- What you can do after
- Diagnose data-quality problems and prepare grouped records for a defensible comparison.
Grokking Relational Database Design
Data integrity is the property that data remains accurate, complete, valid, trustworthy, and protected from unintended alteration or loss.
- How it is used
- Uses database design checks to preserve accurate, complete, consistent, and trustworthy data.
- How deep it goes
- Defines integrity, identifies anomalies, and demonstrates a foreign-key correction without detailing every constraint type.
- Examples
- Uses author and book tables to show how a missing foreign key permits orphan records.
- What you can do after
- Apply it via SQL by reviewing keys, data types, relationships, and constraints for integrity gaps.
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PostgreSQL Mistakes and How to Avoid Them
Data integrity is the accuracy, consistency, and trustworthiness of stored data throughout its lifecycle.
- How it is used
- Protects migrated and operational data from encoding loss, corruption, and unsafe technology or administration choices.
- How deep it goes
- Provides a migration checklist and diagnostic response, but stops short of detailed corruption-repair procedures.
- Examples
- Uses invalid UTF-8 bytes, null bytes, conversion commands, and a PostgreSQL invalid-page log as concrete failure evidence.
- What you can do after
- Diagnose integrity risks with PostgreSQL logs and pg_checksums, and operate migrations using encoding conversion, cleaning, configuration, and realistic-data testing.
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API Design Patterns
Integrity is the property that received data exactly matches the data that was sent and has not been altered in transit.
- How it is used
- Protects API requests from transit tampering and guides resource boundaries when duplicated shared data could become inconsistent.
- How deep it goes
- Defines tampering, compares network protection with request verification, and explains duplication risks without detailing signing implementation.
- Examples
- Uses library books and authors, duplicate group membership, and signed HTTP method, path, host, and body content.
- What you can do after
- Evaluate resource boundaries, preserve shared-data consistency, and verify or reject modified HTTP requests.
Data Without Labels
Data integrity is the preservation of accurate and consistent relationships among related tables, variables, and entities.
- How it is used
- Protects relationships among data entities during collection, loading, transformation, and cross-system use.
- How deep it goes
- Defines relational consistency and illustrates it with employee tables, but stops short of specifying validation code or procedural checks.
- Examples
- Uses employee IDs across linked tables and conflicting revenue figures across systems as concrete integrity cases.
- What you can do after
- Evaluate whether related tables, entities, and cross-system values remain consistent before analysis.
Python How-To
Data integrity is the preservation of data's correctness, validity, and consistency throughout its lifecycle.
- How it is used
- Protects mutable class properties and screens experimental data files before processing.
- How deep it goes
- Explains setter validation and file metadata checks with code, but does not inspect file contents for integrity.
- Examples
- Uses Task.status assignments, allowed status values, experimental .dat files, size cutoffs, and modification timestamps.
- What you can do after
- Apply it via property setters and pathlib metadata checks to reject invalid values and flag suspicious files.
Starting Data Analytics with Generative AI and Python
Data integrity is the preservation of data's accuracy, consistency, completeness, and trustworthiness throughout its handling.
- How it is used
- Evaluates whether data relationships remain coherent and meaningful before text analysis.
- How deep it goes
- Defines integrity through maintained relationships but does not detail validation algorithms.
- Examples
- Uses product reviews, competitor records, stale data, duplicate complaints, and order links.
- What you can do after
- Evaluate review data for broken relationships, duplication, missingness, irrelevance, and staleness before analysis.
Read how Starting Data Analytics with Generative AI and Python explains data integrity →
AI for Everyday IT
Data integrity is the preservation of data's accuracy, consistency, completeness, and trustworthy state throughout its lifecycle.
- How it is used
- Protects trustworthy documents and databases during AI processing, storage, verification, recovery, and routine maintenance.
- How deep it goes
- Explains practical controls and verification, but stops short of detailed database integrity procedures or AI implementation internals.
- Examples
- Uses AI-processing headers, secure document versions, monthly audits, database maintenance prompts, and data-dictionary checks as examples.
- What you can do after
- Apply it by documenting AI handling, verifying outputs, preserving versions, and validating database maintenance queries before execution.
Related concepts
- CIA triadpart of · The CIA triad is an information-security model comprising confidentiality, integrity, and availability as core protection objectives.
- cipher block chaining modecontrasts with · Cipher block chaining is a block-cipher mode that XORs each plaintext block with the preceding ciphertext block before encryption, using an initialization vector for the first block.
- authenticityincludes · Authenticity is the quality of being genuine and faithfully representing a claimed identity, source, origin, expression, experience, or content.
- constraintaddressed by · A constraint is a condition or rule that restricts the permissible states, values, actions, or solutions in a system or problem.
- cryptographyaddressed by · Cryptography is the study and application of mathematical techniques for protecting information and communications through confidentiality, integrity, authentication, and related security properties.
- digital signatureproduced by · A digital signature is cryptographic data generated with a private key and verified with corresponding public information to authenticate data or its signer and detect alteration.
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