Data Validation Articles
Practical data validation articles for ETL workflows, source files, and automated data quality checks.
Data validation stops bad values before they reach the target database, report, or automated workflow. Start with required fields and data types, then add range, lookup, and relationship checks where the business risk justifies them.
Validation rules worth automating
These articles explain the common validation checks used in Advanced ETL Processor. They are practical checks for real source files, not theory wearing a clean shirt.
22 Data Quality Checks Every Data Engineer Should Know
A practical table of 22 data quality checks, what each check does, why it matters, and how to handle failures in ETL workflows.
How to Improve Data Quality Using Automated ETL Validation
Use automated ETL validation rules to catch missing values, bad formats, invalid ranges, and failed records before loading.
What is Data Validation?
Learn what data validation checks, why it matters in ETL workflows, and which validation rules to automate first.
File Duplication Validation
Calculate a file checksum, store it in a database, and check whether the file was processed before to prevent double data processing.
Uniqueness Validation
Check primary keys, business keys, and repeated source records before duplicate values create bad updates or double reporting.
Consistency Validation
Check related fields for conflicting values, enforce business rules, and prevent invalid data combinations before loading.
Cross-Table Validation
Compare data across different tables to confirm that customer codes, accounts, and references exist and are active.
Duplicate Detection
Find repeated rows and repeated business events before duplicate records inflate totals or create unreliable reports.
Date Format Validation in ETL Workflows
Date format validation prevents ambiguous dates and invalid calendar values from entering ETL pipelines.
Creating Complex Data Validation Rules
Complex data validation rules combine several checks so ETL workflows catch missing, invalid, inconsistent, and risky values before loading.
Equality Validation in ETL Workflows
Equality validation compares values between fields, systems, or calculated results to catch mismatched ETL data.
Field Data Type Validation in ETL Workflows
Field data type validation catches text in numeric fields, invalid dates, and other mixed-type data before loading.
Empty String Validation in ETL Workflows
Empty string validation catches blank text values that look harmless but break joins, reports, and business rules.
Field Length Validation in ETL Workflows
Field length validation prevents truncation, failed loads, and oversized values in ETL pipelines.
Lookup Validation in ETL Workflows
Lookup validation checks source values against reference data before ETL loads continue.
Range Validation in ETL Workflows
Range validation catches values outside expected limits before they damage reports, pricing, or operational workflows.
Referential Integrity Validation in ETL Workflows
Referential integrity validation prevents orphan records and broken relationships during ETL loading.
Required Field Validation in ETL Workflows
Required field validation stops missing values before they break ETL loads, reports, and downstream business rules.
Typical Data Quality Issues in ETL
Typical data quality issues include missing values, inconsistent formats, duplicates, typing errors, and non-numeric characters in numeric fields.
Business Rule Validation
Business rule validation applies organization-specific checks so values follow policy, not just format, type, and range rules.
Pattern Validation
Pattern validation uses rules or regular expressions to check values such as UK postcodes, codes, IDs, and formatted text before loading.
What data validation checks catch before loading
Good ETL validation catches missing required fields, unexpected data types, invalid dates, oversized text, duplicate keys, failed lookups, and broken parent-child relationships before they reach the target system.
The safest pattern is simple: read the source, validate the rows, route failures, then load clean data. Raw files stay unchanged, rejected rows stay visible, and nobody has to explain why `N/A` became a customer birth date.
When to validate before loading
Validate before loading when the data feeds a report, a customer system, a finance process, or an automated decision. If the target system rejects bad rows anyway, ETL-side validation still helps because it gives clearer logs and better exception handling.
For a broader checklist, start with 22 data quality checks every data engineer should know. For product setup, see the validation rules documentation and the Validator object documentation.
How Advanced ETL Processor handles failed rows
Advanced ETL Processor Enterprise can validate rows inside a visual workflow, log validation errors, and route rejected records without changing the original file. That makes scheduled imports safer because every run follows the same rules.
Use it when validation needs to be repeatable, auditable, and boring. Boring is excellent here. Boring means nobody is fixing a spreadsheet at midnight.
Next steps
Start with the common data quality problems, then decide which simple and complex rules your workflow needs. Test on a small sample before scheduling the job. Ten bad rows are easier to explain than ten million.
Keep the original data unchanged. Raw files are still the witness statement.