DATABASE CHECK

Database Testing

Check that your data is accurate and reliable, and that it performs well. Automate constraints verification, SQL queries analysis, stored logic runs, and schemas checks instantly.

🗄️ queries.sql

Select a data-validation routine to load parameters into the compiler:

SELECT * FROM users

Verify data accuracy, column checks, and ACTIVE constraints validation rules.

SELECT * FROM orders

Assert foreign key integrity, balance totals verification, and schema constraints.

CALL sync_inventory()

Trigger database stored logic procedures, triggers validation, and execution timelines.

📊 query_output_sandbox
id username email status
1 alex_tester alex@zequto.com active
2 sarah_qa sarah@zequto.com pending
3 john_dev john@zequto.com active
Connecting PostgreSQL client sandbox...
# Select a SQL query template and press 'Execute SQL Query' to validate...

Why Database Testing?

Databases are key to modern applications because they store and process important business data. Manual database testing takes a lot of time and can cause errors. Automated database testing checks data accuracy, database objects, transactions, and performance, so your applications work well in any environment.

Logical Integrity Checks: Automatically check database structures, foreign-key ties, triggers, and functions.

ETL Schema Confirmations: Keep records accurate and verify transformations pipelines integrity.

Key Capabilities

Ensure data layer accuracy and execute relational integrity validations seamlessly.

AI-Powered SQL Gen

Create relational database validation scripts quickly with little manual coding.

Automated Data Checks

Checks records structures, constraints, and business logic across databases tables.

Stored Procedures & Logic Testing

Verify stored functions, triggers, and backend logic procedures execution stability.

Schemas & Constraints Validation

Verify table relationships, primary/foreign key connections, and schema drifts.

Data-Driven Assertions

Execute identical query checks across multiple environment parameters and data scopes.

Automated SQL Verification

Confirm SQL script query syntax validity, execution metrics, and retrieve values.

CI/CD Automation Flow

Triggers automatic database scripts check validation on pipeline build deployments.

Detailed Logs & Metrics

Audit test results history records, execution performance stats, and database errors.

Explore Use Cases

Select a database scenario to view Zequto schema validation code.

Data Validation Testing

Validate records parameters consistency, null value checks, and formatting rules.

Database Schema Checks

Assert structural constraints integrity, tables structures, and indexes mapping.

Stored Procedures & Triggers

Verify procedural calculations outcomes, calculations parameters, and triggers events.

ETL & Migration Testing

Check datasets migration validation pipelines logic, checks mapping, and transformation metrics.

Visual Builder

How Database Testing Works

Click on any step below to reveal execution database validation logs.

1
STEP 01

Schema Analysis

Review database schemas, tables, and business rules to understand your application's data structure.

[DB PARSER - STEP 01]
> Connected database client to sandbox environment PostgreSQL.
> Reading schema tables constraints definitions catalog... parsed 18 tables.
> Discovered database objects: triggers, functions, index mappings.
2
STEP 02

Test Generation

Automatically create database test cases for data validation, SQL queries, and backend workflows.

[DB PARSER - STEP 02]
> Compiling SQL validator test scripts...
> Created assertions: SELECT record checks, stored procedures triggers hooks.
> Target file output: `db_integrity.spec.js` mapped.
3
STEP 03

Multi-Environment Execution

Run tests across multiple environments with different datasets and configurations.

[DB PARSER - STEP 03]
> Executing database migration sandbox...
> Target host: db-cluster.staging.zequto.internal:5432
> Dispatched parallel queries checks.
4
STEP 04

Data Layers Validation

Check data integrity, stored procedures, triggers, transactions, and business logic to make sure everything works as expected.

[DB PARSER - STEP 04]
> Asserting triggers and stored procedural logic outputs...
> Transaction rollback condition checks: PASSED.
> Primary keys uniqueness checks: 100% SUCCESS.
5
STEP 05

Continuous Sync

Keep your database tests up to date and run them regularly as your schemas and application logic change.

[DB PARSER - STEP 05]
> Discovered drift on table "orders" (added column "tax_percent" not present in schema spec).
> Schema script updated automatically. Verified triggers output variables bindings.

Benefits

Realize tangible efficiency gains and secure database testing verification.

Minimize Manual Tasks

Workload

Cut down on manual database testing by automating repetitive data checks and backend procedures.

Highly Accurate Data

Reliability

Keep your data accurate and consistent by regularly checking records, relationships, constraints, and constraints.

Shift-Left Defect Catching

Prevention

Find database schema or constraints issues early in the software development lifecycle, catching bugs before production.

Exceptional Application Stability

UX Safety

Make your applications more reliable and keep data safe by testing database operations and transaction rollbacks.

Continuous Quality Assurance

CI/CD Sprints

Speed up your release cycles by including automated database validation queries checks in your continuous pipeline.

Advanced Calculations Check

Business Rules

Efficiently validate complex business rules by automating the verification of database triggers logic, calculations, and workflows.

Reusability Across Hosts

Scalability

Reduce maintenance overhead by using reusable test query scripts that work across different environments.

Optimized QA Performance

Productivity

Help your QA team work more efficiently by automating database checks, letting testers focus on complex data validation tasks.

Why Choose Zequto for Database Testing?

Zequto makes database testing easier with AI-powered automation, smart validation, reusable test assets, and smooth CI/CD integration. QA teams can verify data accuracy, catch backend issues early, increase test coverage, and ensure reliable application performance with less effort.