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   ● Dataproof capacity

The inconsistency among its banks already exists. It just hasn't caused a visible problem yet.

Dataproof connects to its databases, applies the business rules of the operation and identifies inconsistencies before they become production errors. Each find comes with evidence ready for auditing.

supported banks

Validate in multiple technologies with a single rule engine

DataProof works as a validation layer on the infrastructure you already have. No bank replacement, no migration required.

PostgreSQL

Oracle

MySQL

SQL Server

Trino

AWS RDS

Azure SQL

Intra-bank validation

Checks tables, columns, domains and dependencies within the same bank. Widely used for quality checking after a data load or to confirm that the regulated domains are being respected.

Inter-bank validation

It compares two different banks, even if they are different technologies. Designed especially for phased migrations, when the legacy system and the new need to live together for a period.

What does DataProof validate

Six types of inconsistency that a single execution of Dataproof can identify

Domain

Values respect lists, ranges and regulatory standards, in addition to the technical type.

Dependence

If mode = credit, rate can never be zero. Impossible logic detected.

Completeness

Mandatory fields for the process, not just technical not null.

Oneness

Business key duplicates that would undergo technical constraints.

Cross-table consistency

Balance in the balance table beats with the sum of moves? Dataproof checks.

Calculus

Reprocesses interest, fees and totalizers. Detects rounding and wrong logic.

how it works

From Connecting to the Evidence Report in 3 Steps

01

Connect with scope control

Read-only credentials. Native integration with Azure AD and AWS S3. IT controls access; business sets the rules.

02

Configure business rules without code

Compliance Edits domains, dependencies and consistencies in the interface. Without opening call to you with each adjustment.

03

Act with actionable evidence

Column, row, key, rule, value. Segregated problematic records; Healthy follow the flow immediately.

use cases

Where the validation of databases generates immediate value

ETL/pipeline afterload

After each intake, check that the critical data arrived correct. Eliminate the risk of “ETL ran successfully, but the data is wrong”.

Migration to new banking core or ERP

Compare legacy vs. new core continuously during the coexistence. Migration approved with technical evidence, not by manual sampling.

Checking of Regulated Domains

Validate data before generating files for BACEN, CVM, or the Brazilian Federal Revenue Service. Prevent rejections caused by data inconsistencies at the source.

Quality for Analytics and BI

Intact data in DW and Data Lake. Management reports and AI models with reliable base.

Replicas and contingency environments

Periodically check that the standby seats are synchronized. Discover the divergence before you need the failover.

The difference in practice

With DataProof vs. traditional approach

No DataProof

Business error goes through the technical validation of ETL. Consequence appears in the process, in the report or in the regulator’s notification… too late.

With DataProof

Business validation applied after each load. Inconsistency segregated with evidence. Healthy batch follows immediately. Compliance has the report ready.

with DataProof database DataProof segregated 99.8% BACEN ✓ approved audit trail generated without DataProof database no validation BACEN ✗ rejected rework fines regulatory impact 99.8% approval rate 100% risk

Frequently Asked Questions about Database Validation

Does DataProof need write access to the bank?

No. Access only with read permissions. Writing in evidence records occurs in the Dataproof environment, never directly in the validated bank.

Constraints validate technical structure (type, size, not null, foreign key). DataProof validates business logic: dependencies, calculations, regulatory domains, cross-table consistency. They are complementary layers.

No. Uses optimized reading with data window and controlled parallelism. For high volume, available in On-Premises with dedicated infrastructure.

Directly in the DataProof interface, without depending on development. When the BACEN manual is updated, Compliance adjusts the rules on the same day.

Combine capabilities

Complete quality chain

Multi-source reconciliation

After validating each bank, compare them with each other or with files and APIs. A complete quality chain.

Regulatory Layouts

Validate data at the source (database) before generating files for BACEN or the Brazilian Federal Revenue Service. End-to-end protection and compliance assurance.

Next step

Want to know what inconsistencies exist today among your banks before they become an error in production?

Free Diagnosis: We map the first validation flow with the greatest risk in your environment.

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