Comparedata consistency without downtime

A data reconciliation module that checks whether the target database still faithfully reflects the source during migration, replication, reporting, or active-active setups.

online
without stopping databases
3
comparison algorithms
4+
database engines

Why Compare exists

Confidence that the data on the other side is the same data

Compare is a consistency control module in the GIFRÖST ecosystem. It helps teams quickly confirm that a target, reporting, or standby environment truly reflects the source - without manual spreadsheets, long scripts, or stopping systems.

  • 01

    Verification before a decision

    It gives a clear signal whether migration, replication, or data refresh can safely move to the next step.

  • 02

    Control while systems run

    Comparisons can run repeatedly while data is still changing and the business has no room for long downtime.

  • 03

    Faster mismatch detection

    Instead of hunting differences manually, the team sees where configuration, transformation, or replication needs attention.

Scenarios

Where data-copy quality has real consequences

The product is especially useful where data is moved, replicated, or shared across teams and an error can create business, audit, or operational risk.

  • Migrations and cutover
  • Replication and disaster recovery
  • Active-active and reporting

Control algorithms

From a quick check to full validation

Compare does not force one operating mode. Teams can start with a lightweight row-count check, move to detailed value comparison, or use sampling when tables are very large and recurring control matters most.

01

Row Count — the first step in data validation

The shortest path to answer whether data volume after migration, replication, or a batch load matches between source and target.

02

Row Level — detailed validation of critical data

Full value comparison helps support cutover decisions, incident analysis, or audit work where record counts alone are not enough.

03

Row N-Level for large-scale data control

Sampling every n-th record keeps recurring checks practical for large tables without creating unnecessary pressure on the environment.

Connections and topology

Comparison can run close to the data

Depending on architecture, Compare can use direct database connections or agents running closer to source and target systems. This makes it suitable for simple environments as well as distributed, hybrid, or tightly secured topologies.

  • Direct Database Connection
  • Agent-Based Connection
  • Hybrid environments

Mapping and scope

First define the comparison scope

In real projects, starting a comparison is rarely the only challenge. It is more important to choose the right schemas, tables, and exclusions so the result is useful instead of being buried under technical objects.

  • Name rules
  • Pair review
  • Column control

How Compare works

From defining scope to a validation result

The technical settings sit underneath, but the product user mainly works through a simple flow: choose source and target, define table scope, run the check, and review a result that can become an operational decision.

  • Comparison scope

    Mapping rules help narrow the check to the right schemas and tables instead of comparing everything at once.

  • Control mode

    Teams can start with a quick volume check and move to deeper validation for critical data.

  • A result that supports decisions

    The result should show where the problem is and what needs a decision, rather than burying the team in low-level details.

Technical foundation

Flexible architecture, simple operation

Compare uses direct or agent-based connections, supports different comparison depths, and lets teams tune execution parameters to data scale. Global settings, error limits, rechecks, and batch parameters help run controls calmly without overloading source systems.

  • For large data sets

    Execution parameters and an agent-based approach help move part of the work closer to the data.

  • For different databases

    The product supports projects where source and target are not always the same database type.

  • For operations teams

    Limits, rechecks, and process settings help reduce noise and pressure on the environment.

Want to verify consistency after migration or replication?

Tell us about the source, target, and data scale. We will match the Compare scenario to risk and the operating window.

Book a Compare consultation