Varnix Technologies
Agroholding · Data platform QA

MHP: quality assurance for an agricultural digitalization platform

Six months, a team of three and a full QA cycle for MHP's agricultural digitalization platform: six types of testing plus data-quality and analytical-logic checks, so business users could trust the reports.

MHPAgroholding, Ukraine · Data platform QA
from the first tests to acceptance
6 monthsfrom the first tests to acceptance
people on the Varnix team
3people on the Varnix team
types of testing in one QA cycle
6types of testing in one QA cycle
acceptance testing before business rollout
UATacceptance testing before business rollout
Client
MHP, agroholding, Ukraine
Platform
Power BI, Azure Synapse data warehouse, API integrations
Scope
Full QA cycle, data quality and analytical logic
Duration and team
6 months, a team of three
The starting point

Where the client was

  1. 1Power BI reports had to show correct figures, calculated by the agreed business rules.
  2. 2Data reaches the warehouse through API integrations, so each interface had to work on its own and end to end.
  3. 3New releases could not break reports and integrations that already worked.
  4. 4The warehouse and reports had to hold up under load.
  5. 5Business users had to accept the platform before relying on it.
What we tested

Every layer of the platform

Integrations
  • API integrations
  • Interface tests
  • End-to-end data checks
Data warehouse
  • Azure Synapse
  • Data-quality checks
  • Analytical logic
Reporting
  • Power BI reports
  • Filters and calculations
  • Functional tests
Users
  • Business users
  • Acceptance testing
Controls throughoutRegression after changesLoad testingFull QA cycle
TechnologyMicrosoft Power BIAzure Synapse AnalyticsSQLAPI testing
How we tested

Six types of testing in six months

1

Functional testing

Reports, filters and calculations checked against the requirements.

2

Integration and API testing

Each API integration tested on its own, then the data followed from the interface through the warehouse to the report.

3

Data quality and analytical logic

Report figures checked against the data in Azure Synapse, and calculation rules checked against their business definitions.

4

Regression testing

Repeated after changes, so reports and integrations that already worked kept working.

5

Load testing

The warehouse and reports checked under load before business users depended on them.

6

Acceptance testing

Scenarios run with the business side to confirm the platform was ready for use.

Results

What changed

  • A more reliable platform, tested at every layer from API integrations to Power BI.
  • Analytical reports checked for correct figures and calculation logic.
  • The platform confirmed ready for business users.

Related

Agriculture

Data platforms, ERP and private AI for agroholdings.

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Data warehouse case

A next-generation data warehouse for a large financial institution: query times down 70%.

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Want a result like this?

Most engagements start with a fixed-scope sprint from €10k — a measured answer in weeks, then the programme only if it pays off.

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