Big data testing that checks the data, not just whether the job finished
A pipeline can finish green and still deliver incomplete, duplicated or wrongly transformed data. We test what arrives, what the transformations do and whether the results can be trusted by the people and models that rely on them.
Overview
What it is. Big data testing verifies data pipelines and processing at scale: ingestion, transformation, storage and consumption, covering completeness, correctness, timeliness and performance of batch and streaming flows.
Who it is for. Organisations building data platforms and warehouses, teams migrating pipelines, products that rely on analytics, and teams feeding data to machine-learning features.
Why it matters
The problem it addresses. Data defects are quiet. Nobody sees an error message; they see a dashboard that is slightly wrong, a report that disagrees with another, or a model that behaves oddly.
When it matters most. When a pipeline is built or migrated, when sources or schemas change, before reports or models go live, and when numbers stop agreeing.
What happens without it. Decisions are made on wrong numbers, trust in data erodes, and diagnosing the cause costs far more than catching it at the point of load.
What we test
Source-to-target reconciliation
counts, keys, aggregates, sampled records
Transformation logic validated with known inputs and expected outputs
Data quality rules
uniqueness, completeness, allowed values, referential integrity, freshness
Schema change and drift handling
Late, duplicate and out-of-order records in batch and streaming flows
Backfill and rerun behaviour
idempotency and overwrite risk
Pipeline performance and capacity on realistic volumes
How UpDoer approaches Big Data Testing
Understand
Requirements, tickets, code diffs and product context.
We map the sources, pipelines, consumers and the reports or models that depend on them, and learn where numbers have disagreed before.
Risk Mapping
Where a failure would cost the most.
Datasets are ranked by how many decisions depend on them and how sensitive they are, and data owners name the business rules that define valid data.
Test Strategy
Coverage chosen, test cases drafted.
We decide the checks at each stage, which volumes and edge-case datasets to build, and how test data will be masked and controlled.
Execute
Manual and automated testing runs.
Checks run at load and after transformation, with known-input tests for logic, reconciliation against sources and rerun and backfill scenarios.
AI-Assisted Quality Engineering
Failures classified, release risk scored.
AI profiles datasets, flags distribution shifts and unexpected null or duplicate patterns, drafts checks from schemas and summarises failures.
Human Verification
A QA engineer checks every flagged result.
A tester with the data owner judges whether an anomaly is a defect or a real change, confirms reconciliation results and rates the impact.
Report & Improve
The verified quality signal, then better coverage.
We report by dataset and rule, with failing examples and known gaps stated. Checks stay in the pipeline as ongoing monitoring where agreed.
Big data testing: can the numbers be trusted?
01
Where it breaks
Green job, wrong dataLate and duplicate recordsSchema drift upstream02
Test workflow
DataPipelineProcessValidate03
AI assists
Profile and flag shiftsDraft checks from schemaGroup failed checks04
Humans decide
Owner defines valid dataAnomaly or real change?05
Release signal
Results by datasetFailing examples shownLoad decision by a person
Illustrative workflow. Not a client result or a measured outcome.
Data pipeline test matrix
A dash means no AI step is described for that area.
| Pipeline stage | What we validate | AI assistance | Human validation |
|---|---|---|---|
| Ingestion | Completeness, formats, schema, late and duplicate records | — | Decides what counts as late or duplicate |
| Transformation | Logic against known inputs and expected outputs | Drafts cases from rules | Confirms rules and expected results |
| Storage and models | Keys, relationships, history and slowly changing data | Suggests integrity checks | Judges modelling intent |
| Reconciliation | Counts, aggregates and sampled records versus source | — | Explains and accepts or rejects gaps |
| Reruns and backfills | Idempotency, overwrite risk, recovery | — | Judges business impact of a bad rerun |
| Consumption | Reports and features agree with the data underneath | — | Decides whether differences matter |
| Performance | Runtime and capacity on realistic volumes | Summarises run trends | Interprets bottlenecks with data engineers |
Pipeline stage: Ingestion
- WHAT WE VALIDATE
- Completeness, formats, schema, late and duplicate records
- AI ASSISTANCE
- —
- HUMAN VALIDATION
- Decides what counts as late or duplicate
Pipeline stage: Transformation
- WHAT WE VALIDATE
- Logic against known inputs and expected outputs
- AI ASSISTANCE
- Drafts cases from rules
- HUMAN VALIDATION
- Confirms rules and expected results
Pipeline stage: Storage and models
- WHAT WE VALIDATE
- Keys, relationships, history and slowly changing data
- AI ASSISTANCE
- Suggests integrity checks
- HUMAN VALIDATION
- Judges modelling intent
Pipeline stage: Reconciliation
- WHAT WE VALIDATE
- Counts, aggregates and sampled records versus source
- AI ASSISTANCE
- —
- HUMAN VALIDATION
- Explains and accepts or rejects gaps
Pipeline stage: Reruns and backfills
- WHAT WE VALIDATE
- Idempotency, overwrite risk, recovery
- AI ASSISTANCE
- —
- HUMAN VALIDATION
- Judges business impact of a bad rerun
Pipeline stage: Consumption
- WHAT WE VALIDATE
- Reports and features agree with the data underneath
- AI ASSISTANCE
- —
- HUMAN VALIDATION
- Decides whether differences matter
Pipeline stage: Performance
- WHAT WE VALIDATE
- Runtime and capacity on realistic volumes
- AI ASSISTANCE
- Summarises run trends
- HUMAN VALIDATION
- Interprets bottlenecks with data engineers
Where AI helps
- Drafting candidate validation checks from table schemas and documented rules, for review.
- Grouping failed checks by upstream source or transformation step.
- Summarising check results into a readable data-quality note for owners.
- Suggesting edge-case records to build into test datasets.
How we use AI: AI can accelerate test design, analysis and repetitive QA work, but important outputs remain subject to human review and validation. It informs the release decision; it does not make it.
Where humans matter
- Defining what valid data means for the business.
- Judging whether an anomaly is a real change in behaviour or a pipeline fault.
- Confirming that reconciliation differences are explained and acceptable.
- Deciding which data-quality failures should stop a downstream load.
Tools we build with
- Automation
- SeleniumPlaywrightAppiumKatalonTestComplete
- API & Performance
- PostmanSoapUISwaggerApache JMeter
- CI/CD & Reporting
- JenkinsTeamCityAllureMaven
Related services
Relevant industries
Frequently asked questions
Which big data platforms do you work with?
We work in your environment with the tools you use. The specific platforms and frameworks we can support are confirmed in scoping, since they differ widely between clients.
Can AI decide if data is right?
AI helps spot anomalies and draft checks. A person with the data owner decides whether a difference is a defect.
How is this different from ETL testing?
ETL testing is part of it. Big data testing also covers streaming, scale, schema change, reruns and the consumption layer.