Sample size considered for data checks

When tables contain more than 1 million rows, the DQC Platform automatically applies sampling to ensure data checks are efficient and scalable — without compromising relevance or statistical value.

The platform chooses the sampling method dynamically, based on the structure and distribution of the dataset.

How sampling works

The DQC Platform uses a decision-tree approach to select the most appropriate sampling method based on dataset characteristics:

1. Random sampling

Used when:
The dataset is uniformly distributed without clear subgroups.

Why:
Provides a simple, unbiased, and representative sample of the full dataset.

2. Stratified sampling

Used when:
The dataset contains distinct subgroups (strata) whose representation is important.

Why:
Reduces sampling error and ensures balanced coverage across key segments (e.g., departments, regions, product lines).

3. Systematic sampling

Used when:
The dataset is ordered (e.g., time series or spatial data).

Why:
Ensures even distribution across the range — e.g., one row per 1000 entries.

4. Cluster sampling

Used when:
The dataset is grouped into natural clusters (e.g. by geography or business unit), and it would be costly to sample every unit.

Why:
Efficient and cost-effective when full-row sampling isn't feasible.

Want to run checks on the full dataset?

If you prefer to include all rows in a table check — regardless of size:

1. Go to the More

2. Adjust the Table Check Scope to “No row limit”

3. Save and run the check as usual

Full scans can be enabled manually where needed

Notes

• Sampling only applies to tables with >1 million records

• The sampling method is selected automatically and transparently

• You can override the sampling by switching to full-table scope

• Learn more: Adjustment of the Table Check Scope, Working with the Rules tab, Overview of existing rules