Test Result Aggregation

The core runtime that executes individual data quality tests and produces raw results. It is responsible for:

• Running declarative tests (e.g., from Great Expectations, dbt, Soda Core).
• Executing programmatic assertions in Python or SQL.
• Capturing raw metrics: pass/fail status, record counts, failed examples, and execution timestamps.
• This engine is often integrated directly into data pipeline orchestration tools like Apache Airflow or Prefect.

A persistent, queryable data store that ingests and retains test execution outputs. This forms the system of record for all quality checks.

• Stores: Timestamped test runs, associated metadata (pipeline ID, dataset version), and detailed failure samples.
• Common Backends: Time-series databases (e.g., InfluxDB), data warehouses (BigQuery, Snowflake), or dedicated observability platforms.
• Critical Function: Enables historical trend analysis and calculation of metrics like test coverage and pass rate over time.

The business logic that rolls up individual test results into higher-level summaries. This transforms point-in-time checks into system-wide health indicators.

• Key Operations:
  • Grouping results by data asset (table, column), test suite, or business domain.
  • Calculating aggregate metrics: overall pass/fail percentage, number of failing tests.
  • Applying severity weighting (e.g., a schema break is critical, a minor freshness delay is a warning).
• This logic determines the final data quality score or status for a dashboard.

The component that evaluates aggregated results against predefined thresholds and triggers communications.

• Rule Engine: Compares current quality scores or failure counts against data quality gates and dynamic thresholds.
• Action Triggers:
  • Sends alerts via Slack, PagerDuty, or email.
  • Opens tickets in Jira or ServiceNow.
  • Executes pipeline-gated actions to halt downstream processing.
• Critical Design: Ensures alerts are actionable, routed to the correct team (e.g., data engineering vs. analytics), and avoid alert fatigue.

The user interface that presents aggregated test results for human consumption. It provides the unified view central to test result aggregation.

• Common Visualizations:
  • Health Scorecards: Overall pass/fail status for key data products.
  • Trend Charts: Historical pass rates to identify degradation.
  • Drill-Down Tables: Lists of currently failing tests with root cause details.
• Integration Point: Often embedded within broader data observability platforms or business intelligence tools like Tableau.

The scheduler that coordinates the entire aggregation workflow, ensuring tests run and results are processed at the correct cadence.

• Manages:
  • The timing of test execution engine runs (on-schedule, on-data-arrival).
  • The subsequent execution of aggregation logic.
  • The propagation of results to the dashboard and alerting systems.
• Key Concept: Enables continuous testing by integrating aggregation into CI/CD pipelines and production pipeline triggers.

A declarative or programmatic statement that defines a condition data must satisfy to be considered valid. This is the fundamental unit being tested.

• Examples: A column must be non-null, values must fall within a specific numeric range, a string must match a regex pattern, or a foreign key must have a corresponding primary key.
• Role in Aggregation: Each rule, when executed, produces a single test result (pass/fail, metric). Aggregation collects the outcomes of hundreds or thousands of these individual rules.

A collection of data quality rules or assertions that define the expected properties of a specific dataset. It groups related tests for a table or data asset.

• Framework Example: In Great Expectations, an Expectation Suite is a JSON or Python object containing multiple expectations (e.g., expect_column_values_to_be_unique, expect_table_row_count_to_be_between).
• Role in Aggregation: The suite is the typical unit of execution. A checkpoint runs a suite, and aggregation summarizes the results of all expectations within that suite, providing an asset-level health score.

A configured operation that runs a suite of data quality tests against a specific dataset and triggers actions based on the validation results. It is the execution trigger.

• Components: A checkpoint binds together: 1) a data asset (table, file), 2) an expectation suite, and 3) an action (e.g., store results, send notification).
• Role in Aggregation: Checkpoints produce the raw result objects. Aggregation systems consume these results from multiple checkpoints—potentially across different pipelines and databases—to build a unified view.

The software component within a data testing framework responsible for running test suites, managing dependencies, collecting raw results, and reporting initial outcomes.

• Function: It executes the logic defined in a Data Quality Rule, evaluates it against the target data, and generates a result object containing status, metric value, observed samples, and execution metadata.
• Role in Aggregation: The engine is the source system. Aggregation layers sit on top of one or more engines (e.g., Great Expectations, dbt, Soda Core) to normalize and centralize their disparate result formats.

A predefined quality threshold or set of passing tests that must be met for data to progress in a pipeline or be deemed fit for consumption. It's a decision point.

• Types: A schema gate (all schema tests pass), a freshness gate (data is less than 1 hour old), or a business rule gate (critical revenue calculations are valid).
• Role in Aggregation: Aggregated results are evaluated against these gates. For example, a dashboard may show a "Gate Status" of Passed only if 100% of critical tests and >95% of warning tests pass. Aggregation provides the summary data needed to make the gate decision.

The automated coordination and scheduling of data quality test execution across a complex pipeline. It manages the when and order of test runs.

• Processes: Triggering tests after a data ingestion job completes, running dependent tests in sequence, handling retries on failure, and fanning out tests to parallel execution nodes.
• Role in Aggregation: Orchestration ensures a steady, reliable stream of test results are generated. The aggregation system consumes this stream. Advanced orchestration (e.g., using Apache Airflow) can trigger aggregation jobs themselves to update dashboards after all pipeline tests complete.