Data Engineering - HelpMeTest

Data Engineering

Testing Spark Structured Streaming: Unit Tests, Micro-batch Simulation, and CI

Spark Structured Streaming tests fall into three layers: transformation unit tests using static DataFrames, micro-batch simulation using MemoryStream for source-side logic, and full integration tests with Testcontainers-Kafka. Watermark and late-data behavior requires careful trigger and clock control that MemoryStream provides without real streaming infrastructure. Key Takeaways Test transformations with static

Data Engineering

dbt Advanced Testing Patterns: Custom Tests, Macros, and CI Integration

dbt's testing model goes well beyond the four built-in tests. Generic tests written as macros are reusable across your entire project, singular tests handle complex multi-table business rules, and the dbt-expectations package brings 50+ Great Expectations-style assertions into SQL. In CI, dbt test --select state:modified+ ensures only

Data Engineering

Data Pipeline Quality Assurance: Testing Frameworks, Patterns, and Best Practices

Data pipeline QA requires a layered approach: unit tests for transformation logic, contract tests for schema agreements between systems, integration tests for end-to-end correctness, and SLA-based operational tests for latency and freshness. Idempotency and chaos testing are the two most commonly skipped layers — and the two most likely to catch

Data Engineering

Testing Apache Flink Applications: Unit, Integration, and Stateful Stream Testing

Testing Apache Flink requires specialized tools at each layer: MiniClusterWithClientResource for topology-level tests, KeyedOneInputStreamOperatorTestHarness for stateful operators, and EmbeddedKafkaCluster for end-to-end integration. Event-time semantics and exactly-once guarantees demand explicit test harness control over watermarks and checkpoints. Key Takeaways Unit test operators in isolation. Use KeyedOneInputStreamOperatorTestHarness to feed elements and watermarks

Data Engineering

Great Expectations Data Quality: Writing and Running Expectation Suites

Great Expectations structures data quality as code: Expectations describe what data should look like, Validators apply them to DataFrames or tables, and Checkpoints bundle validation into CI-runnable steps. Data Docs turn validation results into human-readable HTML reports your team can share. Key Takeaways Write expectations from profiles, refine manually. The

Airflow

Testing Apache Airflow DAGs: Unit Tests, DAG Validation, and pytest-airflow

Apache Airflow DAGs are Python code. That means you can test them like any other Python code — validate structure, unit test operators, and run DAGs against test data. This guide covers the full testing spectrum for Airflow. DAG Integrity Tests The first thing to test is that your DAGs load

Testing

ETL Testing Guide: Data Completeness, Transformation Accuracy, and Load Verification

ETL testing verifies three things: all expected data was extracted (completeness), transformations produced correct output (accuracy), and the data was loaded without corruption or duplication (integrity). Each phase requires different testing techniques: reconciliation counts and checksums for extraction, sample-based assertions for transformation, and row count plus constraint checks for loading.

Testing

Snowflake Data Testing: dbt + Great Expectations for Data Quality

Testing data quality in Snowflake requires both SQL-based tests (dbt works natively with Snowflake) and Python-based data validation (Great Expectations connects via the Snowflake SQLAlchemy adapter). Run dbt tests for schema and transformation validation, Great Expectations checkpoints for statistical and pattern-based assertions, and scheduled CI pipelines to catch issues before

Testing

Pandas Testing Best Practices: Testing Data Transformations with pytest

Testing pandas code requires different patterns than testing application code. DataFrames are complex objects—you rarely want to assert on the entire DataFrame at once. Instead, assert on specific columns, row counts, data types, and statistical properties. Use pytest fixtures for reusable test data, pandas.testing for DataFrame comparisons, and

Testing

Data Pipeline Testing: Unit and Integration Tests for Airflow, Prefect, and Dagster

Data pipeline orchestrators (Airflow, Prefect, Dagster) each have different testing models, but the principle is the same: unit-test your business logic in isolation, integration-test the orchestration layer with real or containerized dependencies. This guide covers testing patterns for all three orchestrators plus shared practices that apply to any pipeline. Key

Testing

Great Expectations Tutorial: Data Validation and Pipeline Testing

Great Expectations (GX) is a Python library for defining and running data quality assertions against DataFrames, files, and databases. You define Expectations (e.g., "this column should be non-null"), group them into Expectation Suites, and run them via Checkpoints. Failed Expectations produce HTML Data Docs reports showing exactly

Testing

dbt Testing Guide: Schema Tests, dbt-expectations, and Custom Tests

dbt ships with four built-in tests (not_null, unique, accepted_values, relationships) that cover the basics. For richer assertions—value ranges, regex patterns, statistical checks—add the dbt-expectations package. When neither covers your case, write a custom generic test. This guide covers all three layers plus how to run tests