Data Testing
Event-driven architectures built on Apache Kafka have become the standard for real-time data processing. But testing streaming systems is fundamentally different from testing request-response applications. Events are asynchronous, ordering matters, and failures manifest as silent data loss rather than obvious errors. This guide covers practical strategies for
Testing
SQLMesh is a SQL transformation framework that competes with dbt but adds first-class testing, change management, and virtual data environments. Its testing model is notably different from dbt's: unit tests run against in-memory DuckDB without a warehouse, audits run post-deployment assertions, and the sqlmesh plan
Streaming
Stream processing jobs are notoriously difficult to test. Flink and Spark Streaming jobs process unbounded data with complex windowing logic, watermarks, and stateful operations. The standard "run it and see" approach doesn't work — you need deterministic, reproducible tests that verify your transformation logic without running a
Data Testing
Extract, Transform, Load (ETL) pipelines are the backbone of modern data infrastructure. When they break—silently corrupting data rather than failing loudly—the consequences ripple through every dashboard, report, and decision your organization makes. ETL testing is how you prevent that. This guide covers the strategies, frameworks, and tools QA
Testing
Elementary is an open-source dbt package that adds data observability to your existing dbt project. It turns dbt models into monitored data assets: tracking volume changes, schema changes, freshness, and statistical anomalies in column distributions — without changing your existing dbt tests. Where dbt's built-in tests (not_
Testing
Delta Lake adds ACID transactions, time travel, and schema enforcement to Apache Spark data lakes. These features introduce behaviors that require specific testing: concurrent writes must not corrupt data, time travel must return correct historical snapshots, schema changes must follow defined rules. This guide covers how to test Delta Lake
Testing
dbt's schema tests (not_null, unique, relationships) verify data quality — they check the output of your models. But they can't verify whether your SQL logic is correct. A model that produces the wrong discount calculation or the wrong attribution logic might pass all schema tests while
Testing
dbt snapshots implement Slowly Changing Dimensions (SCD Type 2) — tracking how a record changes over time by keeping the full history. A bug in snapshot configuration silently corrupts your history, making past-dated reports wrong. Testing snapshots requires validating that history is captured correctly, records are updated (not duplicated), and
Data Testing
Data warehouses are where your organization's most important decisions get made. Board reports, investor metrics, operational dashboards—they all pull from your warehouse. When the data is wrong, so are the decisions. Data warehouse testing is the discipline that ensures your analytical data is accurate, complete, and reliable.
Testing
Unit tests verify individual transformations. Schema tests catch bad data. But neither tells you whether the full pipeline — from raw ingestion through transformation to the serving layer — is correct. End-to-end validation catches the bugs that only appear when all the pieces interact: a join condition that drops rows,
Data Testing
Software engineers have spent decades building CI/CD practices that make deployments safe and reliable. Data engineers are catching up—and the lessons transfer directly. A data pipeline CI/CD system automatically tests every change before it reaches production, catches regressions before they corrupt your warehouse, and gives teams the
Testing
Dagster is a data orchestration platform built around Software-Defined Assets (SDAs) — a model where pipelines declare what data assets they produce rather than what tasks to run. This asset-centric model makes testing more natural: you test that assets produce the right data, not just that code runs. This