ETL Testing Complete Guide

Overview

This complete guide offers practical reference material for ETL testing, covering concepts from beginner to advanced. It focuses on critical areas such as source-to-target validation, transformations, reconciliation, incremental loads, Slowly Changing Dimensions (SCDs), data quality, and automation principles. The guide details how to prove that data arrives correctly in the target system without loss, duplication, corruption, or unexpected delay. It is designed to assist QA Engineers, SDETs, and Data Testers in understanding modern data pipelines and applying effective testing strategies. The guide addresses the validation of data at various stages of the ETL process, from source data validation to target data consumption, and includes testing for specific data handling scenarios.

Table of contents

1. ETL Testing at a Glance
2. ETL Architecture for Testers
3. ETL Testing Types
4. Requirements & Mapping Documents
5. Test Data Strategy
6. Source Data Validation
7. Target Data Validation
8. Source-to-Target Reconciliation
9. Transformation Rule Testing
10. Data Type, Length & Precision Validation
11. NULL, Default & Blank Handling
12. Duplicate & Deduplication Testing
13. Referential Integrity Testing
14. Incremental / Delta Load Testing
15. Change Data Capture (CDC)
16. Slowly Changing Dimensions (SCD)
17. Late-Arriving Data
18. Date, Time & Timezone Testing
19. File-Based ETL Testing
20. API-to-ETL Pipeline Validation
21. ETL Data Quality Testing

Selected preview

SoftwareTestPilot • https://softwaretestpilot.com/products 1. ETL Testing at a Glance Understand the pipeline before you test it. What ETL means ETL = Extract, Transform, Load. Data is read from one or more sources, changed according to business rules, and loaded into a target such as a data warehouse, data lake, reporting database, or analytics platform.  Extract: collect data from databases, APIs, files, queues, SaaS systems, or logs.  Transform: clean, standardize, enrich, derive, map, aggregate, or validate data.  Load: write transformed data into destination tables or storage layers. QA goal Prove that the right data arrives in the right place, in the right format, with the right business meaning - without loss, duplication, corruption, or unexpected delay. Layer Typical QA question Source Did we extract all eligible records? Transformation Was each business rule applied correctl

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