ETL Testing Tutorial
ETL Testing Useful Resources
Selected Reading
- ETL Testing - Interview Questions
- ETL Testing - Best Practices
- ETL Testing - Automation
- ETL Testing - Backup Recovery
- ETL Testing - Data Completeness
- ETL Testing - Data Quality
- ETL Testing - Data Transformations
- ETL Testing - Metadata
- ETL Testing - Data Accuracy
- ETL Testing - Scalability
- ETL Testing - Performance
- ETL Testing - Scenarios(Test Cases)
- ETL Testing - Process
- ETL Testing - Techniques
- ETL Testing - Challenges
- ETL Testing - Categories
- ETL vs Database Testing
- ETL Testing - Tasks
- ETL Testing - Introduction
- ETL Testing - Home
ETL Testing Useful Resources
Selected Reading
- Who is Who
- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes
ETL vs Database Testing
ETL vs Database Testing
ETL测试和数据库测试都涉及数据验证,但并不相同。 通常在数据仓系统中进行电子数据处理测试,而在数据来自不同应用到交易数据库的交易系统中,通常进行数据库测试。
在这方面,我们强调了电子职业教育测试与数据库测试之间的重大差别。
ETL Testing
ETL测试涉及以下业务:
验证数据从源向目标系统流动的情况。
核查来源和指标系统的数据。
核实数据提取、按照要求和预期进行转化。
如果在转型期间保持表层关系——合点和钥匙——的话,则予以确认。
普通电子设备测试工具包括QuerySurge, Informatica等。
Database Testing
数据库测试更多地强调数据准确性、数据正确性和有效数值。 它涉及以下行动:
核实是否维持了初级和外国钥匙。
核实表中各栏是否有有效的数据价值。
核实各栏的数据准确性。 Example-月数栏的数值大于12。
核实栏目中缺失的数据。 如无一栏,实际上应具有有效价值。
共同数据库测试工具 ium,QTP等。
下表列出了数据库和电子计算法测试的主要特征及其比较。
Function | Database Testing | ETL Testing |
---|---|---|
Primary Goal | Data vapdation and Integration | Data Extraction, Transform and Loading for BI Reporting |
Apppcable System | Transactional system where business flow occurs | System containing historical data and not in business flow environment |
Common tools | QTP, Selenium, etc. | QuerySurge, Informatica, etc. |
Business Need | It is used to integrate data from multiple apppcations, Severe impact. | It is used for Analytical Reporting, information and forecasting. |
Modepng | ER method | Multidimensional |
Database Type | It is normally used in OLTP systems | It is appped to OLAP systems |
Data Type | Normapzed data with more joins | De-normapzed data with less joins, more indexes, and aggregations. |