Become an Expert in ETL Testing

• DDL,DML,DQL,TCL
• WHERE Clause - Character Strings and Dates, number
• General Comparison Conditions = > >= < <= <>
• Other Comparison BETWEEN , IN , LIKE , NULL
• Logical Conditions AND OR NOT
• Column Alias , Column Position, Multiple Columns
• Explanation of Normalization and Demoralization

• Character Functions: UPPER, LOWER, INITCAP, LENGTH, SUBSTR, INSTR, LPAD, RPAD, CONCAT,
• LTRIM, RTRIM, TRIM, REPLACE, TRANSLATE, REVERSE
• Number Functions: ROUND, TRUNC, MOD, POWER, CEIL, FLOOR, ABS
• Dates Functions: SYSDATE, MONTHS_BETWEEN, NEXT_DAY, LAST_DAY, ADD_MONTHS, ROUND,TRUNC, Arithmetic on Date
• Conversion Functions: Implicit Data-Type Conversion & Explicit Data-Type Conversion, TO_CHAR,TO_NUMBER ,TO_DATE
• General Functions: NVL , NVL2, NULLIF, COALESCE
• CASE Expression, DECODE
• Nested function with real-time usage

• EQUI JOIN / SIMPLE JOIN / NORMAL JOIN
• ANSI JOIN, LEFT OUTER, RIGHT OUTER, FULL OUTER
• INNER JOIN, JOIN ... USING clause, JOIN ... ON clause
• CROSS JOIN, NON-EQUI JOIN,
• SELF JOIN ORACLE STANDARD OUTER JOINS.
• Multi table Joins, Complex Joins – How to simplified complex joins.

• Creating Groups of Data: GROUP BY Clause
• Filtering Group Results: The HAVING Clause

• Single-Row Subqueries- Rules, Operators : = > >= < <= <>
• Null Values in a Subquery
• Multi-Row Subqueries- Rules, Operators : IN, ANY , ALL

• UNION
• UNION ALL
• MINUS

• Rank
• Dense_Rank
• Rownumber
• Views, Materialized Views, Index

• Rank
• Dense_Rank
• Black Box Testing
• White Box Testing
• Unit Testing
• System Testing
• Integration Testing
• Acceptance Testing
• How to perform Manual Testing

• Waterfall model
• Agile Model

• HP ALM
• Azure Devops(ADO)

• Test plan & Testing strategy in data warehousing
• Testing plan
• Test cases possibilities in Data warehousing
• Sample test cases for real time scenarios

• How to generate the reports by using IBM Cognos.
• Test cases possibilities in BI Reports
• Sample test cases for real time scenarios

ETL (Extract, Transform, Load) Testing is the process of validating data during the ETL process to ensure it has been correctly extracted from source systems, accurately transformed, and properly loaded into the target system such as a data warehouse or data lake.

ETL Testing ensures:

• Data accuracy and integrity
• Correct business rules implementation
• High-quality data for reporting and analysis
• Detection of issues like data loss, duplication, or transformation errors

• Testing huge volumes of data
• Complex transformation logic
• Inconsistent or missing source data
• Poor documentation of business rules
• Lack of automation tools

• ETL Testing Tools: QuerySurge, Talend, Informatica Data Validation
• Manual Testing Tools: SQL, Excel, TOAD, DBeaver
• Automation Tools: Selenium (limited use), Apache Nifi, Python scripts for data comparison

Database Testing focuses on validating schemas, indexes, and stored procedures in a database.

ETL Testing is concerned with validating data flow and transformation between source and target systems during ETL processes.

Strong SQL knowledge

Understanding of ETL and data warehouse concepts

Familiarity with data transformation rules

Analytical thinking and attention to detail

Experience with testing and automation tools

Log discrepancies in a defect tracking tool

Investigate the root cause (e.g., source issues, transformation logic errors, load failures)

Work with developers or data engineers to fix and revalidate the data