ETL Testing

ETL (Extract, Transform, Load) Testing ensures the accuracy of data migration from source systems to the target system by validating data extraction, transformation logic, and loading processes.

• Requirement Analysis: Understanding business requirements and data sources.
• Test Planning: Designing test strategies, scenarios, and cases.
• Test Environment Setup: Configuring environments to match production systems.
• Execution: Running tests on extracted, transformed, and loaded data.
• Defect Reporting and Retesting: Identifying and fixing issues.

• Data Completeness Testing: Ensuring all data is transferred.
• Data Accuracy Testing: Verifying the correctness of transformed data.
• Data Transformation Testing: Validating transformation logic.
• Performance Testing: Checking ETL performance under load.
• Data Integrity Testing: Ensuring relationships in data are maintained.
• Regression Testing: Validating changes to ETL logic.

• Automated Testing: Implementing tests for missing values, outliers, and schema changes.
• Monitoring and Logging: Setting up error handling and logging to track issues in real-time.
• Data Profiling: Regularly reviewing the data's statistical properties (e.g., mean, variance) to spot discrepancies.
• Validation Rules: Implementing business rules to ensure that the data is in the right format and meets expectations (e.g., customer age should be non-negative)

• Data Cleaning: Removing duplicates, correcting errors, handling missing values (e.g., imputation or deletion), and standardizing formats.
• Feature Engineering: Creating new variables from raw data that will be useful for analysis or machine learning models. This includes normalization, scaling, and encoding categorical data.
• Aggregation: Summarizing data (e.g., calculating averages, sums, counts) to reduce complexity and focus on key metrics.
• Data Enrichment: Combining data from multiple sources to provide more context (e.g., merging customer data with demographic information).
• Normalization/Standardization: Scaling numerical values to a standard range, which is especially important for machine learning algorithms.

• Extract: The process of pulling data from various sources, such as databases, APIs, or files.
• Transform: Cleaning, filtering, aggregating, and shaping the data into a format suitable for analysis or machine learning.
• Load: Storing the transformed data into a data warehouse or database for further use.

• Open Source: Talend, Apache Nifi, CloverETL.
• Commercial: Informatica, DataStage, QuerySurge, SSIS.
• General Tools: SQL, Python, Excel.