etl testing training in hyderabad

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Presenter name Anusha Thalla Email
info_at_3zenx.com Website address www.3ZenX.com
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• Introduction to ETL Testing
• Importance
• ETL Testing Challenges
• ETL Testing Techniques
• ETL Testing Tools
• Best Practices
• Common ETL Testing Issues
• ETL Testing Process
• ETL Testing Metrics
• Conclusion

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ETL Testing stands for Extract, Transform, Load
Testing. It is a vital process in data
warehousing and business intelligence. ETL
Testing ensures data accuracy, completeness, and
integrity during the ETL process.
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ETL Testing helps identify data quality issues
early on. It ensures that data is transformed
correctly between source and target systems. ETL
Testing mitigates risks associated with data loss
or corruption during the extraction and loading
process.
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• Handling large volumes of data during testing can
  be a challenge.
• Validating complex data transformations requires
  specialized skills.
• Maintaining test data consistency and integrity
  across different environments can be difficult.

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• Data completeness testing ensures that all
  expected data is extracted and loaded.
• Data transformation testing verifies that data is
  transformed correctly according to business
  rules.
• Data reconciliation testing ensures accuracy by
  comparing source and target data.

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• Informatica PowerCenter provides comprehensive
  ETL testing capabilities.
• Talend Open Studio offers a robust set of ETL
  testing features.
• Apache Nifi is an open-source tool known for its
  data integration and ETL testing capabilities.

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• Define clear test objectives and requirements
  before starting the testing process.
• Develop comprehensive test cases covering all
  possible scenarios.
• Automate ETL testing to improve efficiency and
  reduce human error.

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• Data quality issues such as missing, duplicate,
  or incorrect data.
• Performance bottlenecks due to inefficient data
  transformations.
• Integration issues with source and target systems.

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• Identify data sources and validate their
  compatibility with the target system.
• Design and execute test cases based on ETL
  mappings and transformations.
• Validate data accuracy, completeness, and
  integrity in the target system.

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Data completeness percentage Measures the
percentage of data successfully extracted and
loaded. Error rate Indicates the number of
errors encountered during the ETL process. Data
transformation efficiency Measures the time
taken for data transformation and load processes.
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ETL Testing plays a crucial role in ensuring data
quality and integrity. It helps identify and
resolve issues early on, minimizing risks. By
following best practices and using appropriate
tools, organizations can achieve reliable and
efficient ETL testing processes.
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