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Answer: Design a multi-stage ETL pipeline with data validation and reconciliation steps to ensure data consistency and accuracy across the sources, leveraging data quality tools and techniques.
Option B is the correct answer. Designing a multi-stage ETL pipeline with data validation and reconciliation steps allows for ensuring data consistency and accuracy across multiple sources. Leveraging data quality tools and techniques can help identify and resolve data discrepancies. Using a single-stage ETL process or a traditional file-based approach may not provide the desired level of data consistency and accuracy. Ignoring the data integration aspect may lead to data quality issues and inconsistencies.
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Your company is planning to implement a data integration solution to consolidate data from multiple sources into a single view. Describe the steps you would take to design and implement an ETL pipeline for data integration, and explain the considerations involved in ensuring data consistency and accuracy across the sources.
A
Use a single-stage ETL process to load all data into a central repository and perform all transformations and integration there, without considering data consistency and accuracy.
B
Design a multi-stage ETL pipeline with data validation and reconciliation steps to ensure data consistency and accuracy across the sources, leveraging data quality tools and techniques.
C
Use a traditional file-based approach to integrate the data, as it is more cost-effective than using an ETL pipeline.
D
Focus only on the ETL process and ignore the data integration aspect, as it is not relevant to data consistency and accuracy.