Databricks Certified Data Engineer - Professional
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You are designing a data engineering solution on Azure Databricks that requires high data integrity and reliability for a financial reporting system. The solution must comply with strict regulatory requirements, including GDPR, and should be scalable to handle petabytes of data. Which of the following strategies would you employ to ensure the integrity and reliability of data in Delta Lake, considering the need for transactional consistency, schema enforcement, and the ability to audit data changes over time? Choose the best option.
A
Relying solely on the automatic optimizations provided by Delta Lake, assuming they cover all necessary aspects of data integrity and compliance without additional configuration.
B
Implementing manual checks and balances, such as custom data validation scripts and external checksums, outside of Delta Lake to ensure data integrity, despite the potential for increased operational overhead and human error.
C
Utilizing Delta Lake's built-in features, including ACID transactions, schema enforcement, and time travel capabilities, to ensure data integrity, reliability, and compliance with regulatory requirements.
D
Avoiding the use of Delta Lake's ACID properties and schema enforcement features to maximize performance, even if it compromises data integrity and compliance.