The correct answer is Data compliance auditing. Data compliance auditing involves assessing an organization's data security practices to ensure they align with relevant standards, regulations, and internal policies. It includes reviewing processes, controls, and procedures related to data handling, storage, access, and protection to identify any gaps or non-compliance issues. By conducting regular compliance audits, organizations can mitigate risks, enhance data security, and demonstrate adherence to legal and regulatory requirements.

Apache Spark is recommended for handling large volumes of diverse data due to its distributed computing capabilities, in-memory processing, and support for complex data transformations. It can efficiently process streaming data from IoT devices and social media platforms.

Micro-batching offers advantages such as better resource utilization and lower latency compared to traditional batch processing. However, it also introduces higher processing overhead due to the frequent scheduling of small batches. This approach may be suitable for scenarios where low-latency processing is not critical, but real-time processing is not feasible due to infrastructure limitations.

A data governance framework establishes ownership and accountability for data-related activities within an organization. It defines roles and responsibilities for managing and protecting data, ensuring that individuals or teams are accountable for data quality, security, and compliance. Ownership ensures that there are clear stakeholders responsible for making decisions about data governance policies and practices.

A physical data model includes aspects such as storage parameters and optimization strategies, which are not present in conceptual or logical models. These aspects are essential for database implementation and performance tuning.

In normalization, a functional dependency occurs when one attribute in a relation uniquely determines another attribute's value. This forms the basis for eliminating redundancy and ensuring data integrity.

Push Data Pipeline is not a commonly used data extraction technique. ETL, CDC, and Web Scraping are more commonly employed methods for extracting data from various sources.

The primary goal of data quality assessment techniques is to ensure the accuracy, reliability, and overall quality of data. This involves identifying and addressing issues such as inconsistency, incompleteness, duplication, and correctness within datasets, ultimately improving the usefulness and trustworthiness of the data for decision-making and analysis.

Standardization refers to the process of transforming data into a consistent format or representation, making it easier to compare, analyze, and integrate across different systems or datasets. This process may involve converting data into a common data type, unit of measurement, or naming convention, ensuring uniformity and compatibility across the dataset. Standardization is essential for data quality and interoperability in data management and analysis workflows.

Scalability and fault tolerance are critical considerations when designing a data transformation pipeline for processing streaming data in real-time. The system must be able to handle varying workloads and maintain reliability to ensure uninterrupted data processing.

Data transformation in the ETL process includes tasks like cleaning and validating data to ensure consistency and accuracy, often involving standardizing formats and values.

Talend is an ETL tool that is widely recognized for its intuitive visual interface and drag-and-drop functionality, enabling users to easily design and implement complex data pipelines without writing code.