An example of sensitive data is a Social Security Number (SSN), which is personally identifiable information (PII) uniquely identifying individuals and often used for official purposes. Sensitive data typically includes any information that, if disclosed or compromised, could lead to financial loss, identity theft, or privacy violations.

A successful data governance framework comprises several key components that work together to ensure effective management and utilization of data assets. These components include clearly defined data policies outlining how data should be handled, data stewardship roles and responsibilities for overseeing data assets, mechanisms for managing and improving data quality, and controls for safeguarding data privacy. By integrating these components into a cohesive framework, organizations can establish a culture of data governance and drive data-driven decision-making processes.

The primary goal of data cleansing is to ensure data accuracy and consistency. It involves detecting and correcting errors, inconsistencies, and discrepancies in data to improve its quality and reliability for analysis, decision-making, and other data-driven processes. By removing or rectifying inaccuracies, data cleansing enhances the usability and trustworthiness of the data.

In an ERD, a Many-to-Many relationship signifies that each instance of one entity can be related to multiple instances of another entity, and vice versa. This relationship is common in database modeling scenarios.

The Kafka Zookeeper is responsible for managing the metadata of topics, partitions, and replicas. It maintains information about the structure and configuration of the Kafka cluster.

The primary purpose of a data warehouse is to store historical data for analysis, enabling organizations to make informed decisions based on trends and patterns over time.

The Kafka Broker is responsible for storing the published messages. It manages the storage and distribution of data across topics in Kafka.

ACID stands for Atomicity, Consistency, Isolation, and Durability. It is a set of properties that ensure that database transactions are processed reliably. Atomicity ensures that either all the operations within a transaction are successfully completed or none of them are. Consistency ensures that the database remains in a consistent state before and after the transaction. Isolation ensures that the transactions are isolated from each other. Durability ensures that once a transaction is committed, its changes are permanently stored in the database even in the event of system failures.

Paxos is a well-known distributed consensus algorithm designed to achieve agreement among a group of nodes in a distributed system. It ensures that all nodes agree on a single value, even in the presence of network failures and node crashes. Paxos has been widely used in various distributed systems to maintain consistency and reliability.

Deduplication is the process of identifying and removing duplicate records or entries from a dataset. Duplicate records can arise due to data entry errors, system issues, or data integration challenges, leading to inaccuracies and redundancies in the dataset. By detecting and eliminating duplicates, data cleansing efforts aim to improve data quality, reduce storage costs, and enhance the effectiveness of data analysis and decision-making processes.

An example of data inconsistency that data cleansing aims to address is duplicated records with conflicting information. These duplicates can lead to discrepancies and errors in data analysis and decision-making processes. Data cleansing techniques, such as data deduplication, help identify and resolve such inconsistencies to ensure data integrity and reliability.

The extraction phase of the ETL process involves extracting data from multiple sources such as databases, files, or applications to be used for further processing.