Data cleansing refers to the process of detecting and correcting (or removing) inaccurate or incomplete data from a dataset. It involves tasks such as removing duplicates, correcting typographical errors, filling in missing values, and standardizing formats. On the other hand, data validation ensures that data meets specific criteria or conforms to predefined rules or standards. It involves tasks such as checking data types, ranges, formats, and relationships to ensure accuracy and consistency. Both processes are crucial for maintaining high-quality data in databases and analytics systems.

Denormalization can lead to faster queries due to reduced join operations. By combining data from multiple normalized tables into a single denormalized table, the need for complex joins is minimized, resulting in faster query execution times, especially for read-heavy workloads.

Synchronous communication requires the sender and receiver to be active simultaneously, with the sender waiting for a response before proceeding, whereas asynchronous communication allows the sender to continue operation without waiting for an immediate response. Asynchronous communication offers benefits such as decoupling of components, better scalability, and fault tolerance, albeit with potential complexities in handling out-of-order messages and ensuring eventual consistency.

HBase is the component of the Hadoop ecosystem that provides real-time, random read/write access to data stored in HDFS (Hadoop Distributed File System). It is a NoSQL database that runs on top of HDFS.

Minimizing network latency is a key factor in achieving high performance in a distributed system. Network latency refers to the delay or time it takes for data to travel between nodes in a network. By reducing network latency, distributed systems can improve responsiveness and overall performance, especially in scenarios where data needs to be exchanged frequently between distributed components. Techniques such as data caching, load balancing, and optimizing network protocols contribute to reducing network latency.

The core abstraction for data processing in Apache Flink is the DataStream, which represents a stream of data elements and supports operations for transformations and aggregations over continuous data streams.

Apache Airflow offers several advantages over traditional scheduling tools for data workflows. It provides dynamic workflow scheduling, allowing for the definition and execution of complex workflows with dependencies. Built-in monitoring and logging capabilities facilitate better visibility and debugging of workflows. Airflow is highly scalable, capable of handling large-scale data processing tasks efficiently. Its dependency management features ensure that tasks are executed in the correct order, improving workflow reliability and efficiency.

Completeness is a data quality metric that measures the extent to which all required data elements are present within a dataset. It evaluates whether all necessary information is available and accounted for, without any missing or omitted values. Complete data sets are essential for making informed decisions and conducting accurate analyses.

The choice between data modeling tools such as ERWin and Visio depends on factors like cost, complexity, specific requirements of the project, and the availability of features required for the task.

In a data governance framework, the process of defining policies, procedures, and standards for data management falls under the domain of Data Governance. Data governance encompasses the establishment of overarching principles and guidelines for managing data effectively across the organization. It involves defining rules and best practices to ensure data is managed, accessed, and used appropriately to support organizational objectives while maintaining compliance and mitigating risks.

Web Scraping is a data extraction technique used to extract data from semi-structured or unstructured sources like emails, documents, or social media platforms, enabling analysis and processing of the data.

GDPR stands for General Data Protection Regulation, a comprehensive European Union (EU) legislation designed to protect the privacy and personal data of EU citizens and residents. It imposes strict requirements on organizations handling personal data, including consent mechanisms, data breach notification, data subject rights, and hefty fines for non-compliance, aiming to harmonize data protection laws across the EU and empower individuals with greater control over their personal information.