Hadoop YARN (Yet Another Resource Negotiator) improves upon the classic MapReduce framework by introducing a resource management layer, allowing for support of various processing frameworks beyond MapReduce.

A weak entity in an ERD is one that cannot be uniquely identified by its attributes alone. It depends on a related entity (owner entity) for its existence and is represented by a double-bordered rectangle.

Determining the maximum number of retry attempts requires careful consideration of various factors. The nature of the operation being retried is crucial, as some operations may be more tolerant of retries than others. Service-level agreements (SLAs) also play a significant role, as they dictate acceptable response times and failure rates. Additionally, factors such as network conditions and time of day may influence the likelihood of successful retries and should be taken into account when setting retry policies.

After finalizing the logical data model, the next step would be to proceed with the database implementation phase, where the logical design is translated into the actual database schema and structures, ready for deployment.

The validation feature in data modeling tools ensures that the design adheres to predefined rules and standards, helping maintain consistency and quality in the database schema design process.

It is important to involve stakeholders in the data modeling process to gather their requirements, ensure buy-in, and incorporate their insights, which ultimately leads to a database design that meets their needs.

The process of transforming raw data into a format suitable for analysis in a data warehouse is called ELT (Extract, Load, Transform). In this approach, data is first loaded into the warehouse and then transformed according to analysis requirements.

The primary purpose of Dimensional Modeling is to organize data for data warehousing purposes, making it easier to analyze and query for business intelligence and reporting needs.

In an RDBMS, a primary key is a column or set of columns that uniquely identifies each row in a table. It ensures the uniqueness of rows and provides a way to reference individual rows in the table. Primary keys are crucial for maintaining data integrity and enforcing entity integrity constraints. Typically, primary keys are indexed to facilitate fast data retrieval and enforce uniqueness.

Data cleansing is the process of identifying and resolving inconsistencies, errors, and discrepancies in data to ensure its quality before it is used for analysis or other purposes.

Structured Streaming is a high-level API in Apache Spark that enables scalable, fault-tolerant processing of real-time data streams. It provides a DataFrame-based API, allowing developers to apply the same processing logic to both batch and streaming data, simplifying the development of real-time analytics applications and ensuring efficient handling of streaming data.

Data profiling involves analyzing the structure, content, and relationships within the dataset to identify anomalies, inconsistencies, and inaccuracies. By prioritizing data profiling, you can gain insights into the overall quality of the dataset, including missing values, duplicates, outliers, and inconsistencies, which is crucial for ensuring data accuracy and reliability.