The primary advantage of columnar databases for analytics and querying is their ability to quickly aggregate and query large datasets. Columnar databases store data in a column-wise fashion, which is more suitable for analytical workloads as it allows for faster data retrieval and processing for tasks like aggregation and reporting.

To handle a high volume of transactions with quick read and write capabilities and consistency, a NewSQL database is the most appropriate choice. NewSQL databases combine the benefits of SQL (consistency) and NoSQL (scalability and speed) databases.

Traditional RDBMS (Relational Database Management Systems) are typically better suited for OLTP (Online Transaction Processing) operations, which require fast and efficient handling of numerous small, concurrent transactions.

For an organization needing immediate insights into real-time business operations, Operational Business Intelligence (Operational BI) is most suitable. Operational BI focuses on monitoring and analyzing current data to support daily decision-making and operational activities. It provides up-to-the-minute information for faster response.

Dimensional modeling is a technique used in data warehousing that enables the analysis of data at different granularities, such as year, quarter, month, and day. It involves creating fact tables and dimension tables to represent data hierarchies, facilitating efficient querying and reporting.

In a sales data model, the Time Hierarchy is crucial for analyzing sales trends. It allows analysts to explore sales data over different time periods, such as daily, monthly, or yearly, to identify patterns, seasonality, and trends. This hierarchy helps in time-based analysis, forecasting, and decision-making.

In a top-down approach to building a data infrastructure, data sources are typically the first components to be addressed. Data sources include various systems and databases that store raw data, and they need to be integrated and processed to feed into data warehouses and data marts. Starting with data sources is fundamental to ensuring data quality and consistency.

The process of cleaning, transforming, and enhancing the data to prepare it for loading into a data warehouse is called "Data Transformation." During this phase, data is cleansed, structured, and enriched to ensure its quality and consistency for analysis.

Data replication is a strategy in data warehousing that involves creating copies of the data warehouse at regular intervals. This approach helps minimize data loss in case of failures by ensuring that there are up-to-date backup copies of the data readily available. Data replication is essential for data resilience and disaster recovery.

To address memory overloads in a data warehouse, implementing data partitioning is a strategic maintenance strategy. Data partitioning involves dividing large tables into smaller, more manageable segments. This can reduce the memory requirements and improve query performance during peak hours.