HDFS (Hadoop Distributed File System) is a distributed file system designed to store and manage large volumes of data across multiple nodes in a Hadoop cluster. It provides high throughput and fault tolerance, making it suitable for storing and processing big data applications. Unlike traditional relational databases like MySQL and SQLite, HDFS is optimized for handling large-scale distributed data across commodity hardware.

Workflow orchestration tools assist in data processing tasks by automating and orchestrating complex data workflows. They enable data engineers to define workflows consisting of multiple tasks or processes, specify task dependencies, and schedule the execution of these workflows. This automation streamlines the data processing pipeline, improves operational efficiency, and reduces the likelihood of errors or manual interventions. Additionally, these tools provide monitoring and alerting capabilities to track the progress and performance of data workflows.

A covering index in a database is an index that includes all the columns required by a query. It allows the database to retrieve data directly from the index without needing to access the table, improving query performance.

When selecting a data loading strategy, data storage capacity is not typically considered. Instead, factors such as data volume, complexity, and network bandwidth are prioritized for optimal performance.

Data partitioning is the process of breaking down large datasets into smaller chunks, often based on key attributes, to distribute processing tasks across multiple nodes in a streaming pipeline. This approach enables parallel processing, improves scalability, and facilitates efficient utilization of computing resources in real-time data processing scenarios.

Documenting data modeling decisions and assumptions is crucial for facilitating future modifications, troubleshooting, and ensuring that all team members are aligned with the design choices made during the modeling process.

In Apache Airflow, DAGs (Directed Acyclic Graphs) are used to define the order of task execution and specify any dependencies between tasks. A DAG represents a workflow as a collection of tasks and the relationships between them. By defining DAGs, users can orchestrate complex workflows with clear dependencies and execution orders, facilitating efficient task scheduling and management.

The primary aim of denormalization is to improve query performance by reducing the number of joins needed to retrieve data, even at the cost of increased redundancy. This can speed up read-heavy operations.

An Entity-Relationship Diagram (ERD) is specifically designed to illustrate the relationships between entities in a database, helping to visualize the structure and connections within the database.

The primary advantage of using a document-oriented NoSQL database, such as MongoDB, is schema flexibility, allowing for easy and dynamic changes to the data structure without requiring a predefined schema.