Apache Spark is commonly used in big data storage solutions to process large datasets in memory across distributed computing clusters. It provides an efficient and fault-tolerant framework for distributed data processing, enabling tasks like data transformation, querying, and machine learning on massive datasets in real-time or batch mode. Spark's in-memory processing capability enhances performance compared to traditional disk-based processing, making it a popular choice for big data analytics and processing.

The primary goal of a data governance framework is to ensure data quality and integrity throughout an organization. This involves establishing policies, processes, and controls to manage and protect data assets, thereby enhancing decision-making, regulatory compliance, and overall trust in the data.

A consumer group in Kafka is responsible for grouping consumers for parallel processing of messages, ensuring efficient data consumption.

In Apache NiFi, the process of extracting data from various sources and bringing it into the data flow is known as ingestion. It involves collecting data and initiating its movement through the data flow.

Designing a real-time recommendation engine for an e-commerce company involves employing collaborative filtering algorithms to analyze user behavior and preferences. Apache Spark facilitates data processing to generate personalized recommendations, with Redis caching frequently accessed items for faster retrieval. RESTful APIs ensure seamless integration with the e-commerce platform for serving recommendations to users in real-time.

Document-oriented NoSQL databases, such as MongoDB, are best suited for hierarchical data structures as they store data in a flexible, JSON-like format, allowing for nested and complex data structures.

A Factless Fact Table in Dimensional Modeling is used to capture events or transactions that lack measurable quantities. Instead, it focuses on recording the occurrences of events, making it valuable for scenarios where the relationship between dimensions is important but no numerical facts are associated.

MapReduce is a distributed computing model popularized by Google for processing and generating large datasets in parallel across a distributed cluster of nodes. It divides a large problem into smaller tasks, distributes them to different nodes for processing, and aggregates the results. This approach enables efficient parallel processing and scalability for handling massive datasets.

Typically, normalization aims to achieve Third Normal Form (3NF), which ensures that there is no transitive dependency in the data and eliminates redundant data, leading to efficient storage and data integrity.

In the context of data warehousing and analyzing sales data, the most suitable data modeling technique for representing relationships between dimensions and measures is the Star Schema. This schema design simplifies data retrieval and analysis by organizing data into dimensions and a central fact table, facilitating efficient querying and reporting.