Real-time data processing is commonly referred to as "Stream Processing." In this approach, data is processed as it is generated, allowing for real-time analysis and decision-making. It is crucial in applications where immediate insights or actions are required.

Semi-structured data is a type of data that falls between structured and unstructured data. It can often be represented as a combination of structured tables with additional metadata or annotations. This format provides some level of organization and makes it more manageable for analysis. Examples of semi-structured data include JSON, XML, and log files, which have some inherent structure but may also contain unstructured elements.

Unsupervised Learning with K-Means Clustering is suitable for customer segmentation as it groups customers based on similarities in credit card usage behavior without predefined labels. Supervised learning requires labeled data, reinforcement learning is used for sequential decision-making, and semi-supervised learning combines labeled and unlabeled data.

Talend is an ETL (Extract, Transform, Load) tool known for providing native integrations with Apache Hadoop, Apache Spark, and other big data technologies. This makes it a popular choice for organizations dealing with big data workloads, as it allows for efficient data extraction and processing from these technologies within the ETL pipeline. Other tools mentioned do not offer the same level of native integration with big data technologies.

NoSQL databases are schemaless, which means they do not require a fixed schema for data storage. This flexibility allows for the storage of various types of data without predefined structure constraints.

To prevent SQL injection, you should sanitize and parameterize user inputs in your queries. This involves validating and escaping user input data to ensure that it cannot be used to execute malicious SQL commands. Other options, while important, do not directly prevent SQL injection.

Relational databases are designed for storing structured data with well-defined rows and columns. This structured format allows for efficient storage and querying of data. Unstructured data, on the other hand, lacks a predefined structure.

Apache Flume is primarily used in the Hadoop ecosystem for data ingestion from various sources. It is a distributed, reliable, and available system for efficiently collecting, aggregating, and moving large amounts of data to Hadoop's storage or other processing components. Flume is essential for handling data ingestion pipelines in Hadoop environments.

In a data warehouse, the "Fact" table is used to store aggregated data at various levels of granularity. These tables contain measures or metrics, which are essential for analytical queries and business intelligence reporting.

Linear Regression is a commonly used algorithm for predicting continuous target variables. It establishes a linear relationship between the input features and the target variable, making it suitable for tasks like price prediction or trend analysis in Data Science.

Collaborative Filtering is a recommendation approach that identifies users similar to the target user based on their interactions and recommends items liked by those similar users. It relies on user-user similarity for recommendations.

The Convolutional layer in Convolutional Neural Networks (CNNs) is responsible for detecting local features in the input data, such as edges and textures. It does this by applying convolution operations across the input data, which allows the network to recognize spatial patterns in images or other structured data.