In supervised learning, a 'feature' refers to a characteristic or attribute of the input data that is used by the model to make predictions. Features are the variables or dimensions that the algorithm analyzes to understand patterns and relationships.

Apache Spark is well-suited for handling large-scale data processing and analysis, making it an ideal choice for projects dealing with the substantial volume and velocity of data generated by Internet of Things (IoT) devices. Its in-memory processing capabilities contribute to efficient data handling.

Change Data Capture (CDC) is a technique in ETL (Extract, Transform, Load) that involves incrementally updating the data warehouse by identifying and capturing changes made to the source data since the last update. It is particularly useful for efficiently updating large datasets without reloading the entire dataset.

ETL processes are used to extract data from different regional databases, transform it into a common format, and load it into the data warehouse. This integration allows for global analysis and reporting across the entire organization.

Decision Trees are commonly used for classifying data into predefined categories. They work by recursively splitting the data based on features, forming a tree structure that represents decision paths.

In dashboard design, clutter should be minimized to focus the viewer's attention on the most important data. Unnecessary elements, like excessive labels or gridlines, can distract from key insights.

Network diagrams are effective for visualizing the structure of a dataset and the relationships between its components. Nodes represent data points, and edges represent connections or relationships, providing insights into the overall structure of the data.

Time zone standardization is crucial for ensuring data consistency when integrating data from multiple international sources. Consistent time representation is essential to avoid discrepancies and errors in data analysis and reporting.

Mean imputation is a common method used to handle missing data. It involves replacing missing values with the mean of the observed values in that column, providing a simple way to fill in gaps without introducing bias.

Effective problem-solving involves thinking systematically to consider various perspectives and analyze the problem from different angles. This helps in developing comprehensive and well-rounded solutions.