Sharding is a technique used in databases to improve performance by horizontally partitioning and distributing a large database across multiple servers or nodes. It helps distribute the workload and enhance scalability. Joins, Normalization, and Indexing are also techniques but do not specifically focus on distributing a large database.

ETL tools typically handle data from different sources with varying formats through data mapping and transformation. This involves creating mappings between source and target data structures, and applying transformations to ensure consistency and compatibility across the data.

The primary difference is that classification is used for predicting categorical outcomes (e.g., class labels), while regression is used for predicting numeric outcomes (e.g., quantity). Classification answers questions like "Is this email spam or not?" whereas regression answers questions like "How much will the house sell for?"

The primary purpose of an API (Application Programming Interface) in web development is to facilitate communication between different software systems, allowing them to exchange data and functionality. APIs define the methods and data formats that applications can use to communicate with each other.

Power BI is known for its efficient real-time data processing capabilities, allowing users to analyze and visualize data as it is generated. Tableau also supports real-time analytics but may not be as efficient as Power BI in certain scenarios.

The knitr package in R is widely used for creating dynamic reports and documents. It enables the integration of R code and output into various document formats. The other options (reportr, docgen, dynamicdoc) are not standard packages for dynamic report generation in R.

Autocorrelation measures the correlation of a time series with its own past values at different lags. It helps identify patterns and dependencies within the time series data.

The main benefit is scalability. Cloud-based data warehouses offer the ability to scale resources up or down based on demand, providing flexibility and cost-effectiveness compared to traditional warehouses with fixed hardware.

In advanced data warehousing, Stream Processing is used for real-time data processing and analytics. This technique allows for the processing of data as it is generated, enabling quick insights and analysis in real-time scenarios.

The Insertion sorting algorithm is efficient for datasets that are already substantially sorted because it has minimal time complexity in best-case scenarios. Its adaptive nature makes it suitable for nearly sorted data.