Logical data models provide the abstract structure for your analytics, but the physical implementation determines the actual query latency and cost. A well-designed schema must account for how the database engine stores and retrieves blocks of data from disk. This section addresses the specific configurations required to optimize storage layout in columnar data warehouses.

We begin by examining partitioning strategies. By dividing large tables into smaller segments based on specific columns - usually dates - you enable the query engine to skip large sections of the dataset during retrieval. If a table has $T$ total bytes and you query a single day representing a fraction of the data, partition pruning reduces the scan cost significantly. We can represent the ideal scanned volume $V$ for a perfectly pruned query as:

$V_{scanned} \approx \frac{V_{total}}{N_{partitions}}$

Next, we look at sorting and clustering. While partitioning segments the data coarsely, clustering organizes the rows within those segments to improve the effectiveness of filter predicates. You will learn how to select appropriate columns for clustering to minimize I/O operations.

The curriculum also covers the handling of semi-structured data. Modern data warehouses allow the use of nested fields, such as arrays and structs, which can simplify ingestion and reduce the complexity of join logic. Finally, we assess the One Big Table (OBT) design. This approach flattens dimensions and facts into a single wide table, trading storage efficiency for read performance by eliminating joins entirely. You will evaluate the trade-offs between this heavily denormalized approach and the traditional Star Schema.