Data warehouse partitioning is a database technique used to divide large tables into smaller, more manageable pieces called partitions. This approach is designed to improve the management and processing of very large volumes of data. Partitioning can be applied not only to tables but also to indexes. A partitioned index may exist on a non-partitioned table, and a partitioned table may have non-partitioned indexes. Despite this internal structure, partitioning is transparent to applications, meaning users can write SQL queries and data manipulation statements in the same way as with non-partitioned tables. 

There are two primary ways to partition a table: vertically and horizontally. Vertical partitioning divides a table based on its columns, grouping attributes so they can be stored separately. Frequently used columns can be placed in one partition, while less frequently used columns are stored in another. This allows more relevant data to fit into main memory, which can reduce processing time. Horizontal partitioning, on the other hand, divides a table by rows. Each partition has the same structure as the original table but contains fewer records. A common example is partitioning a fact table by time, such as by month or year. Queries that focus only on recent data can access just the relevant partition, and data warehouse refresh operations become more efficient because only the latest partition may need updating. 

Partitioning offers several benefits beyond performance. It simplifies administrative tasks by breaking large tables and indexes into smaller, more manageable units. Maintenance operations, such as backups and index rebuilding, can be performed on individual partitions instead of the entire table. Partitioning also increases data availability. If one partition becomes unavailable, other partitions may still remain online, allowing the system to continue serving queries that do not require the unavailable data. Because partitions can be stored in different tablespaces or devices, backup and recovery can be done independently for each partition, reducing downtime. 

Query performance can be significantly improved in partitioned databases through techniques such as partition pruning. Partition pruning occurs when the database system identifies which partitions are relevant to a query and scans only those partitions. For example, if a sales table is partitioned by month and a query requests data for a single month, only that partition is accessed instead of the entire table. This can lead to dramatic improvements in response time. Partitioning can also enhance join performance. When two tables involved in a join are partitioned using the join attributes, or when a referenced table is partitioned by its primary key, large joins can be broken into smaller joins between corresponding partitions. This can be further accelerated through parallel processing. 

From a management perspective, partitioning supports more flexible and efficient database administration. Administrators can back up or restore individual partitions rather than whole tables, which saves time and resources. It also supports higher availability, as the database can continue operating even if certain partitions are temporarily offline. This makes partitioning particularly useful in large-scale data warehouse environments where continuous access to data is critical. 

There are three common partitioning strategies used in database systems: range partitioning, hash partitioning, and list partitioning. Range partitioning assigns rows to partitions based on value ranges of a partition key, often a date column. For instance, a partition may contain all rows from a specific month. Hash partitioning distributes rows among partitions using a hashing algorithm applied to the partition key, aiming for an even distribution of data across partitions. This method is often used when data needs to be spread across multiple devices and when time-based partitioning is not required. List partitioning allows explicit control by assigning rows to partitions based on predefined lists of values for the partition key, enabling customized data organization. Some database vendors, such as Oracle, also support composite partitioning, which combines multiple strategies, such as range partitioning at the top level and hash partitioning within each range partition. 

Reference: 

Vaisman, A., & Zimányi, E. (2014). Data warehouse systems: Design and implementation. Springer.