type workload. Generally, in data warehouses, query planning time is less of a concern as the majority of processing time is spent during query execution. With either of these two types of workload, it as re-partitioning large quantities of data can be painfully slow. Simulations of the intended workload are often beneficial for optimizing the partitioning strategy. Never assume that more partitions better to create separate indexes and rely on the index-combination feature. For example, if your workload includes a mix of queries that sometimes involve only column x, sometimes only column y, and sometimes

type workload. Generally, in data warehouses, query planning time is less of a concern as the majority of processing time is spent during query execution. With either of these two types of workload, it as re-partitioning large quantities of data can be painfully slow. Simulations of the intended workload are often beneficial for optimizing the partitioning strategy. Never assume that more partitions better to create separate indexes and rely on the index-combination feature. For example, if your workload includes a mix of queries that sometimes involve only column x, sometimes only column y, and sometimes

type workload. Generally, in data warehouses, query planning time is less of a concern as the majority of processing time is spent during query execution. With either of these two types of workload, it as re-partitioning large quantities of data can be painfully slow. Simulations of the intended workload are often beneficial for optimizing the partitioning strategy. Never just assume that more partitions better to create separate indexes and rely on the index-combination feature. For example, if your workload includes a mix of queries that sometimes involve only column x, sometimes only column y, and sometimes

type workload. Generally, in data warehouses, query planning time is less of a concern as the majority of processing time is spent during query execution. With either of these two types of workload, it as re-partitioning large quantities of data can be painfully slow. Simulations of the intended workload are often beneficial for optimizing the partitioning strategy. Never just assume that more partitions better to create separate indexes and rely on the index-combination feature. For example, if your workload includes a mix of queries that sometimes involve only column x, sometimes only column y, and sometimes

type workload. Generally, in data warehouses, query planning time is less of a concern as the majority of processing time is spent during query execution. With either of these two types of workload, it as re-partitioning large quantities of data can be painfully slow. Simulations of the intended workload are often beneficial for optimizing the partitioning strategy. Never just assume that more partitions better to create separate indexes and rely on the index-combination feature. For example, if your workload includes a mix of queries that sometimes involve only column x, sometimes only column y, and sometimes

type workload. Generally, in data warehouses, query planning time is less of a concern as the majority of processing time is spent during query execution. With either of these two types of workload, it as re-partitioning large quantities of data can be painfully slow. Simulations of the intended workload are often beneficial for optimizing the partitioning strategy. Never just assume that more partitions better to create separate indexes and rely on the index-combination feature. For example, if your workload includes a mix of queries that sometimes involve only column x, sometimes only column y, and sometimes

type workload. Generally, in data warehouses, query planning time is less of a concern as the majority of processing time is spent during query execution. With either of these two types of workload, it as re-partitioning large quantities of data can be painfully slow. Simulations of the intended workload are often beneficial for optimizing the partitioning strategy. Never just assume that more partitions better to create separate indexes and rely on the index-combination feature. For example, if your workload includes a mix of queries that sometimes involve only column x, sometimes only column y, and sometimes

type workload. Generally, in data warehouses, query planning time is less of a concern as the majority of processing time is spent during query execution. With either of these two types of workload, it as re-partitioning large quantities of data can be painfully slow. Simulations of the intended workload are often beneficial for optimizing the partitioning strategy. Never just assume that more partitions better to create separate indexes and rely on the index-combination feature. For example, if your workload includes a mix of queries that sometimes involve only column x, sometimes only column y, and sometimes

warehouse type workloads, it can make sense to use a larger number of partitions than with an OLTP type workload. Generally, in data warehouses, query planning time is less of a concern as 101 Data Definition these two types of workload, it is important to make the right decisions early, as re-partitioning large quantities of data can be painfully slow. Simulations of the intended workload are often beneficial better to create separate indexes and rely on the index-combination feature. For example, if your workload includes a mix of queries that sometimes involve only column x, sometimes only column y, and sometimes

type workload. Generally, in data warehouses, query planning time is less of a concern as the majority of processing time is spent during query execution. With either of these two types of workload, it as re-partitioning large quantities of data can be painfully slow. Simulations of the intended workload are often beneficial for optimizing the partitioning strategy. Never just assume that more partitions better to create separate indexes and rely on the index-combination feature. For example, if your workload includes a mix of queries that sometimes involve only column x, sometimes only column y, and sometimes