High cardinality in time-series data refers to the large number of unique values contained in a dataset, often resulting from detailed monitoring and sensor network deployments. This can lead to challenges such as full table scans and join operations becoming resource-intensive, potentially overwhelming database hardware. However, modern database systems offer solutions through sophisticated indexing strategies that help manage high-cardinality data by organizing it into more manageable subsets. Effective management of high cardinality data enables more precise analysis and deeper insights, but requires careful consideration of how to query the data efficiently.