Which filter type in Tanium uses a Bloom filter for determining data uniqueness?

The New Items Filter uses a Bloom filter to determine data uniqueness within Tanium. A Bloom filter is a space-efficient probabilistic data structure that allows for fast checks of whether an element is a member of a set. In the context of the New Items Filter, this means it can efficiently ascertain whether certain data points have already been seen or processed, which is critical for identifying new or unique items.

The advantage of using a Bloom filter is its ability to provide quick results with low memory overhead. This characteristic is particularly beneficial when dealing with a large amount of data, as it allows Tanium to handle uniqueness checks without significant processing delays.

This filter distinguishes itself from others by focusing specifically on new or unique items, making it essential in scenarios where keeping track of previously encountered data is crucial for accurate reporting or analysis. Other filter types like Numeric, Regular Expression, and Combined Filters do not employ Bloom filters for uniqueness checks, as they serve different purposes or functionalities in data filtering.