A hash table organises data so you can quickly look up values for a given key

Strengths

Fast lookups. Lookups take O(1) time on average.
Flexible keys. Most data types can be used for keys, as long as they're hashable.

Weaknesses

Slow worst-case lookups. Lookups take O(n) time in the worst case.
Unordered. Keys aren't stored in a special order. If you're looking for the smallest key, the largest key, or all the keys in a range, you'll need to look through every key to find it.
Single-directional lookups. While you can look up the value for a given key in O(1) time, looking up the keys for a given value requires looping through the whole dataset — O(n) time.
Not cache-friendly. Many hash table implementations use linked lists, which don't put data next to each other in memory.

Hash maps are built on arrays

Arrays are pretty similar to hash maps already. Arrays let you quickly look up the value for a given "key" except the keys are called "indices," and we don't get to pick them — they're always sequential integers (0, 1, 2, 3, etc).

Think of a hash map as a "hack" on top of an array to let us use flexible keys instead of being stuck with sequential integer "indices."

All we need is a function to convert a key into an array index (an integer). That function is called a hashing function.

To look up the value for a given key, we just run the key through our hashing function to get the index to go to in our underlying array to grab the value.

How does that hashing method work? There are a few different approaches, and they can get pretty complicated. But here's a simple proof of concept:

Grab the number value for each character and add those up.