A heap is a specialized tree-based data structure that satisfies the heap property: If $A$ is a parent node of $B$ then $key(A)$ is ordered with respect to $key(B)$ with the same ordering applying across the heap. Either the keys of parent nodes are always greater than or equal to those of the children and the highest key is in the root node (this kind of heap is called max heap) or the keys of parent nodes are less than or equal to those of the children (min heap). Heaps are crucial in several efficient graph algorithms such as “Dijkstra's Algorithm”, and in the sorting algorithm “Heap Sort”.
Note that, as shown in the graphic, there is no implied ordering between siblings or cousins and no implied sequence for an in-order traversal (as there would be in, e.g., a binary search tree). The heap relation mentioned above applies only between nodes and their immediate parents. The maximum number of children each node can have depends on the type of heap, but in many types it is at most two, which is known as a binary heap.
The heap is one maximally efficient implementation of an abstract data type called a priority queue, and in fact priority queues are often referred to as "heaps", regardless of how they may be implemented. Note that despite the similarity of the name "heap" to "stack" and "queue", the latter two are abstract data types, while a heap is a specific data structure, and "priority queue" is the proper term for the abstract data type.
A heap data structure should not be confused with the heap which is a common name for dynamically allocated memory. The term was originally used only for the data structure.