The tf.data.Dataset.concatenate() method is the closest analog of tf.stack() when working with datasets. If you have two datasets with the same structure (i.e. same types for each component, but possibly different shapes):

dataset_1 = tf.data.Dataset.range(10, 20)
dataset_2 = tf.data.Dataset.range(60, 70)

then you can concatenate them as follows:

combined_dataset = dataset_1.concatenate(dataset_2)

If by stacking you mean what tf.stack() and np.stack() do:

Stacks a list of rank-R tensors into one rank-(R+1) tensor.

https://www.tensorflow.org/api_docs/python/tf/stack

Join a sequence of arrays along a new axis.

https://docs.scipy.org/doc/numpy/reference/generated/numpy.stack.html

then I believe the closest you can come with a tf.data.Dataset is Dataset.zip():

@staticmethod
zip(datasets)

Creates a Dataset by zipping together the given datasets.

https://www.tensorflow.org/api_docs/python/tf/data/Dataset?version=stable#zip

This allows you to iterate through multiple datasets at the same time by iterating over the shared dimension of the original datasets, similarly to a stack()ed tensor or matrix.

You can then also use .map(tf.stack) or .map(lambda *t: tf.stack(t, axis=-1)) to stack the tensors along new dimensions at the front or back, respectively,

If indeed you want to achieve what tf.concat() and np.concatenate() do, then you use Dataset.concatenate().

Assume you have two datasets which elements shape is respectively (bs,d0,d1) and (bs,d0',d1) and you want a new dataset which element shape is (bs,d0+d0',d1) you can do it using tf.Dataset.zip and then concatenating each element on the second axis, like in the example below:

import tensorflow as tf

a = tf.zeros((100,4,8))
b = tf.ones((100,1,8))

d1 = tf.data.Dataset.from_tensor_slices(a)
d1 = d1.batch(16,drop_remainder=True)      # elements shape (16,4,8)

d2 = tf.data.Dataset.from_tensor_slices(b)
d2 = d2.batch(16,drop_remainder=True)      # elements shape (16,1,8)

d = tf.data.Dataset.zip((d1,d2))
d = d.map(lambda x,y:tf.concat([x,y],axis=-2)) # elements shape (16,4+1,8)

it = iter(d)
x = next(it)
print(x.shape)
print(x)

If you want instead to stack two datasets with the same elements shape (bs,d0,d1) into a new dataset with elements shape (bs,d0,d1,2) you can do it zipping the two datasets and then staking the elements

import tensorflow as tf

a = tf.zeros((100,4,8))
b = tf.ones((100,4,8))

d1 = tf.data.Dataset.from_tensor_slices(a)
d1 = d1.batch(16,drop_remainder=True)      # elements shape (16,4,8)

d2 = tf.data.Dataset.from_tensor_slices(b)
d2 = d2.batch(16,drop_remainder=True)      # elements shape (16,4,8)

d = tf.data.Dataset.zip((d1,d2))
d = d.map(lambda x,y:tf.stack([x,y],axis=-1)) # elements shape (16,4,8,2)

it = iter(d)
x = next(it)
print(x.shape)
print(x)