diff --git a/docs/graph/athena.rst b/docs/graph/athena.rst
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 Setup on Amazon Athena
 ======================
 
 The Software Heritage Graph Dataset is available as a public dataset in `Amazon
 Athena <https://aws.amazon.com/athena/>`_. Athena uses `presto
 <https://prestodb.github.io/>`_, a distributed SQL query engine, to
 automatically scale queries on large datasets.
 
 The pricing of Athena depends on the amount of data scanned by each query,
 generally at a cost of $5 per TiB of data scanned. Full pricing details are
 available `here <https://aws.amazon.com/athena/pricing/>`_.
 
 Note that because the Software Heritage Graph Dataset is available as a public
 dataset, you **do not have to pay for the storage, only for the queries**
 (except for the data you store on S3 yourself, like query results).
 
 
 Loading the tables
 ------------------
 
 .. highlight:: bash
 
 AWS account
 ~~~~~~~~~~~
 
 In order to use Amazon Athena, you will first need to `create an AWS account
 and setup billing
 <https://aws.amazon.com/premiumsupport/knowledge-center/create-and-activate-aws-account/>`_.
 
+You will also need to create an **output S3 bucket**: this is the place where
+Athena will store your query results, so that you can retrieve them and analyze
+them afterwards.  To do that, go on the `S3 console
+<https://s3.console.aws.amazon.com/s3/home>`_ and create a new bucket.
+
 
 Setup
 ~~~~~
 
 Athena needs to be made aware of the location and the schema of the Parquet
 files available as a public dataset. Unfortunately, since Athena does not
 support queries that contain multiple commands, it is not as simple as pasting
 an installation script in the console. Instead, we provide a Python script that
 can be run locally on your machine, that will communicate with Athena to create
 the tables automatically with the appropriate schema.
 
 To run this script, you will need to install a few dependencies on your
 machine:
 
 - For **Ubuntu** and **Debian**::
 
     sudo apt install python3 python3-boto3 awscli
 
 - For **Archlinux**::
 
     sudo pacman -S --needed python python-boto3 aws-cli
 
 Once the dependencies are installed, run::
 
   aws configure
 
 This will ask for an AWS Access Key ID and an AWS Secret Access Key in
 order to give Python access to your AWS account. These keys can be generated at
 `this address
 <https://console.aws.amazon.com/iam/home#/security_credentials>`_.
 
 It will also ask for the region in which you want to run the queries. We
 recommand to use ``us-east-1``, since that's where the public dataset is
 located.
 
 Creating the tables
 ~~~~~~~~~~~~~~~~~~~
 
 Download and run the Python script that will create the tables on your account:
 
 .. tabs::
 
   .. group-tab:: full
 
     ::
 
-      wget https://annex.softwareheritage.org/public/dataset/graph/latest/athena/tables.py
-      wget https://annex.softwareheritage.org/public/dataset/graph/latest/athena/gen_schema.py
-      ./gen_schema.py
+      wget https://annex.softwareheritage.org/public/dataset/graph/latest/athena/athena.py
+      python3 athena.py -o 's3://YOUR_OUTPUT_BUCKET/'
 
   .. group-tab:: teaser: popular-4k
 
-    This teaser is not available on Athena yet.
+    ::
+
+      wget https://annex.softwareheritage.org/public/dataset/graph/latest/athena/athena.py
+      python3 athena.py -o 's3://YOUR_OUTPUT_BUCKET/' -d popular4k -l 's3://softwareheritage/teasers/popular-4k'
 
   .. group-tab:: teaser: popular-3k-python
 
-    This teaser is not available on Athena yet.
+    ::
+
+      wget https://annex.softwareheritage.org/public/dataset/graph/latest/athena/athena.py
+      python3 athena.py -o 's3://YOUR_OUTPUT_BUCKET/' -d popular3kpython -l 's3://softwareheritage/teasers/popular-3k-python'
 
 To check that the tables have been successfully created in your account, you
 can open your `Amazon Athena console
 <https://console.aws.amazon.com/athena/home>`_. You should be able to select
 the database corresponding to your dataset, and see the tables:
 
 .. image:: _images/athena_tables.png
 
 
 Running queries
 ---------------
 
 .. highlight:: sql
 
 From the console, once you have selected the database of your dataset, you can
 run SQL queries directly from the Query Editor.
 
 Try for instance this query that computes the most frequent file names in the
 archive::
 
   SELECT from_utf8(name, '?') AS name, COUNT(DISTINCT target) AS cnt
   FROM directory_entry_file
   GROUP BY name
   ORDER BY cnt DESC
   LIMIT 10;
 
 Other examples are available in the preprint of our article: `The Software
 Heritage Graph Dataset: Public software development under one roof.
 <https://upsilon.cc/~zack/research/publications/msr-2019-swh.pdf>`_