This course uses a bunch of tools for data science and big data with Python, that can cause hassles to install directly on a machine. Therefore, we built a Docker image for the course which makes things much easier. The main tools available in this image are :

The image provides also extra things, such as the Polynote notebook and Scala that we won’t use (but useful if you want to learn/use these) and Dask, which provides an alternative to Spark that we’ll only explore very quickly, since the course is focused on Spark.

It is mandatory to use this image. We won’t help to install anything on your laptop. The docker technology is an industrial standard and should work in almost all possible reasonable configurations.

Installation of the Docker image of the course

  1. Download, install and launch docker on your laptop at

  2. Create an account on docker hub (if you don’t have one already) at

  3. Open a terminal (powershell on windows) and type

    docker login

    and provide the username and password you use in

  4. Type the command

    docker pull stephanegaiffas/big_data_course:v2

    that will download the image we will use for the course. This might take some time as the image weights almost 3GB

  5. The image has been downloaded and appears when typing

    docker image ls

  6. Let’s run this image now, namely let’s create a container based on it. This is easily achieved using the docker-compose command. This command reads the contents of the YAML file called docker-compose.yml to understand how docker can create a container based on the image. First, open your favorite text editor to create a file named docker-compose.yml, and copy-paste this:

    version: "3.7"
        container_name: big_data_course
        image: stephanegaiffas/big_data_course:v2
          - "8192:8192"
          - "8888:8888"
          - "4040:4040"
        restart: always
          - "/Users/stephanegaiffas/Code/big_data_course/:/opt/polynote/notebooks/"
        restart: always

    The only thing to modify in this file is the path /Users/stephanegaiffas/Code/big_data_course indicated in the volumes section of the file. You just need to replace it by the path you want to use on your laptop, namely "PATH:/opt/polynote/notebooks/" where PATH is the path you want to use. Do not modify anything else in this line, in particular do not change the :/opt/polynote/notebooks/ path, and do NOT leave a space before the : symbol. You can add several such paths if it pleases you. Once you’ve finished to modify docker-compose.yml, save it and close it.

  7. Now, type in your terminal:

    docker-compose up

    In the terminal are printed a bunch of things, saying that inside the container, jupyter and polynote are running among other things.

  8. You can open another terminal (and close the previous one if you want) and type

    docker container ls

    which says that a container named big_data_course is running. You can now access this container in several ways described below.

Some explanations

The docker-compose up command that we used before reads the docker-compose.yml file in order to understand what it has to do to “up” the required services. This file explains to Docker that it has to run a service (a synonym for container) called big_data_course with the following informations:

  • the name of the container to create is big_data_course
  • the image to use is stephanegaiffas/big_data_course:v2 (the one you pulled before, note that Docker would have pulled it automatically if it was not already there)
  • there are some ports to map
  • and a volume to map
  • and there is an environment variable to define

Actually, we could have not used the docker-compose.yml file and type directly the following command to run directly the image:

$ docker run \
   --name big_data_course \
   -it --rm \
   -p 8888:8888 \
   -p 8192:8192 \
   -p 4040:4040 \
   -v /Users/stephanegaiffas/Code/big_data_course/:/opt/polynote/notebooks/ \

which is equivalent to what is described in docker-compose.yml. The options in this command line mean the following:

  • --name: we give the name big_data_course to the container (but we can give any other name)
  • -it: we run in interactive and terminal mode (allowing to see in the terminal the stdout of the container)
  • --rm: we remove the container after exit. If you want a persistent container, remove the --rm tag
  • -p: we map the ports 8888 and 4040 of the container to the ports 8888 and 4040 of the host machine (your laptop). If you are using already one of these ports an error is raised. You can change the port if you want, for instance -p 7777:8888 means that port 7777 on your laptop is mapped to the port 8888 in the container.
  • e: we need to set an environmental variable PYSPARK_ALLOW_INSECURE_GATEWAY for Spark
  • v: we need to mount a local volume (on the file system of your laptop) to a directory in the linux file system running in the container. Once again the option is -v LOCAL_PATH:CONTAINER_PATH. If we don’t do this, then the container has no access to the file system of the host and conversely, so that any file created or modified in the container would die with it. You can mount more directories if it pleases you.

Let us now explain all the things you can do/use with a container based on this image.

Access the Jupyter notebook

When the container is running locally you can access the Jupyter notebook from any web-browser using the link

This will show a password page

The password is abcdef. It’s useless to try and change it since you run Jupyter locally on your machine and not on a server. Then, you’ll end up with the jupyter home, showing the files in the PATH you specified in the docker-compose.yml file. Namely, you should see something like this :

Now, you can work !

Access a terminal with bash

If your container is named big_data_course this is done simply by typing

docker-compose exec big_data_course bash

which simply tells to execute bash in the container in interactive and terminal mode.

Run a Spark job using spark-submit

While we will mainly use pyspark in the jupyter notebook, we can also use the spark-submit command to launch a spark job. Note that for large jobs, it’s usually done this way.

docker-compose exec big_data_course bash
cd setup_files

This opens a terminal in the container, go to a folder containing the python script and runs it with spark-submit. You should see something starting like this:

and you should see at some point something like this:

The line Lines with a: ... is the output of this Spark job. To understand what it’s doing, let’s have a look at the script:

from pyspark.sql import SparkSession

logFile = "requirements.txt"  # Should be some file on your system
spark = SparkSession.builder.appName("SimpleApp").getOrCreate()
logData =

numAs = logData.filter(logData.value.contains('a')).count()
numBs = logData.filter(logData.value.contains('b')).count()

print("\n\n\n\nLines with a: %i, lines with b: %i\n\n\n\n" % (numAs, numBs))


It turns out that this Spark job is simply counting the number of lines in the requirements.txt file containing the letter a and the letter b.

Access Polynote

You can also use the Polynote notebook, which is a very recent project from NetFlix which provides an alternative to Jupyter, and is particularly interesting for Scala code. We won’t be using it in the course, but it’s nice to have access to it easily if you want to learn more about Scala (we’ll do only Python during this course). You can access it from a Chrome web-browser using the link

It’s pretty young, so it only works with Chrome for now.

The main docker commands

You might need to run, stop, rm, ls containers or images, so here are the most useful commands. Ideally, have a look at the docker tutorial here:

A list of the main commands is as follows:

  • docker-compose down : stops all the containers created by docker-compose up
  • docker container ls : list containers
  • docker image ls : list images
  • docker container stop id : stop the running container id
  • docker container rm id : remove container id
  • docker image rm id : remove image id
  • docker image prune : remove all unused images
  • docker container prune : remove all stopped containers
  • docker pull : download an image

Use docker container ls -a and docker image ls -a to see all containers and images. This is useful if you want to do some cleaning, when docker uses a lot of memory or disk space you might want to remove containers and images that are not used, using rm or prune.