Guide: Creating Datasets

This information is relevant only to developers wishing to create their own datasets for distribution.

What is a Dataset?

Think of a Dataset similar to a package managed by yum or apt. Instead of binaries and configuration files, installing a Dataset gives you a Cassandra schema, sample data, and a Jupyter notebook with tutorials on how to use that data.

Create a new project from the skeleton

Make sure CDM is installed. You will not be able to provide additional Python modules other than what CDM already provides (yet).

Create a new dataset with the cdm new command. It will generate a project skeleton for you. For example:

cdm new example-name

Installers are created by having a file called in the top level of your dataset. The installer must subclass cdm.installer.Installer. The cdm utility will discover the Installer automatically so the name is somewhat arbitrary, however it should reflect the dataset’s name as a convention.

Download resources and setup

Set up your post_init() hook. You should download and load any data into memory you’ll need for all the various imports:

class MovieLensInstaller(Installer):
    def post_init(self):
        context = self.context

If you need to download any data (like a zip file of CSVs, etc), you can use which will download and cache the file at the URL return a file pointer. Caching is provided automatically.

If you download a zip file, the easiest way to access the data is using the built in Python ZipFile module:

fp ="")
zf = ZipFile(file=fp)
fp ="ml-100k/u.item")

You can use the file pointers returned from as normal pointers. If you’re working with CSV data, it’s recommended to use the Pandas library (provided by CDM):

movies = read_csv(fp, sep="|", header=None, index_col=0, names=["id", "name", "genre"]).fillna(0)

If you’d like to include your data with your dataset (a good idea of the dataset is small), you

You can see how it’s pretty easy to use the Context to download and cache external files, then process and prepare using Pandas.

Set up Cassandra Schema

Next you’ll want to set up a schema for Cassandra. There’s a few options varying in complexity. Read up on the different options for configuring your Cassandra Schema.

Load Cassandra Data

Assuming you’ve loading some data into memory in the post_init(), you can now load data into your schema.

To load data, you’ll want to use the session provided by the Context:

class MyInstaller(Installer):
    def install_cassandra(self):
        context = self.context
        session = context.session
        prepared = session.prepare("INSERT INTO data (key, value) VALUES (?, ?)")
        for row in
            session.execute(prepared, row.key, row.value)

Provided Libraries

Cassandra Driver
The project would be useless without a driver, so it’s included. We will stay reasonably up to date with current packages. It is always made available via the Context as the session variable.
Pandas is an excellent library for reading various raw formats such as CSV. It also provides facilities for data manipulation, which may be required to transform data.
Faker makes for each generation of fake data. This is especially useful when you’re dealing with an incomplete data model or one that has been anonymized.


Testing datasets is important. This project is leveraging features of py.test that make it easy to test datasets.

CDM will include a tool for testing a project. This runs all the projects unit tests as well as tests that verify project structure and conventions:

cdm test

All tests must pass cdm test for inclusion in the official Dataset repository.