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Select the Working with CQL notebook When opening the notebook you will see a connection exception.

Ive found myself in this situation too many times, with a variety of databases, while working with multiple organizations.

I write so that you can skip through the operational steps, and start building brilliant data models, sculpting beautiful queries, and quickly prototyping cloud applications that not only look great, but are responsive, scalable, and indestructible.

All of those non-functional requirements of your application are highly dependent on choosing the right data layer.

At the end of this guide, you will have a containerized environment hosting both the DataStax Distribution of Cassandra and a notebook based data exploration tool for performing data modeling, query profiling, and data visualization.

Step 1. Create Docker Account As you may have assumed by the title, you will need Docker installed with access to the Docker Hub repository to pull the Cassandra images.

Docker Images are prebuilt making it easier to consume over traditional tar-ball distributions with the additional benefits of workspace isolation.

If you already have a docker account, skip this step.

Step 2.

Installing Docker Containers add a level of platform independence allowing for installation on various operating systems including Linux, Mac, and Windows.

Find your operating system below, and follow the installation process.

After installing, you will have access to the docker terminal from the command line terminal.

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Windows: Visit Windows Download and Tutorial Mac: Visit Mac Download and Tutorial Step 3.

Pull The Docker Images To pull docker images from the Docker Hub, you will need to provide your docker account credentials (see step 1).

Then you can execute the docker pull commands from the terminal.

Docker Login Following the login command, you will be prompted to provide the docker id and password.

Once the login completes, the credentials will be added to your session and used on all subsequent requests.

Studio also has the ability to save, import and export notebooks.

This allows you to share your findings with your team as you go.

Awesome) docker pull datastaxdse-studio:latest studio pull output Step 4.

Once the container is created you wont have to perform the run command again (i.e.

Start the DataStax Server Container The -name parameter provides a human readable reference for the container operations, but can also be used as a resolvable hostname for communication between containers ( required for later steps ).

As stated before, the DataStax distribution comes with some additonal integrations for building different models, making it highly sought after for implementing domain driven design patterns.

The -g flag starts a Node with Graph Model enabled The -s flag starts a Node with Search Engine enabled The -k flag starts a Node with Spark Analytics enabled docker run -e DSLICENSEaccept --memory 4g --name my-dse -d datastaxdse-server -g -s -k Start DataStax Studio Container The - link parameter provides a way to map a hostname to a container IP address.

In this example, we map the database container to Studio container by providing its name, my-dse.

Now Studio can connect to the database using the container name instead of an IP address.

The -p flag is for mapping ports between container and host.

Connecting Studio Visit the Studio page that is now hosted on your docker container by entering in your browser.

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