Learning Data Engineering: Module 1B

Most of module 1 was created on my local machine, this article will walk-through how to re-create those services on a Google Cloud virtual machine.

CONFIGURE A VM ON GOOGLE CLOUD (CLOUD + SSH)

CREATE A SSH KEY PAIR

We created .ssh directory mkdir ~/.ssh, generated a ssh key pair and ran the ssh-keygen command in terminal. You’ll be prompted with several questions to confirm selections. This will generate a public (gcp.pub) and private key (gcp) used for our ssh access. We opened the public file, using catgcp.pub command and copied its contents.

cd ~/.ssh
ssh-keygen -t rsa -f KEY_FILENAME -C USERNAME

Within the GCP console, paste the key contents under the Metadata tab. Connect to the instance from your local machine.

ssh -i ~/.ssh/[key_file] [user]@[external IP]

CONFIGURE GCP VM

Within the GCP, we configured the settings:

• Name, Region, Series and Machine Type

• For Boot Disk: Select Ubuntu 20.04 LTS, balanced persistent disk, and size to 30 GB.

All other settings were left to default values.

CONNECT SSH TO VM

ssh -i ~/.ssh/[Key_Filename] [user]@[external IP]

OPTIONAL: CONFIGURE A SSH ALIAS

To easily connect to machine from terminal, we made a configuration file that list server vm details by creating a config file within the .ssh directory. I learned that the HostName, User and IdentityFile must be indented or it won’t work. We can now ssh into the VM using the hostname ssh hostname.

Host host
    HostName hostname
    User user
    IdentityFile ~/.ssh/[key_file]

INSTALL PACKAGE LIBRARIES ON VM

We installed Anaconda, Docker, Docker-Compose, pgCLI and cloned our project’s GitHub repo all necessary for the course to our remote server.

ANACONDA - DOWNLOAD AND RUN THE FILE

wget https://repo.anaconda.com/archive/Anaconda3-2022.10-Linux-x86_64.sh

and, then bash Anaconda3-2022.10-Linux-x86_64.sh. When prompted, I entered yes to run conda init. Then, execute source .bashrc to apply the changes. Alternatively, you can apply changes by logging out of the terminal session and ssh back into the machine. I used which python to confirm that python is installed and that Anaconda is the active environment.

DOCKER - INSTALL AND RUN

sudo apt-get update
sudo apt-get upgrade
sudo apt-get install docker-io

I encountered a permission denied error so to avoid this, I configured it to run without needing sudo and give me the permission to run Docker commands. To take effect, log out and log back in. I ran docker run hello-world to confirm everything works.

sudo groupadd docker
sudo gpasswd -a $USER docker
sudo service docker restart

DOCKER-COMPOSE - DOWNLOAD AND RUN

We created a mkdir -p ~/bin for the download and marked it as an executable so the system can run it.

wget https://github.com/docker/compose/releases/download/v2.35.0/docker-compose-linux-x86_64 -O docker-compose

chmod +x docker-compose

The bin directory was added to the system’s PATH, at the end of .bashrc. This ensures any executable in ~/bin is included in my $PATH and to apply the changes, we ran source ~/.bashrc.

echo 'export PATH="${HOME}/bin:${PATH}"' >> ~/.bashrc

PGCLI - INSTALL AND CONNECT

I tested the connection using my database credentials from module 1.

# install pgcli
pip install pgcli

# connect to the database
pgcli -h [hostname] -u [username] -d [database-name]

OPTIONAL: SET-UP PORT FORWARDING IN VS CODE TO CONNECT TO PGADMIN AND JUPYTER

In VS Code, under the Ports tab and we added a new port by setting up port forwarding. By forwarding a port from the remote server, VS Code allows local access to services like PostgreSQL, making them behave as though they’re running on your own machine. In this case, we’re adding Postgres, pgAdmin and Jupyter Notebook ports as listed in the output of the docker ps command.

RUN JUPYTER ON DATA

We changed directory to the Jupyter script in module 1 within the virtual machine. I somehow did not clone the script to my remote server so I copied the file locally.

scp /path/to/local/ingest_data_file.ipynb [username]@vm_external_ip:/path/on/vm

I then ran the Jupyter server: jupyter notebook, which launches Jupyter Notebook to run the script. For some reason, this didn’t initially work for me because I had other applications running on port 8888. As a workaround, I identified the process using the 8888 port: lsof -i :8888 and terminated the process kill -9 <PID> on my local and remote machines.

TERRAFORM

DOWNLOAD TERRAFORM

The process to run Terraform was similar to how I installed the application on my local machine. We started by downloading the linux binary for Terraform inside the bin folder, from earlier.

wget https://releases.hashicorp.com/terraform/1.11.4/terraform_1.11.4_linux_amd64.zip

I also installed unzip to unzip the file.

sudo apt install unzip
unzip terraform_1.11.4_linux_amd64.zip

CONFIGURE GOOGLE CLOUD (GCP) SERVICE ACCOUNT

Like how we configured on my local machine, we needed to setup our GOOGLE_APPLICATION_CREDENTIALS remotely. It’s important to remember where you saved your my-gcp-key.json within your local Terraform folder, to copy it to your remote Terraform instance. I did create a .gc folder to store the key file remotely.

scp ~/terraform/[my-gcp-key.json] username@your-server-ip:~/.gc/

export GOOGLE_APPLICATION_CREDENTIALS=~/.gc/my-gcp-key.json

gcloud auth activate-service-account --key-file $GOOGLE_APPLICATION_CREDENTIALS

You should see a message saying the account was activated sucessfully. The creators of the Datacamp series used sftp instead of scp. If you're curious, check out this Youtube Video where he walks-through that method.

RUN TERRAFORM COMMANDS

We’re ready to run the basic Terraform commands: terraform init and terraform plan. No need to run terraform apply at this point— we’ve already created buckets for this project in module 1.

SHUTDOWN AND REMOVE VM

To shut down your virtual machine, you can either run sudo shutdown now in the terminal or use the Google Cloud Console to stop the instance manually by clicking the delete option.