In this course, you'll gain an understanding of how to begin consolidating your code, compute, and data all together in a Google Cloud Platform environment using a variety of developer tools combined with GCP services you're already familiar with.
At the core of cloud development is a thorough knowledge of Cloud Source Repositories. In this course, Developing on the Google Cloud Using Datalab and Cloud Source Repositories, you’ll learn how to use developer tools on the Google cloud. First, you’ll learn the features that these products offer and see how easily they can integrate with other GCP services. Next, you’ll explore the suite of developer tools that you can work with on the Google Cloud and how to pick the best tool for your use case. Finally, you’ll discover how to create your Cloud Datalab instance for data exploration and visualization. When you’re finished with this course, you’ll have a foundational knowledge of Google Cloud Source Repositories which will help you as you move forward in bringing your code, compute, and data all together on one platform.
An engineer and tinkerer, Vitthal has worked at Google, Credit Suisse, and Flipkart and
studied at Stanford and INSEAD. He has worn many hats, each of which has involved
writing code and building models. He is passionately devoted to his hobby of laughing at
his own jokes.
Course Overview Hi, my name is Vitthal Srinivasan, and I'd like to welcome you to this course on Developing on the Google Cloud Using Datalab and the Cloud Source Repositories. A little bit about myself, I have masters degrees in financial math and electrical engineering from Stanford University and have previously worked in companies such as Google in Singapore and Credit Suisse in New York. I am now a co-founder at Loonycorn, a studio for high-quality video content based in Bangalore, India. Once data and compute are both on the cloud, the logical next step is to have the code moved there as well. The GCP offers several products to help with this, of which two browser-based technologies are particularly worth of detailed study. These are Cloud Datalab for on-cloud Python development with cloud-source repositories for committing and integrating code with the version-control system. In this course, we will understand the features that these products offer and see how easily they integrate with other GCP services. First you will understand the suite of developer tools that you can work with on the GCP, and learn how to pick the best tool for your use case. Cloud Datalab is a service that allows you to run GCP-hosted Jupyter Notebooks on a compute-engine instance. This instance runs a Jupyter Docker container and it's preinstalled with a number of libraries and packages, which are useful for data scientists and analysts. Next you will learn how you can create your Cloud Datalab instance for data exploration and visualization. You will learn how you can configure your Datalab instance to share in a team environment and how Datalab offers easy integrations with other GCP services such as Cloud Storage and BigQuery, wire magic functions, as well as wire client libraries. You will see how code from your Datalab instance can be committed to a Git report on the Cloud Source Repositories. Finally, you will move on to studying Cloud Source Repositories in detail, learning how to clone your repository to your local machine, and use Git to manage branches, commits, and integrations. You'll also get a taste of how a CI/CD pipeline can be set up by integrating Cloud Source Repositories with cloudBit. You can also configure interactions with a remote repo or a repo hosted on another platform such as GitHub or Bitbucket. Google Cloud Source Repos support all of these use cases. When you're done with this course, you will be comfortable using developer tools on the Google cloud, thus bringing code, compute, and data all together onto one platform.