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Data Studio vs. Cloud Datalab

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Contents

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Introduction
Conclusion
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Overview
DifficultyIntermediate
Duration40m
Students726

Description

Course Description

Google Data Studio is a web-based application for creating reports and dashboards. It’s an easy-to-use tool for displaying your data visually. It was designed to help Google Analytics users create custom reports, but it can now read data from many sources, including BigQuery, Cloud SQL, and Cloud Storage.

In this course, you will learn how to connect a Data Studio report to a BigQuery dataset, visualize it with charts and graphs, and share it with your co-workers to make data-driven decisions.

Learning Objectives

  • Create a report in Data Studio
  • Connect a Data Studio report to a BigQuery dataset
  • Share a Data Studio report with appropriate levels of access
  • Explain the differences between Data Studio and Cloud Datalab

Intended Audience

  • Data professionals, especially those who work with big data
  • People studying for the Google Professional Data Engineer exam

Prerequisites

  • “Introduction to Google BigQuery” course or experience with BigQuery
  • Google Cloud Platform account (sign up for free trial at https://cloud.google.com/free if you don’t have an account)

This Course Includes

  • 39 minutes of high-definition video
  • Many hands-on demos

 

Transcript

If you look at the list of big data services on Google Cloud Platform, you’ll see one called Cloud Datalab. It says that you can use it to visualize large datasets (and that includes BigQuery datasets). That sounds like what you can do with Data Studio, so why does Google have two different products for this? Well, they serve different purposes.

Cloud Datalab is intended for doing for data science and machine learning. While Data Studio is focused on reports, Datalab is focused on notebooks. Here’s an example.

This is a Jupyter notebook, which is a mix of code, results, and documentation. It was generated by the Jupyter Notebook App, which is an open-source application that runs in Datalab. Jupyter was formerly known as IPython, so you may see references to that.

The idea is that a data scientist can explore, analyze, and visualize data, and keep a record of that exploration. You can use Python or one of the other languages supported by Jupyter to work with the data. You can also share a notebook with other people when you’re working on a project together. You can even execute code in an existing notebook.

Since you have to write code to create visualizations in Datalab, it’s much more difficult to use than Data Studio. It’s also more difficult to get Datalab running. You have to spin up a VM and connect your browser to it over a special port. That’s not nearly as user friendly as Data Studio.

Datalab is very useful for data scientists, though. It’s just not a general reporting tool like Data Studio.

And that’s it for Cloud Datalab.

About the Author

Students13770
Courses41
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Guy launched his first training website in 1995 and he's been helping people learn IT technologies ever since. He has been a sysadmin, instructor, sales engineer, IT manager, and entrepreneur. In his most recent venture, he founded and led a cloud-based training infrastructure company that provided virtual labs for some of the largest software vendors in the world. Guy’s passion is making complex technology easy to understand. His activities outside of work have included riding an elephant and skydiving (although not at the same time).