Explore the features of the charting API in GCP.
- [Instructor] I want to show you how to use the Charting API in Datalab in order to build graphs and visualizations. We will do so with data from both BigQuery and Cloud Storage. The exercise file for this, and the following videos in the module, will be available under "05_XX_Visualizations_with_Datalab." First, let me take a look at the Charting API. It can be accessed through the command "%%chart." Doing "--help" on this command shows all the different types of charts that can be created.
This includes common types, like bar, line, histogram and pie. You can also do complex charts, like treemap, candlestick, heatmap and org. You can add descriptions, like titles, and also control the size of the charts. Combining charts with data from BigQuery and Cloud Storage provides a unique platform on GCP to do exploratory analysis and share this data with other stakeholders through Notebooks.
- Setting up Cloud DataLlb for exploratory data analytics
- Segmentation and profiling
- Reading and writing data from BigQuery
- Managing cloud storage buckets
- Creating visualizations of BigQuery data with the GCP Charting API
- Managing Datalab instances
Skill Level Intermediate
Predictive Customer Analyticswith Kumaran Ponnambalam1h 37m Intermediate
1. Exploration Options in GCP
2. Cloud Datalab Basics
3. Datalab: BigQuery
4. Datalab: Cloud Storage
5. Datalab: Visualizations
6. EDA with GCP: Use Case
7. Managing Datalab
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.Cancel
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.