You work for an advertising company and want to understand the effectiveness of your company's latest advertising campaign. You have streamed 500 MB…

AI Platform Notebooks is a service that provides managed Jupyter notebooks for data science and

machine learning. You can use AI Platform Notebooks to create, run, and share your code and

analysis in a collaborative and interactive environment1. BigQuery is a service that allows you to

analyze large-scale and complex data using SQL queries. You can use BigQuery to stream, store, and

query your data in a fast and cost-effective way2. Pandas is a popular Python library that provides

data structures and tools for data analysis and manipulation. You can use pandas to create,

manipulate, and visualize dataframes, which are tabular data structures with rows and columns3.

AI Platform Notebooks provides a cell magic, %%bigquery, that allows you to run SQL queries on

BigQuery data and ingest the results as a pandas dataframe. A cell magic is a special command that

applies to the whole cell in a Jupyter notebook. The %%bigquery cell magic can take various

arguments, such as the name of the destination dataframe, the name of the destination table in

BigQuery, the project ID, and the query parameters4. By using the %%bigquery cell magic, you can

query the data in BigQuery with minimal code and manipulate the results with pandas in AI Platform

Notebooks. This is the most convenient and efficient way to achieve your goal.

The other options are not as good as option A, because they involve more steps, more code, and

more manual effort. Option B requires you to export your table as a CSV file from BigQuery to Google

Drive, and then use the Google Drive API to ingest the file into your notebook instance. This option is

cumbersome and time-consuming, as it involves moving the data across different services and

formats. Option C requires you to download your table from BigQuery as a local CSV file, and then

upload it to your AI Platform notebook instance. This option is also inefficient and impractical, as it

involves downloading and uploading large files, which can take a long time and consume a lot of

bandwidth. Option D requires you to use a bash cell in your AI Platform notebook to export the table

as a CSV file to Cloud Storage, and then copy the data into the notebook. This option is also complex

and unnecessary, as it involves using different commands and tools to move the data around.

Therefore, option A is the best option for this use case.

Reference:

AI Platform Notebooks documentation

BigQuery documentation

pandas documentation

Using Jupyter magics to query BigQuery data