Add magics tutorial with BigQuery Storage API integration. #2087
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This is a notebooks tutorial, modeled after the Jupyter notebook example code for BigQuery. Use some caution when running these tests, as they run some large-ish (5 GB processed) queries and download about 500 MB worth of data. This is intentional, as the BigQuery Storage API is most useful for downloading large results.
googlebot added the cla: yes 
shollyman approved these changes Apr 8, 2019
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I see why you'd like to convert transform simple queries to a set of projection columns and a row filter for the storage case, as it would avoid the intermediate query.
Only issue is related to the ongoing testing costs. Looks like dependencies table is ~7GB which is probably still reasonable given it can short circuit and the query looks cacheable.
plamut pushed a commit to plamut/python-bigquery-storage that referenced this pull request Sep 2, 2020
…oogleCloudPlatform/python-docs-samples#2087) * Add magics tutorial with BigQuery Storage API integration. This is a notebooks tutorial, modeled after the Jupyter notebook example code for BigQuery. Use some caution when running these tests, as they run some large-ish (5 GB processed) queries and download about 500 MB worth of data. This is intentional, as the BigQuery Storage API is most useful for downloading large results. * Update deps. * Don't run big queries on Travis.
plamut pushed a commit to googleapis/python-bigquery-storage that referenced this pull request Sep 10, 2020
…oogleCloudPlatform/python-docs-samples#2087) * Add magics tutorial with BigQuery Storage API integration. This is a notebooks tutorial, modeled after the Jupyter notebook example code for BigQuery. Use some caution when running these tests, as they run some large-ish (5 GB processed) queries and download about 500 MB worth of data. This is intentional, as the BigQuery Storage API is most useful for downloading large results. * Update deps. * Don't run big queries on Travis.
Linchin pushed a commit that referenced this pull request Aug 18, 2025
…2087) * Add magics tutorial with BigQuery Storage API integration. This is a notebooks tutorial, modeled after the Jupyter notebook example code for BigQuery. Use some caution when running these tests, as they run some large-ish (5 GB processed) queries and download about 500 MB worth of data. This is intentional, as the BigQuery Storage API is most useful for downloading large results. * Update deps. * Don't run big queries on Travis.
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parthea pushed a commit to googleapis/google-cloud-python that referenced this pull request Aug 21, 2025
…oogleCloudPlatform/python-docs-samples#2087) * Add magics tutorial with BigQuery Storage API integration. This is a notebooks tutorial, modeled after the Jupyter notebook example code for BigQuery. Use some caution when running these tests, as they run some large-ish (5 GB processed) queries and download about 500 MB worth of data. This is intentional, as the BigQuery Storage API is most useful for downloading large results. * Update deps. * Don't run big queries on Travis.
parthea pushed a commit to googleapis/google-cloud-python that referenced this pull request Sep 16, 2025
…oogleCloudPlatform/python-docs-samples#2087) * Add magics tutorial with BigQuery Storage API integration. This is a notebooks tutorial, modeled after the Jupyter notebook example code for BigQuery. Use some caution when running these tests, as they run some large-ish (5 GB processed) queries and download about 500 MB worth of data. This is intentional, as the BigQuery Storage API is most useful for downloading large results. * Update deps. * Don't run big queries on Travis.
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This is a notebooks tutorial, modeled after the Jupyter notebook example
code for BigQuery. Use some caution when running these tests, as they
run some large-ish (5 GB processed) queries and download about 500 MB
worth of data. This is intentional, as the BigQuery Storage API is most
useful for downloading large results.