Database Reference
In-Depth Information
$
bq query "
SELECT SUM(word_count)
FROM publicdata:samples.shakespeare
WHERE corpus = 'hamlet'"
Waiting on bqjob_… (0s) Current status: DONE
+
-------+
| f0_ |
+-------+
| 32446 |
+-------+
For more information on
ebq
and available encryption modes, check out the
Encrypted BigQuery docs available from
https://pypi.python.org/
Scientific Data Processing Tools in BigQuery
Sometimes, pure SQL isn't enough for your data analysis needs. Maybe
you'd like to train a machine learning model on your data or apply advanced
statistical functions to squeeze meaning from your tables. A number of
scientific and mathematical processing tools are available to perform these
types of analyses.
This section shows how to use BigQuery with two popular free scientific
processing applications: R and Pandas. R is a popular programming
language and runtime environment for statistical data processing. Pandas,
while it is somewhat less ubiquitous than R, has the advantage that it
enables you to work directly in Python. If you're already using Python,
pandas can be easier to integrate with your existing tools and libraries. Both
R and pandas have good support for BigQuery. R can interact with BigQuery
via an extension package written by a researcher from Rice University.
Pandas, on the other hand, has direct BigQuery support available in the
library.
If you use a different tool that doesn't have direct support for BigQuery,
it is likely you can connect to BigQuery via ODBC, which makes BigQuery
look like any other relational database. The “BigQuery Connectors” section
earlier in this chapter has more information to help out in these cases.
