In this webinar, we will cover advanced concepts and some complex queries. We will give a demo of how to fetch data from BigQuery into tools like Excel, R and python so we can continue further analysis. Along With Hands-on exercise, we will demonstrate how to automate queries using Apps Script and Command Line tool.

1. Interacting with
BigQuery
Working with Advanced Queries

2. SpeakersModerator
Pankaj Bhatlawande
Customer Success Manager, Tatvic
Sarjak Patel
Lead - Customer Success Manager,
Tatvic
2
Who’s Who
Karan Dave
Content Marketing Manager, Tatvic

3. Type your comments
and questions here
@Tatvic
3
How to Interact?

4. Recap of Webinar #1
4
Introduction to BigQuery
Architecture
GA 360 Data in BigQuery
Integrations and Use Cases

5. OUTLINE
5
1 Interacting with BigQuery
2 Useful Concepts and Queries
3 Automating Queries
4 Exporting Data
5 Joining Online & Offline Data

6. Interacting with BigQuery
6
Web UI
REST API
Command-Line Tool

7. Interacting with BigQuery
7
Web UI
https://bigquery.cloud.google.com/welcome/

8. Interacting with BigQuery
8
Command-Line Tool
• Python-based tool
• https://cloud.google.com/bi
gquery/bq-command-line-
tool

9. Interacting with BigQuery
9
BigQuery
C#
Go
Java
Node.jsPHP
Python
Ruby
➢ Direct HTTP requests to the server
➢ Provide idiomatic, generated or
hand-written code in each language
➢ Handle all the low-level details of
communication with the server
➢ Cloud Client Libraries available in
multiple programming languages
REST API

10. OUTLINE
10
1 Interacting with BigQuery
2 Useful Concepts and Queries
3 Automating Queries
4 Exporting Data
5 Joining Online & Offline Data

11. Useful Concepts and Queries
11
Custom
Funnels
Page
Sequence
Analysis
Useful
Functions
Best
Practices
Custom
Funnels
Using
Flatten

12. Page Sequence Analysis
12
• Understand the sequence the users follow to reach a certain page
• The sequences can be modified to be before or after and to also include events
Landing
Page
Page
A
Page
B
Page
C
Contact
Page

13. Custom Funnels
13
• Get unsampled custom funnels with added
benefits
- No Backfilling
- Historical Information
- Apply filters
- Unlimited steps
• Both open and closed funnels

14. 14
Using Flatten
Sample Data Set
• BigQuery uses repeated (nested) fields to
store data
• While querying nested data, BigQuery
automatically flattens the table data
• However, when dealing with more than one
repeated field, we need to explicitly use
FLATTEN on the table

15. 15
Using Flatten
While querying data using
below query,
BigQuery flattens the data
automatically

16. 16
Using Flatten
• However, running the query:
• It shows the error:
• Use FLATTEN as per shown in below query to fetch proper results:

17. 17
Useful Functions
• BigQuery functions are more or less similar in nature to that of regular SQL
• However, there are some functions which act differently to an extent
COUNT([DISTINCT] field [, n]) REGEXP_MATCH('str', 'reg_exp')
EXACT_COUNT_DISTINCT(field) REGEXP_REPLACE('orig_str', 'reg_exp', 'replace_str')
GROUP_CONCAT('str' [, separator]) DATEDIFF(<timestamp1>,<timestamp2>)
FIRST(expr), LAST(field) DAY/MONTH/HOUR/MINUTE(<timestamp>)
STRING(numeric_expr),
INTEGER(expr)
TABLE_DATE_RANGE(prefix, timestamp1, timestamp
2)
CONCAT('str1', 'str2', '...') ROW_NUMBER()
DATE(<timestamp>) RANK()

18. Best Practices
18
✓ Start by sketching the expected result
before writing your query
✓ Only select the relevant fields; The
more fields you select, the more data
it has to process and eventually
leading to more time and cost
✓ Avoid using functions as fields when
possible and prefer specific times and
dates, to allow caching
✓ Use intermediate tables for commonly
used subqueries
✓ Use caching whenever possible,
reduce execution time and costing
✓ Format and comment your queries, it
will be easier to read, understand and
debug
✓ Statements are not case-sensitive but
it’s a best practice to capitalize

19. OUTLINE
19
1 Interacting with BigQuery
2 Useful Concepts and Queries
3 Automating Queries
4 Exporting Data
5 Joining Online & Offline Data

20. Automating Queries
20
Using Command-Line Tool
1. Launch virtual machine on Google Cloud
2. Upload command line scripts
3. Schedule queries using cronjob

21. Automating Queries
21
Using Apps Script
• Google Apps Script is a scripting language based on JavaScript
• Uses Google BigQuery API
Manage BigQuery projects
Upload new data
Execute queries
• https://developers.google.com/apps-script/advanced/bigquery

22. OUTLINE
22
1 Interacting with BigQuery
2 Useful Concepts and Queries
3 Automating Queries
4 Exporting Data
5 Joining Online & Offline Data

23. Exporting Data
23
BigQuery Connector for Excel
Google Cloud Storage R Studio
Google
Sheets

24. OUTLINE
24
1 Interacting with BigQuery
2 Useful Concepts and Queries
3 Automating Queries
4 Exporting Data
5 Joining Online & Offline Data

25. Joining Online & Offline Data
25
GA
360
CRM
Data
BigQuery
Using Command-Line Tool
1. Upload CRM data to BigQuery
2. Use join queries to create a Single View
3. Analyze end-to-end user journey

26. Type your comments
and questions here
@Tatvic
26
Any Questions?

27. Title: How to Make the Best Use of Your Google Analytics 360
Data for eCommerce Businesses
Speakers: Bismayy Mohapatra and Anshul Bhatt
Date: December 14, 2017
Time: 8:30 PM IST
27
Upcoming Webinar

28. THANKS!
28
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