This webinar aims to provide the BigQuery product walkthrough right from the basics. Our core focus will be on the use cases and applications that help to gain additional customer insights from the data integrated within BigQuery. BigQuery is equipped with the ability to crunch TBs of data in seconds while ensuring scalability and speed. It also enables us to perform advanced statistical analysis by providing unsampled raw hit level analytics data.

1. Getting Started with
BigQuery
Basics, Use Cases & its Applications

2. SpeakersModerator
Aditi Buch
Marketing Manager, Tatvic
Pankaj Bhatlwande
Customer Success Manager, Tatvic
Sarjak Patel
Lead - Customer Success Manager,
Tatvic
2
Who’s Who

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

4. OUTLINE
4
1 Introduction to BigQuery
2 Architecture
3 GA360 Data in BigQuery
4 Integrations and Use Cases
5 Hands-on Exercise

5. Introduction
5
Querying massive datasets Secure Access Control
Single view of your data pointsSuper-fast SQL-like queries
Google Analytics data in BigQuery

6. ● Scales in Petabytes
● Input/Output of TBs in seconds
● 100,000 rows/sec per table Streaming API
● Simple data ingest from GCS or Hadoop
● Connect to R, Pandas, Hadoop, Dataflow, etc.
● Row level security and data expiration
Power of BigQuery
6

7. OUTLINE
7
1 Introduction to BigQuery
2 Architecture
3 GA360 Data in BigQuery
4 Integrations and Use Cases
5 Hands-on Exercise

8. • BigQuery is based on Dremel, a technology pioneered by Google & extensively
used within
• Dremel is a querying service that allows you to run SQL queries against huge
datasets (think hundreds of millions of rows)
• It uses multi-level execution trees to achieve interactive performance for
queries against multi-terabyte datasets
• BigQuery's performance advantage comes from its parallel processing architecture
Architecture
8

9. Architecture
BigQuery Column IO Storage
Record Oriented Storage Column Oriented Storage
How does that help?
Column oriented storage reads data from columns which are being queried as compared to
all the data in row oriented storage
9

10. • The query is processed by thousands of servers in a multi-level execution tree
structure, with the final results aggregated at the root
• Data in BigQuery is structured in the below format:
• Datasets
• Tables
• Rows
• Columns
• BigQuery is a publicly available implementation of Dremel which is available as an
IaaS (Infrastructure as a Service)
Architecture
10

11. OUTLINE
11
1 Introduction to BigQuery
2 Architecture
3 GA360 Data in BigQuery
4 Integrations and Use Cases
5 Hands-on Exercise

12. Linking GA360 Data in BigQuery
12
Step 1: Create Google Cloud Platform Project
Step 2: Enable Billing Account
Step 3: Link BigQuery to GA360 Property
Step 4: Query Google Analytics Data

13. GA360 Data in BigQuery
13
GCP Project

14. User Interface
14
GCP Project
BigQuery
Dataset
BigQuery
Dataset

15. User Interface
15
GCP Project
BigQuery Dataset
BigQuery Dataset
Table Table
Table Table

16. Data Structure
GA360 Data in BigQuery Schema is stored as a row (record) for each session, with nested and repeated
fields for some dimensions and hits
16
BigQuery Export Schema Reference: https://support.google.com/analytics/answer/3437719

17. ✓ fullVisitorId represents unique visitor ID (hashed GA Client ID)
✓ visitId is aligned with how GA generates sessions
✓ For custom dimensions and metrics, scope matters!
✓ For Session and User scope – customDimensions
✓ For Hit scope - hits.customDimensions
✓ For Product Scope - hits.product.customDimensions
✓ The last-non direct attribution model applies
GA360 Data in BigQuery
17

18. GA360 Data in BigQuery
Access to raw data
Individual level customer
data
Opportunity to perform
statistical analyses
Ability to tie in other data
sources
BigQuery
Export
18

19. OUTLINE
19
1 Introduction to BigQuery
2 Architecture
3 GA360 Data in BigQuery
4 Integrations and Use Cases
5 Hands-on Exercise

20. Integrations with BigQuery
20

21. Sample Use Cases
21
• Get unsampled custom funnels with added benefits
• No Backfilling
• Historical Information
• Apply filters
• Unlimited steps
• Get the last interaction (event) that the user performed before
landing on a given page
• Get all the sessions with transactions wherein particular
events were performed by users and funnels generated after
a particular event has been performed

22. OUTLINE
22
1 Introduction to BigQuery
2 Architecture
3 GA360 Data in BigQuery
4 Integrations and Use Cases
5 Hands-on Exercise

23. 23
Overview of ‘spydeR’
Handle Large Hit Volume
Sampling will not be a concern for
GA Standard Users
Unsampled Data for
GA Standard Users
Real Time Reporting
Our highly Optimized and in-memory
market handler is designed to perform
millions of calculations in real time to
bring never seen before Predictive
analysis
Seamless Integration
Combine data from multiple resources
Actionable Insight
Trend Analysis
Campaign Optimization
Sentiment Analysis

24. Type your comments
and questions here
@Tatvic
24
Any Questions?

25. Title: BigQuery: Advanced Concepts and Working with Queries
Speakers: Sarjak and Pankaj
Date: November 30, 2017
Time: 8:30 PM IST
25
Upcoming Webinar

26. THANKS!
26
●Your data speaks. We help you listen to your data.