BigQuery is Google Cloud Platform's interactive big data service that allows users to analyze massive datasets in seconds using SQL-like queries. It offers a scalable and fast way to query terabytes of data without the expense of maintaining servers or databases. BigQuery organizes data into a project-dataset-table hierarchy and uses a distributed architecture to efficiently process queries across servers.

2. Agenda
 Why BigQuery
 What is BigQuery
 Architecture
 BigQuery Organization
 Use case
 Pros and cons

3. Why BigQuery?
● Generate big data reports require expensive servers and
skilled database administrators.
● Interacting with big data has been expensive, slow and
inefficient.
● BigQuery changes all that
 Reducing time and expense to query data.

4. What’s BigQuery?
● Service for interactive analysis of massive datasets (TBs)
 Query billions of rows, seconds to write, seconds to return
 Uses a SQL-style query syntax
● Reliable and secure
 Replicated across multiple sites
 Secured through Access Control Lists
● Scalable
 Store hundreds of terabytes
 Pay only for what you use
● Fast
 Run ad hoc queries on multi-terabyte data sets in seconds
BigQuery is Google Cloud Platform’s interactive big data service. You can analyze terabytes of
data in a matter of seconds through SQL-like queries.

5. At a high level, a query from the client is received at the
root server, the workload is divided and distributed to a
number of intermediate servers, which is then further
reduced to the leaf servers. The leaf servers read from the
columnar-based storage.

6. Architecture

7. BigQuery Organization
BigQuery is structured as a hierarchy with 4 levels:
● Project. All data in BigQuery belongs inside a project
● Dataset. Holds one or more tables
● Table. Row-column structure that contains actual data
● Job. The tasks you are performing on the data

8. Datasets and Tables
Table name is represented as
follows:
● Current Project
<dataset>.<table
name>
● Different Project
<project>:<dataset>.<table>
e.g. publicdata:samples.wikipedia

9. Use cases
● Log Analysis. Making sense of computer generated records
● Retailer. Using data to forecast product sales
● Ads Targeting. Targeting proper customer sections
● Sensor Data. Collect and visualize ambient data
● Data Mashup. Query terabytes of heterogeneous data

10. Some customer case studies
Uses BigQuery to hone ad targeting and gain insights into
their business
Dashboards using BigQuery to analyze booking and
inventory data
Use BigQuery to provide their customers ways to expand
game engagement and find new channels for
monetization
Used BigQuery, App Engine and the Visulizaton API to
build a business intelligence solution

11. Pros
 Automatically optimises queries to fetch data quickly.
 Allows efficient management of data across multiple
databases.
 Relatively easy to use interface once you get used to it.
 Data volume management.
 BigQuery Omni allows for querying data across cloud
platforms, including Azure, AWS, and GCP

12. cons
 Queries that have not been performance-tuned and queries
returning a lot of redundant data can become costly very quickly
 Tooling support outside of the GCP ecosystem is often lacking
compared to other platforms
 Works best with flat tables, which can make managing an
enterprise data model difficult

13. Thank you!
Questions?