A brief introduction to Google BigQuery concepts with learning resources. Understand the benefits, challenges, and core concepts of BigQuery.

1. BIGQUERY

2. Agenda
• What is BigQuery?
• Why BigQuery?
• An overview of BigQuery’s Architecture
• Advantages of BigQuery
• BigQuery Challenges
• Core Concepts of BigQuery
• Links to learning resources

3. What is BigQuery?
• BigQuery was released in 2011 as an externalized version of the Dremel query
service software.
• A serverless, scalable, and cost-efficient cloud warehouse from Google Cloud
Platform (GCP).
• A Platform-as-a-Service (PaaS) that supports storing and querying of datasets
using ANSI SQL.
• A service that runs on Google storage infrastructure.

4. Why BigQuery?
• Interactive analysis, running ad hoc queries faster and storing hundreds of
terabytes of data.
• Use SQL-style syntax to query billions of rows in seconds.
• Reliable service with the ability to be replicated across multiple sites and
accessible through APIs.
• A service secured through Access Control Lists.

5. An overview of BigQuery's architecture
BigQuery Architecture

6. An overview of BigQuery's architecture
BigQuery: Under the hood

7. Advantages of BigQuery
Visibility and
Accessibility on
Insights with Predictive
& Real-Time Analytics.
Accessing Data &
Sharing Insights.
Implementing Data
Protection.
Creating crash reports
and processing spam
analysis.

8. BigQuery Challenges
Some of these challenges may arise ideally when Organizations implement
BigQuery solutions:
• Slow data movement during migration.
• Increased infrastructural parameters due to limitations in the existing
architecture.
• Performance issues due to lack of native integration and complexities.
• Security concerns during migration.

9. Core Concepts of BigQuery
BigQuery
ML
BigQuery
Omni
BigQuery
BI Engine
BigQuery
GIS
Building,
exporting and
operationalizing
all of the ML
models on
planet-scale,
semi-structured
and structured
data within
BigQuery.
A multi-cloud
analytics
solution within
BigQuery for
managing, and
analyzing data
across all
clouds, such as
Azure, AWS, or
Google.
It is termed as
an in-memory
analysis service,
that is used to
analyze larger
datasets in a
short response
time with high
concurrency.
Helps to combine
BigQuery’s
serverless
architecture with
the native support
embedded upon
geospatial
analysis.

10. Links to Learning Resources
It's important to go through the official BigQuery documentation and articles that
explain all the aspects of BigQuery in detail. Learn more about BigQuery by visiting
these links:
1. Google BigQuery Documentation.
2. GCP Labs – BigQuery- Whizlabs
3. What is BigQuery? – Whizlabs

11. THANK YOU