The document outlines the agenda for a Google Cloud Study Jam session focusing on various cloud database solutions including cloud storage, relational databases like Cloud SQL and Spanner, non-relational databases such as Firestore and Bigtable, and the capabilities of BigQuery for data analysis. It highlights the features and use cases for each database service, emphasizing scalability, performance, and ease of management, and addresses challenges companies face in realizing data value. Notably, it mentions that a significant percentage of top Google Cloud customers utilize these services for their enterprise needs.

1. Google Cloud Study
Jam
Session 4

2. Session Agenda
1. Cloud Storage
2. Database Services

10. Database Services

12. Types of Databases

13. Proprietary + Confidential
Your database choice depends on your needs
Availability
Consistency Cost Future proofing Data model
Multi-Region Skill Set Operations Compatibility
Open standard

14. Proprietary + Confidential
In-Memory Database in the Cloud

15. Proprietary + Confidential
Memorystore
Fully Managed Redis and Memcached database for sub-millisecond data
access

16. Proprietary + Confidential
Real-time analytics
For workloads that require microseconds latency,
Memorystore is a scalable, secure, highly available
in-memory service
Fully compatible with Redis and
Memcached, offering easy migration
for Redis and Memcached workloads.
Over 90% of the top 100 Google
Cloud customers use Memorystore.
Example use
cases
Caching
Session store
Leaderboard
Jobs and queues
Fast data ingestion

17. Proprietary + Confidential
Relational Databases in the Cloud

18. Proprietary + Confidential
Cloud SQL
Managed Database Platform for MySQL, PostgreSQL and SQL Server Databases

19. Fully Managed & Enterprise Ready
Easy to set up, operate, and scale
Trusted
Enterprise-grade data protection, security and governance
Developer Friendly
Application centric observability and API-first administration
Supports PostgreSQL, MySQL and SQL
Server
Full compatibility with source database engines
More than
90%
of Google Cloud’s top 100 customers use Cloud
SQL
Cloud SQL
Fully managed relational database
service

20. Proprietary + Confidential
Spanner
Enterprise-grade, globally-distributed, and strongly-consistent managed
SQL database service built for the cloud

21. What is
Spanner?
Relational
ACID transactions,
SQL, Schemas
Horizontally
scalable
Distributed RDBMS,
Near unlimited scale
Fully managed
++
Simplified administration,
Enterprise grade
99.999% uptime SLA
Automatic sharding
Superior price-performance
No maintenance downtime
Zero-touch global replication
Automatic failure recovery
RPO =0, RTO = 0
Online, unlimited scaling
Security and compliance
Strong external consistency
Spanner processes over 2 billion requests per second at
peak Spanner has more than 6 exabytes of data under
management

22. Proprietary + Confidential
Non-Relational Databases in the
Cloud

23. Proprietary + Confidential
Firestore
Serverless NoSQL document database built for automatic scaling, high performance in the cloud

24. Firestore
Unlock application innovation with simplicity, speed and confidence
Firestore by the numbers
Over
4 million
databases have been created in Firestore
Firestore apps power more than
1 billion
monthly active end-users using Firebase Auth
Serverless, document database
JSON-compatible data model, serializable ACID
transactions, elastic scalability, up to 99.999% availability
SLA, pay only for what you consume
Secure, backend as a service
Connect directly and securely to the database, making
middle tiers optional
Real-time sync & offline access
Built-in data syncing, and fallback to on-device caching
when a client loses network connectivity
Well integrated
Deliver results faster with native integrations with Google
Cloud, Firebase and 3rd party developer services via
Extensions

25. Proprietary + Confidential
Bigtable
Globally distributed, fully managed NoSQL database with high performance
at any scale

26. Bigtable
Real-time data serving and operational analytics at any scale
Bigtable has over 10
Exabytes of data under
management
Bigtable processes more than 5
Billion requests per second at peak
High throughput
Millions of RPS, Predictable
single-digit ms latency
Compatible
HBase API, Apache Spark,
Integrates with Apache Beam
ecosystem
Flexibility at scale
Flexible schema, eventual consistency*
Example use
cases
Fraud detection
Data Fabric/Operational
Data Store
Time Series
Product/Content metadata
* Strong consistency within a single cluster
Personalization
Customer 360
Battle tested by
Google

27. Proprietary + Confidential
68%
of companies are unable to realize measurable value from data.
More duplication
More silos
More complexity
More point
solutions More
security risk
Data is big and
multi-format.
Data requires more than
SQL.
Data reaches
everyone.
High costs
Constant Capacity
Planning Low
productivity
Limited access
Data
unavailable
Poor SLAs
Unclear compliance
Accenture, Closing the Data Value Gap

28. BigQuery
The core of
Google’s Data
Cloud to power
your data-driven
innovation.
BigQuery
Limitless
data
Limitless
reach
Limitless
workloads

29. Limitless
Data

30. Why BigQuery?
Limitless
data
Identity management
Distributed
Memory
Shuffle Tier
BigQuery
Completely elastic
Distributed storage and compute with ultra-high
bandwidth including distribute petabyte scale in-memory
storage for temp data and state:
● Auto-start and auto-pause
● 0-Second warm up to get maximum performance
● Accelerate queries in flight
● No performance cliff due to local capacity saturation
● Immune to large-scale hardware failures
Google Cloud
Security
Petabit network
Hardware infrastructure
Collect Process Activate
Store Analyze Empowe
r
Replicated,
Distributed
Storage
(99.9999999999%
)
High-Available
Cluster
Compute
(Dremel)
VS
● Simplifies capacity management
● Dynamically adjusts to demand
● Plan, manage, pay VMs
● Limit use data due to capacity
restrictions
Completely
serverless

31. Why BigQuery?
Limitless
data
All your data types in one
platform
● Structured
● Semi-structured (JSON)
● Unstructured (text, images, docs)
● Parquet
● JSON
● Nested Tables
● Geospatial
VS
● Manage pipelines and
integrations
● Miss value from
unsupported data types
● Simplifies data type
management with a
unified ecosystem
● Provides unique data
capabilities (geospatial)
All data
types

32. Limitless
Workloads

33. Why BigQuery?
All
workloads
Machine Learning for all Built-in
ML with SQL
● Execute, iterate, and automate ML
initiatives all within BigQuery using
predefined models
● Leverage external models developed in
Tensorflow directly from SQL
● Export developed models for use in
Vertex AI
VS
● Provide ML access to
more users through a
simple SQL interface
● Require every ML use
case to go through more
specialized systems that
require advanced skill
sets
Built-in AI/ML |
BQML

34. Limitless
Reach

35. Open
Everyone can analyze billions of
rows of data in Sheets, without
specialized DW knowledge
No additional charge with any
Google Workspace plan - Enterprise,
Business, and Personal (free)
Connected Sheets for
Looker
Sheet
s
Easy to use and
share
Intelligent
Familiar interface
Simple and flexible
analysis
+
BigQuery
Analyze petabytes
of data
Complex queries
Reduce time to insights
Looker
60+ database
connections available
Modeled data
Integrated insights
Connected
Sheets
Analyze billions of rows
of data in Sheets,
without any need for
specialized knowledge
For
everyone
BI Beyond
Dashboards
Spreadsheet Analysis of Tomorrow. Today.

36. Thank You