Introducing Snowflake, an elastic data warehouse delivered as a service in the cloud. It aims to simplify data warehousing by removing the need for customers to manage infrastructure, scaling, and tuning. Snowflake uses a multi-cluster architecture to provide elastic scaling of storage, compute, and concurrency. It can bring together structured and semi-structured data for analysis without requiring data transformation. Customers have seen significant improvements in performance, cost savings, and the ability to add new workloads compared to traditional on-premises data warehousing solutions.

1. Introducing Snowflake
Data warehousing for everyone

2. 2
Current realities
Complex Data Infrastructure
Complex systems, data pipelines,
data silos
EDW Datamarts
Hadoop
/ noSQL
Data Diversity Challenges
External data, multi-structured data,
machine-generated data
Barriers to Analysis
Analysis limited by incomplete data,
delays in access, performance
limitations

3. 3
Our vision:
Reinvent the data warehouse
Data warehousing for everyone
Data warehouse
performance &
enterprise capabilities
Cloud elasticity &
agility
Big data flexibility &
scalability

4. 4
Our product:
The Snowflake Elastic Data Warehouse
All-new SQL data
warehouse
No legacy code or
constraints
Delivered as a service
No infrastructure, knobs or
tuning to manage
Designed for the cloud
Running in Amazon Web
Services

5. 5
A team of data experts
Expert team
• Experts in databases and data
processing from leading companies
• >100 years collective experience
building databases
• >120 patents
Leading investors
Bob Muglia, CEO
Former President of Microsoft’s Server and
Tools Business
Benoit Dageville, Founder & CTO
Lead architect of Oracle parallel execution and
a key manageability architect
Marcin Zukowski, Founder & VP of
Engineering
Inventor of vectorized query execution in
databases
Thierry Cruanes, Founder Architect
Leading expert in query optimization and
parallel execution at Oracle

6. 6
Our value proposition
Bring together diverse
data and workloads in
one system
Simplify and accelerate
path from data to
analytics
Remove the cost and
complexity of
conventional solutions

7. 7
A new architecture:
Multi-cluster, shared data
• Standard interfaces
• Cloud services layer
coordinates across service
• Independent compute
clusters access data
• Data centralized in enterprise-
class cloud storage

8. 8
Scale using multi-dimensional elasticity
• Elastic scaling for storage
Low-cost cloud storage, fully
replicated and resilient
• Elastic scaling for compute
Virtual warehouses scale up &
down on the fly to support
workload needs
• Elastic scaling for concurrency
Scale concurrency using
independent virtual warehouses
Data
Science
Reporting
Marketing
Loading /
ETL
Test
Development

9. 9
Bringing together structured & semi-
structured data
> SELECT … FROM …
Semi-structured data
(e.g. JSON, Avro, XML)
Structured data
(e.g. CSV, TSV, …)
Direct ingestion
Load in raw form (e.g.
JSON, Avro, XML)
Optimized storage
Optimized data type,
no fixed schema or
transformation required
Optimized SQL querying
Full benefit of database
optimizations (pruning, filtering, …)

10. 10
Data warehouse as a service
Hardware infrastructure
Software infrastructure
Data modeling
Data analysis
Customer
Index
management
Data
partitioning
Metadata
updates
Dataprotection
Availability&
DR
Security
implementation
Query
optimization

11. 11
No infrastructure, knobs, or tuning
Infrastructure
management
Virtual hardware and
software managed by
Snowflake
Metadata
management
Automatic statistics
collection, scaling, and
redundancy
**..
**..
Manual query
optimization
Dynamic optimization,
parallelization, and
concurrency management
Data storage
management
Adaptive data distribution,
automatic compression,
automatic optimization

12. 12
Customers
“Snowflake is faster, more flexible,
and more scalable than the
alternatives on the market. The fact
that we don’t need to do any
configuration or tuning is great
because we can focus on analyzing
data instead of on managing and
tuning a data warehouse.”
Craig Lancaster, CTO

13. 13
Customer results
Gaming company
Replace noSQL data store with Snowflake
for storing & transforming event data
Snowflake: 1.5 minutes
noSQL data store:
8 hours
Snowflake: 26 minutes
Data warehouse appliance:
7 hours
Market research company
Replace on-premises data warehouse with
Snowflake for analytics workload
Telco
Improved performance while adding new
workloads at a fraction of the cost Snowflake: added 2 new workloads for $50K
Data warehouse appliance:
$5M + to expand

14. 14
Customer example
Before
• Fragile data pipeline
• Delays in getting updated data
• High cost and complexity
• Limited data granularity
After
• >50x faster data updates
• Reduced costs by >50%
• Nearly eliminated pipeline failures
• Able to retain full data granularity