Introducing SnowflakeData warehousing for everyone

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Current realities

Complex Data Infrastructure

Complex systems, data pipelines, data silos

EDW Datamarts

Hadoop / noSQL

Data Diversity ChallengesExternal data, multi-structured data, machine-generated data

Barriers to AnalysisAnalysis limited by incomplete

data, delays in access, performance limitations

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Our vision: Reinvent the data warehouse

Data warehousing for everyone

Data warehouse performance &

enterprise capabilities

Cloud elasticity & agility

Big data flexibility & scalability

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Our product: The Snowflake Elastic Data

Warehouse

All-new SQL data warehouse

No legacy code or constraints

Delivered as a serviceNo infrastructure, knobs

or tuning to manage

Designed for the cloud

Running in Amazon Web Services

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A team of data expertsExpert team• Experts in databases and data

processing from leading companies• >100 years collective

experience building databases• >120 patents

Leading investors

Bob Muglia, CEOFormer President of Microsoft’s Server and Tools Business

Benoit Dageville, Founder & CTOLead architect of Oracle parallel execution and a key manageability architect

Marcin Zukowski, Founder & VP of EngineeringInventor of vectorized query execution in databases

Thierry Cruanes, Founder ArchitectLeading expert in query optimization and parallel execution at Oracle

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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

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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

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Scale using multi-dimensional elasticity

• Elastic scaling for storageLow-cost cloud storage, fully replicated and resilient

• Elastic scaling for computeVirtual warehouses scale up & down on the fly to support workload needs

• Elastic scaling for concurrencyScale concurrency using independent virtual warehouses

Data Science

Reporting

Marketing

Loading / ETL

Test

Development

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Bringing together structured & semi-structured data

> SELECT … FROM …

Semi-structured data(e.g. JSON, Avro,

XML)

Structured data (e.g. CSV, TSV, …)

Direct ingestionLoad in raw form (e.g.

JSON, Avro, XML)Optimized storageOptimized data type,

no fixed schema or transformation required

Optimized SQL queryingFull benefit of database optimizations (pruning,

filtering, …)

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Data warehouse as a service

Hardware infrastructure

Software infrastructure

Data modeling

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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

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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

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Customer results

Gaming companyReplace 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 companyReplace on-premises data warehouse with Snowflake for analytics workload

TelcoImproved performance while adding new workloads at a fraction of the cost

Snowflake: added 2 new workloads for $50K

Data warehouse appliance: $5M + to expand

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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