Demystifying Data Warehousing as a Service - DFW

1© 2017 Snowflake Computing Inc. All Rights Reserved.
Y O U R D A T A , N O L I M I T S
© 2017 Snowflake Computing Inc. All Rights Reserved.
@KentGraziano
KENT GRAZIANO
Chief Technical Evangelist
Snowflake Computing
Demystifying
Data Warehousing as a Service
(DWaaS)

• Chief Technical Evangelist, Snowflake Computing
• Oracle ACE Director (DW/BI)
• OakTable Network
• Blogger – The Data Warrior
• Certified Data Vault Master and DV 2.0 Practitioner
• Former Member: Boulder BI Brain Trust (#BBBT)
• Member: DAMA Houston & DAMA International
• Data Architecture and Data Warehouse Specialist
• 30+ years in IT
• 25+ years of Oracle-related work
• 20+ years of data warehousing experience
• Author & Co-Author of a bunch of books (Amazon)
• Past-President of ODTUG and Rocky Mountain Oracle
User Group
My Bio

About Snowflake
Experienced,
accomplished
leadership team
2012
Founded by
industry veterans
with over 120
database patents
Vision:
A world with
no limits on data
First data
warehouse
built for the
cloud
Over 1000
customers
since GA

• Data Challenges
• What is a Data Warehouse as a Service?
• What can a Cloud DWaaS do for me?
• Features of a Cloud DWaaS
• Top 10 (or so) Cool Features of Snowflake
• Becoming more Agile with DWaaS
• Where Snowflake fits
• Spark Integration
• Continuous Loading
• Conclusion
Agenda

5© 2017 Snowflake Computing Inc. All Rights Reserved.© 2017 Snowflake Computing Inc. All Rights Reserved.
Data challenges today

Scenarios with affinity for cloud
Gartner 2016
Predictions:
By 2018, six
billion connected
things will be
requesting
support.
Connecting applications, devices, and
“things”
Reaching employees, business partners,
and consumers
Anytime, anywhere mobility
On demand, unlimited scale
Understanding behavior; generating,
retaining, and analyzing data

40 Zettabytes by 2020
Web ERP3rd party apps Enterprise apps IoTMobile

It’s not the data itself
it’s how you take full advantage of the insight it provides
Web ERP3rd party apps Enterprise apps IoTMobile

Most firms don’t consistently turn data into action
Source: Forrester
All Possible data All Possible Action
of firms
aspire to be
data-driven.
73%
of firms are
good at
turning data
into action.
29%

The data struggle
Data is stale
Hard to incorporate
new data sources
Too much time
waiting for data
Too much time spent
on manual
administration
Reports are too slow
whenever the system
is busy
Hard to experiment
with data

Symptoms of fundamental challenges
Data silos
Data locked into
separate
databases, big
data systems, and
applications
Inflexibility
Slow, cumbersome
scaling and limited
support for diverse
data
Complexity
Multiple systems
to integrate and
manage requiring
specialized skills
and tools
Performance
Contention for
limited resources
resulting in latency
and delays
Cost
Painful upfront
costs and
overprovisioned
capacity

The evolution of data platforms
Data warehouse
& platform
software
Vertica,
Greenplum,
Paraccel, Hadoop
Data
warehouse
appliance
Teradata
1990s 2000s 2010s
Cloud DWaaS
Snowflake
1980s
Relational
database
Oracle, DB2,
SQL Server

What is a Cloud DWaaS?
DW- Data Warehouse
• Relational database
• Uses standard SQL
• Optimized for fast loads and
analytic queries
aaS – As a Service
• Like SaaS (e.g. SalesForce.com)
• No infrastructure set up
• Minimal to no administration
• Managed for you by the vendor
• Pay as you go, for what you use

Goals of a Cloud DWaaS
Make your life easier
• So you can load and use your data
faster
Support business
• Make data accessible to more people
• Reduce time to insights
Handle big data too!
• Schema-less ingestion

What to Expect from a
Cloud DWaaS It should support standard SQL
(natively)
• It should support standard ETL, BI
& data science tools
• ODBC or JDBC connectivity
It should be infinitly scalable
(cloud)
• Handle huge amounts of data
• Handle large number of concurrent
queries without performance
degradation
It should handle flexible schema
data types
• No sharding or ETL required

What to Expect from a
Cloud DWaaS
It should be secure
• Built in encryption?
It shoud be stable
• Resiliancy and availability should be
easy to configure and manage
It should be easy to configure and
manage
It should provide a lower TCO
• Cloud scale pricing

Common customer scenarios
Data warehouse
for SaaS offerings
Use Cloud DW as back-
end data warehouse
supporting data-driven
SaaS products
noSQL replacement
Replace use of noSQL
system (e.g. Hadoop) for
transformation and SQL
analytics of multi-
structured data
Data warehouse
modernization
Consolidate legacy
datamarts and support
new projects

Enabling key use cases
Datamart & data silo consolidation
Consolidate legacy datamarts to eliminate silos and support new projects
Integrated data analytics
Directly load structured + semi-structured data into Snowflake for reporting
& analytics
Data Science, Exploratory & ad hoc analytics
Direct access to data for SQL analysts and data scientists to explore data,
identify correlations, build & test models

Introducing Snowflake

Snowflake:
1st Data Warehouse Built for the Cloud
SQL relational database
Optimized storage & processing
Standard connectivity – BI, ETL, …
Data Warehousing…
Existing SQL skills and tools
“Load and go” ease of use
Cloud-based elasticity to fit any scale
Data
scientists
SQL
users &
tools
…for Everyone

What is Snowflake?
Built for the
cloud
SQL Data
Warehouse
Our Vision
Provide all users anytime, anywhere
insights so they can make actionable
decisions based on data
Our Solution
Next-generation data warehouse built
from the ground up for the cloud and
for today’s data and analytics
Delivered as
a service

Snowflake’s differentiating technology
Unique architecture: Multi-cluster, shared data
Single place for
data
z
Instant, unlimited
scalability
Zero
management
Instant, live data
sharing

The Data Warrior’s
Top 10+ Cool Things About Snowflake
(A Data Geeks Guide to Cloud-native DW)

#10 – Persistent Result Sets
• No setup
• In Query History
• By Query ID
• 24 Hours
• No re-execution
• No Cost for Compute

#9 -Connect w/JDBC & ODBC to the cloud
Data Sources
Custom & Packaged
Applications & Data
Science
ODBC WEB UIJDBC
Interfaces
Java
>_
Scripting
Reporting
& Analytics
Data Modeling,
Management &
Transformation
SDDM

#8 - UNDROP
UNDROP TABLE <table name>
UNDROP SCHEMA <schema name>
UNDROP DATABASE <db name>
Part of Time Travel feature: AWESOME!

#7 Fast Clone (Zero-Copy)
Instant copy of table, schema, or database:
CREATE OR REPLACE TABLE MyTable_V2
CLONE MyTable;
With Time Travel:
CREATE SCHEMA mytestschema_clone_restore
CLONE testschema
BEFORE (TIMESTAMP =>
TO_TIMESTAMP(40*365*86400));
PROD
PUBLIC
Table A Table B
Table C
DEV
PUBLIC
Table A Table B
Table C
PUBLIC
Table A Table B
Table C
INT

#6 – JSON Support with SQL
Apple 101.12 250 FIH-2316
Pear 56.22 202 IHO-6912
Orange 98.21 600 WHQ-6090
Structured data
(e.g. CSV)
Semi-structured data
(e.g. JSON, Avro, XML)
{ "firstName": "John",
"lastName": "Smith",
"height_cm": 167.64,
"address": {
"streetAddress": "212nd Street",
"city": "New York",
"state": "NY",
"postalCode": "10021-3100"
},
"phoneNumbers": [
{ "type": "home", "number":"212555-1234"},
{ "type": "office", "number": "646 555-4567"}
]
}
Optimized storage
Flexible schema - Native
Relational processing
select v:lastName::string as last_name
from json_demo;
All Your Data!

#5 – Standard SQL w/Analytic Functions
select Nation, Customer, Total
from (select
n.n_name Nation,
c.c_name Customer,
sum(o.o_totalprice) Total,
rank() over (partition by n.n_name
order by sum(o.o_totalprice) desc)
customer_rank
from orders o,
customer c,
nation n
where o.o_custkey = c.c_custkey
and c.c_nationkey = n.n_nationkey
group by 1, 2)
where customer_rank <= 3
order by 1, customer_rank
SQL
Complete SQL database
• Data definition language (DDLs)
• Query (SELECT)
• Updates, inserts and deletes (DML)
• Role based security
• Multi-statement transactions
New partner post: https://sonra.io/2018/02/04/create-custom-aggregate-udaf-window-functions-snowflake/

#4 – Separation of Storage & Compute
Snowflake’s multi-cluster, shared data architecture
Centralized storage
Instant, automatic scalability & elasticity
Service
Compute
Storage

#3 – Support Multiple Workloads
Accelerate the data pipeline
Run loading & analytics at any time, concurrently, to get
data to users faster
Scale compute to support any workload
Scale processing horsepower up and down on-the-fly,
with zero downtime or disruption
Scale concurrency without performance impact
Multi-cluster “virtual warehouse” architecture scales
concurrent users & workloads without contention
Deliver faster analytics
at any scale
Loading
Marketing
Finance

• 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
Automatically scale
concurrency using multi-
cluster virtual warehouses
Instant, unlimited scalability

#2 – Secure by Design with Automatic Encryption of Data!
Embedded
multi-factor authentication
Federated authentication
available
Certified against enterprise-
class requirements
HIPPA Certified!
PCI Certified!
All data encrypted, always,
end-to-end
Encryption keys managed
automatically
NEW: Tri-secret security
Role-based access
control model
Granular privileges on all
objects & actions
Authentication Access control Data encryption External validation

#1 - Automatic Query Optimization
Zero Management
Fully managed with no knobs or tuning required
No indexes, distribution keys, partitioning,
vacuuming,…
Zero infrastructure costs
Zero admin costs

New #1 Fav – Data Sharing (The Data “Sharehouse”)
Data
Consumers
Data
Providers
No data movement
Share with unlimited
number of consumers
Live access
Data consumers
immediately see all updates
Ready to use
Consumers can immediately
start querying

Data Sharing - Configuring access
create share s1; --empty share
grant usage on database sales to share s1; -- add database
grant usage on schema sales.east to share s1; -- add schema
grant usage on view sales.east.accts to share s1; -- add view
alter share s1 add accounts=a1, a2, a3; -- add accounts
s1
Provider Database
Data Share Object
Schema List
View List
Account List
a3
a1
a2
Data provider Data Consumers
create database sales from share p1.s1;

Enabling Agile DW in the Cloud
Examples

•Agile Warehouse Scaling
• Separation of Workloads
• Virtual WH Scaling Techniques
•Agile Data Lifecycle
• Cloning
Enabling the Agile Data Warehouse

Agile Warehouse Scaling
Separation of Workloads

• DWaaS - Snowflake solution
• Assign each workload its own Virtual WH
• At a minimum, two WH: ETL and Business
• ETL can run continuously if it makes sense for the
business
• ETL / Business workload contention is eliminated
• Further subdivide business workloads into
own clusters as needed
• Eliminate contention between Sales, Marketing,
Finance, Data Science workloads
• International groups can operate clusters on their
own local time schedules
• Easy to add new workloads on demand!
Separation of Workloads
Virtual
Warehouse
Databases
Virtual
Warehouse
ETL & Data Loading
Business Workloads
Finance
Virtual
Warehouse
Test/Dev
Virtual
Warehouse
S
Marketing
Virtual
Warehouse
Sales
Virtual
Warehouse
S
Research

• Increase cluster size – on the fly!
• More data being analyzed
• More complex queries
• Get some concurrency boost
• No need to start, stop, or reboot!
• Automatic scale out!
• Multi-cluster warehouse concurrency
• Workload queries have usual weight
• But more of them, e.g. 20 dashboard users rather than the
usual 5
• Automatically scales back after the load
drops
Other Agile Warehouse Scaling Techniques

• Anticipated surges
• Explicitly increase WH nodes (T-shirt size) when expecting more data
• Explicitly increase MCWH minimum clusters when expecting more queries
• Can do both at once with ALTER WAREHOUSE
• Use cron or other scheduling/orchestration tool
• Unanticipated surges
• Rely on MCWH maximum clusters for some extra headroom
• Maximize Business Agility!
• Responsiveness for users
• Throughput and value extracted from variable compute power
• Minimize
• Cost and administrative overhead
Agile Warehouse Scaling – Best Practices

Agile Data Lifecycle

• Separation of Workloads
• Individual virtual warehouse for each dev/test/prod functional area
• CLONE for dev/test or Data Science Sandbox – on demand!
• Full logical copy of the data, but uses no extra storage
• Test/dev operations against clone have no effect on original data
• Security
• RBAC limits dev/test access to clone and not production data
• Secure Views permit role- or user-based obfuscation / masking / projection
• Business Impact – better quality code
• Dev and test teams are working on data at scale, see true app performance
• Full range of values means fewer surprises when app encounters live data

• Create development (DEV) and integration (INT) databases from production
(PROD)
Scenario 1
PROD
PUBLIC
Table
A
Table B
INT
PUBLIC
Table
A
Table B
DEV
PUBLIC
Table
A
Table B
CLONE
CLONE
CREATE OR REPLACE DATABASE DEV
CLONE PROD
CREATE OR REPLACE DATABASE INT
CLONE PROD

• Create two new tables, C and D, in the development (DEV) database
Scenario 2: new development
PROD
PUBLIC
Table
A
Table B
INT
PUBLIC
Table
A
Table B
DEV
PUBLIC
Table
A
Table B
Table
C
Table
D

• Mini-release: promote table C for integration testing
PROD
PUBLIC
Table
A
Table B
INT
PUBLIC
Table
A
Table B
DEV
PUBLIC
Table
A
Table B
Table
C
Table
D
Table
C
CREATE
TABLE
LIKE

• Deploy to production: promote table C to PROD database
PROD
PUBLIC
Table
A
Table B
INT
PUBLIC
Table
A
Table B
DEV
PUBLIC
Table
A
Table B
Table
C
Table
D
Table
C
CREATE
TABLE
LIKE
Table
C

• Refresh dev: get latest PROD data into DEV and INT
PROD
PUBLIC
Table
A
Table B
INT
PUBLIC
Table
A
Table B
DEV
PUBLIC
Table
A
Table B
Table
C
Table
D
Table
C
Table
C
CLONE
DEV
PUBLIC
Table
A
Table B
Table
C
Table
D
DEV2
CLONE
CREATE OR REPLACE
TABLE Dev2.TableD
CLONE Dev.TableD
CREATE OR REPLACE
DATABASE DEV2
CLONE PROD

• CLONE for Data Scientists
• Quick and Safe sandbox for discovery and testing
• Experiment and explore, and write back!
• Test hypothisis and keep iterating – drop and reclone as needed
• No impact to production data
• Combine with own virtual warehouse for complete isolation
• Business Impact – better data science
• More fine-grained data over longer time intervals
• Deeper insights, better forecasting, more monetizable results
• CLONE for Compliance
• Monthly, quarterly, annual clones – financial reporting, auditing requirements
• Business Impact – simpler compliance
• Your "backups" are live and immediately available

Snowflake & Spark

Where Snowflake fits with complementary solutions
Vast majority, >> 80% or
more, is semi-/structured
>> 20% or less is unstructured
Data
Sources
for
Analytics
(Audio, video, text, etc.)
(Tables, CSV, JSON, Avro,
Parquet, XML, flat files, etc.)
Complementary
Special data processing
(Spark ML, Hadoop lakes, NoSQL
front-ends, In-memory DBs,
non-relational exploring, et al.)
Built for the cloud, relational
data warehouse/DB & analytics
delivered as a service
52
Use Cases
(Tables, metadata)
• Live analytics
• Operational DW
• Real-time dashboards
• Continuous data loading
• Change data capture
• Traditional DW/BI
• Consolidation of data silos
• Relational data exploration
• Interactive queries
• Data science/statistics (Python, R)
• And more
• Spark machine learning
• Unstructureddata mining
• Streaming analytics
• Non-relational data exploration
• Non-relational data science
• And more

Modern Data Architectures
Data Production Data Lake Data
Consumption
Web
Servers
Data
Bases
App
Metrics
Snowflake
Business
Intelligence
Data Science:
Exploration
Data Science:
Machine Learning
Model Training
Data
Augmentation &
Loading
Data
Augmentation &
Loading
Model-driven
Apps
Online
Recommendations
& Behavioral
Targeting on Web
Pages
Offline Churn
Prediction
Other Machine
Learning Apps

Modern Data Architectures:
Data Loading & Augmentation
Web
Servers
App
Metrics
Data
Bases
Data ProductionCustomers &
End Users
External Data
Augmentation
Spark
Cluster
Kafka
SnowSQL
COPY
Kinesis
Transformations and
Augmentations in e.g.
Python, Scala,
SparkSQL, Spark
Streaming etc.
Snowflake
Warehouse
Snowflake
Spark
Connector
Full/Pruned
Data Lake with
Augmentation
Snowflake
Spark
Connector
Source/Raw
Data
Augmented
Data
Source/Raw
Data
Augmented
Data
Transformations and
Augmentations using Snowflake
SQL
Augmentation
Augmentation
CDC -
FiveTran

Modern Data Architectures: Data Science
Jupyter
Notebook
using e.g.
ML
Data Exploration
Model Design
Spark app
using
Spark ML
Model
(Re-)Training
Operationalized
Model (Code + Params)
External Data
Augmentation
Spark
Cluster
Snowflake
Warehouse
Snowflake
Spark
Connector
Data Lake with
Augmentation
Snowflake
Spark
Connector
Source/Raw
Data
Augmented
Data
Source/Raw
Data
Augmented
Data
Augmentation
Augmentation
Data Exploration
using Snowflake SQL

Snowflake WarehouseSpark Cluster (e.g. in AWS EMR)
AWS S3
3 2
Data Flow:
Data Frames
Data Flow:
Snowflake Tables
Master Node
Slave Nodes
Query
(SQL)
Cloud Services
Virtual Warehouse
1
Architecture: Data Interchange (Read)

Snowflake WarehouseSpark Cluster (e.g. in AWS EMR)
AWS S3
1 3
Data Flow:
Data Frames
Data Flow:
Snowflake Tables
Master Node
Slave Nodes
ControlFlow
(SQL)
Cloud Services
Virtual Warehouse
2
Architecture: Data Interchange (Write)

Spark Cluster
Snowflake Virtual Warehouse
Super-Charge Spark Processing with Snowflake
• Spark optimizer extension
automatically identifies Spark
operations with corresponding
Snowflake implementations
• Spark connector pushes these
operations into Snowflake SQL
• Pushed operations include: project,
filter, join, aggregation, limit
• Optimized Snowflake SQL applied to
all Spark data frames backed by
Snowflake tables
• Transparent to Spark language choice:
No need to rewrite Spark (Python,
Scala, Java, …) or SparkSQL code
df1 = spark.load…(‘T1’)
df2 = spark.load…(‘T2’)
df1
.join(df2, ‘id’)
.groupBy(‘region’)
.count()
.collect()
SELECT region, count(*)
FROM T1
JOIN T2
ON T1.id = T2.id
GROUPBY region
Snowflake SQL
AWS
S3
Results

• Reduced complexity, better manageability
• Same data store for curated and raw data
• Same data store for data science and BI data sets
• Fully transactional data store even for raw data when needed
• Performance benefits
• Leveraging structure of the data even in semi-structured data formats such as JSON as compared to flat file
storage
• Metadata for pruning based on queries
• Easy to integrate with Spark as compute and application platform
• Overall compute capacity and cost savings due to performance benefits with
Snowflake
• Blog posts:
• https://www.snowflake.net/snowflake-and-spark-part-1-why-spark/
• https://www.snowflake.net/snowflake-spark-part-2-pushing-query-processing/
Snowflake + Spark Benefits

NEW
Continous Data Loading with Snowpipe

Data is created constantly.Data accumulated over time.
Data is Being Generated Faster than Ever
Before
Infrequent
Now
Constant
Web
data
Corp
data
App
data
There’s an enormous opportunity to use
continuously generated data in analysis.

Snowpipe is an automated service that
asynchronously listens for new data as it arrives in
Amazon Web Services’ S3 cloud storage service
and continuously loads that data into Snowflake.
© 2017 Snowflake Computing Inc. All Rights Reserved. Snowflake
Confidential – under NDA only.

6
Data Ingress Approaches & Snowpipe Support
Approach Approach Definition Snowpipe
Batch Data accumulates over time (hours,
days) and is then loaded periodically
Option 1:
Point at an S3 bucket and a destination table in
your warehouse at which point new data is
automatically uploaded.
Option 2:
Technical resources can interface directly with the
programmatic REST API along with Java and Python
SDKs to enable highly customized loading use cases
Micro-batch Data accumulates over small time
windows (minutes) and is then loaded
Continuous Every data item is loaded individually as
it arrives

Snowpipe Scenario: Automatic Loading from S3
Snowflake
Database
External
S3
SnowPipe Service
Server-less
Loader
S3 notification
File data

Snowpipe – Current Preview
Snowflake
Database
REST Call
{file names}
S3
Application
SnowPipe Service
REST Endpoint
Server-less
Loader

Snowpipe Auto-Ingest: Fully Automatic Loading from S3
Snowflake
Database
External
S3
SnowPipe Service
Server-less
Loader
S3 notification
File data
Use simple Snowflake DDL statements to configure bucket for automatic load with Snowpipe
See the video here: https://www.youtube.com/embed/rwZWmcy0aBU?autoplay=1&rel=0
Blog posts:
https://www.snowflake.net/your-first-steps-with-snowpipe/
https://www.snowflake.net/snowpipe-serverless-loading-for-streaming-data-2/

Key Snowpipe Benefits
You only pay
for the compute
time you use to
load data
Avoid repeated
manual COPY
commands
Continuously
generated data is
available for
analysis in seconds
0 management. No
indexing, tuning,
partitioning or
vacuuming on load
Full support for
semi-structured
data on load
Server-less: No
servers to manage or
concurrency to worry
about.

• Server-less billing model: utilization-
based billing
• No warehouse to manage for load
• Per core, per second granularity
• Charged as Snowflake credits
• New line item on Snowflake bill
• Components of the Snowpipe
charges:
• Loading work – idle times are not charged
• Time spent in metadata management for file
loading
6
Pricing

Snowflake in Action Today

What customers are doing with Snowflake
DATA MARTS
& EXTRACTS
Market research
company consolidated
data marts to reduce
costs and data silos
Gaming company
replaced Hadoop +
SQL database with
Snowflake
STAGING
DATA LAKE
DATA
WAREHOUSE
Consumer retailer
modernizing DW by
replacing legacy
appliance with
Snowflake
Mobile analytics
company shares live
data with clients
REPORTING, ANALYTICS
& APPLICATIONS
DATA SOURCES

Delivering compelling results
Simpler data pipeline
Replace noSQL database with Snowflake
for storing & transforming JSON event
data
Faster analytics
Replace on-premises data warehouse with
Snowflake for analytics workload
Significantly lower cost
Improved performance while adding new
workloads--at a fraction of the cost
noSQL data base:
8 hours to prepare data
Snowflake:
1.5 minutes
Data warehouse appliance:
20+ hours
Snowflake:
45 minutes
Data warehouse appliance:
$5M + to expand
Snowflake:
added 2 new workloads for $50K

Over 1000 customers demonstrating what’s possible
Up to 200x faster reports that enable analysts to make
decisions in minutes rather than days
Ability to load and update data in near real time by replacing
legacy data warehouse + Hadoop cluster
New applications that provide secure access to analytics to
11,000+ pharmacies

Chosen by leading enterprises
Built advertising analytics
platform on Snowflake
Moving enterprise reporting to
Snowflake
Deploying BI solution stack on
Snowflake
Built new embedded analytics
application on Snowflake
Moved audience reporting
infrastructure to Snowflake
Replaced Hadoop + legacy data
warehouse with Snowflake

Ranked #1 Cloud Data Warehouse!
“Snowflake Hits All the Marks” – Gigaom
4,85
4,50
4,45
3,75
3,75
3,35
3,20
3,15
2,60
Cloud
Analytics
Database
Distruption Vectors
AWS Redshift
Oracle Database Exdata Cloud Service
SAP HANA Cloud Platform
Azure Data Warehouse
Vertica
DashDB (IBM)
Teradata
Google Big Query
Snowflake
RobustnessofSQL15%
Built-inOptimization15%
On-the-flyElasticity25%
DynamicEnvironment
Adaption20%
SeparationofCompute
fromstorage15%
SupportforDiversedata
10%
Score
“You can tell the data warehouse
pedigree from the development…
With superior performance and
the most hands-off model of
ownership, Snowflake is the
epitome of data warehouse as a
service. The model, cost, features
and scalability have already
caused some to postpone
Hadoop adoption.”
William McKnight
Gigaom
Disruption
Vectors
Gigaom Analyst Report: Sector Roadmap: Cloud Analytic
Databases 2017
Read the full report on snowflake.net

What does a Cloud-native
DWaaS Provide?
Cost effective storage and analysis of
GBs, TBs, or even PB’s
Lightning fast query performance
Continuous data loading without
impacting query performance
Unlimited user concurrency
Full SQL relational support of both
structured and semi-structured data
Support for the tools and languages you
already useODBC WEB UIJDBCJava
>_
Scripting

Big Data does not have to
equal Big Effort
Web ERP3rd party
apps
Enterprise
apps
IoTMobile

Discover the performance, concurrency, and simplicity of
Snowflake
As easy as 1-2-3!
01 Visit Snowflake.net
02 Click “Try for Free”
03 Sign up & register
Snowflake is the only data warehouse built for the
cloud. You can automatically scale compute up,
out, or down—independent of storage. Plus, you
have the power of a complete SQL database, with
zero management, that can grow with you to
support all of your data and all of your users. With
Snowflake On Demand™, pay only for what you
use.
Sign up and receive
$400 worth of free
usage for 30 days!

Kent Graziano
Snowflake Computing
Kent.graziano@snowflake.net
On Twitter @KentGraziano
More info at
http://snowflake.net
Visit my blog at
http://kentgraziano.com
Contact Information

YOUR DATA, NO LIMITS
Thank You!

Demystifying Data Warehousing as a Service - DFW

Recommended

More Related Content

What's hot (20)

Similar to Demystifying Data Warehousing as a Service - DFW (20)

More from Kent Graziano (20)

Recently uploaded (20)

Demystifying Data Warehousing as a Service - DFW