Zero to Snowflake Presentation

© 2018 Snowflake Computing Inc. All Rights
Reserved.
August 24, 2018© Snowflake Computing Inc. All Rights Reserved
ZERO TO
SNOWFLAKE IN
90 MINUTES
in partnership with

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• Download Materials @ https://tinyurl.com/yy9frfuw (Hotmail / outlook / o365)
• Download Worksheets and unzip
• Partner Introduction
• Snowflake Introduction
• Data Warehousing Today
• What Users do with Snowflake
• Hands on Snowflake
• Data loading- sample data set is shared
• Integrations
• Multi-clustering
• And more!
• Scaling/Workload Isolation
• Snowflake in the Real World- Data Sharing and Customer Example
• Conclusion
AGENDA

© 2018DMICONFIDENTIAL&PROPRIETARY
3
DATA & ANALYTICS
Snowflake
Solution Integration
Partner
Creating an intelligent existence by linking physical
& digital worlds to unleash the power of connectivity
〉 DATA PLATFORM SOLUTIONS
〉 MODERN DATA SOLUTIONS
〉 VISUAL SOLUTIONS
〉 ADVANCED ANALYTICS
〉 ENTERPRISE DATA STRATEGY
〉 EXECUTIVE ADVISORY
〉 ANALYTICS AS A SERVICE
〉 AGILE ANALYTICS

© 2018DMICONFIDENTIAL&PROPRIETARY
4
DATA & ANALYTICS
PARTNERSHIPS
***8 MOST IMPORTANT AI AND ANALYTIC TRENDS OF 2019 EBOOK AND MORE INFO ABOUT DMI @ WWW.DMINC.COM/EBOOK M
Today’s Moderators
〉 Brett VanderPlaats
Sr. Data Architect, Data Platform & Analytics
〉 David Mellinger
Practice Lead, Data Platform & Analytics

1,500+ CUSTOMERS
Building new analytic applications
Delivered new analytic application to
pharmacies using Snowflake
Moving to the cloud
Using Snowflake to move data analytics
to the cloud
Modernizing data platforms
Replaced data warehouse appliance +
Hadoop with Snowflake
Accelerating enterprise BI and analytics
Moved from legacy data warehouse
systems (appliance & cloud) to
Snowflake
© 2018 Snowflake Computing Inc. All Rights Reserved. 5

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What Are Users Doing Today?

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August 24, 2018© Snowflake Computing Inc. All Rights ReservedAugust 24, 2018© Snowflake Computing Inc. All Rights Reserved
What would you like in your Data Warehouse?
8
Complete
SQL Database
Zero
Management
All of
Your
Data
All of
Your
Users
Pay Only for
What You
Use
Live Data
Sharing

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What Are Users Doing with Snowflake?

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What Are User Doing with Snowflake?

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Snowflake’s Differentiating Architecture

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SNOWFLAKE’S MULTI-CLUSTER, SHARED DATA ARCHITECTURE
Centralized storage
Instant, automatic scalability & elasticity
Service
Compute
Storage

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Why Scaling Compute Saves $$$$$$

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How Does Snowflake Fit?
ADVANCED
ANALYTICS
INTEGRATION
TOOLS
ELT
Stream

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Scale compute and concurrency
ADF

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Scale Up (Response Time)
Queries that are:
--Large, Complex, many Calculations
Scale Out
(Throuhput)
Many users
or processes
concurrently
querying
S M L XL 2XXS 3X 4X
XS
XS
XS
XS
XS
XS
XS
XS
XS
L L
2X
2X2X2X
4X
4X
4X

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Differences from other EDW vendors

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Business Implications for Organizations
• Native ANSI-SQL database for leveraging existing skills
• Reducing expensive retraining
• Interoperability with existing tools (Power BI, Tableau, and others have SF
connectors)
• Simplified Migration
• Support/Documentation on Snowflake – Take a look at

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But Wait….
…There’s

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DEV OPS?

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Data Protection w/o Restore?

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Data Sharing (Monetization)
Disaster Recovery
***Platform (AZURE AWS) in 2019

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Native Support for
structured and semi-
structured data

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Cross Region Data Replication

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Comprehensive data protection

BIO-BREAK
THEN
LET’S DIVE INTO
SNOWFLAKE!
https://tinyurl.com/yy9frfuw

Module 1
Snowflake DB/WH/Object
Configuration

Module 2
Queries and Performance

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Azure Storage

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Performance through Caching
Azure Storage

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Query
Profiler•Processing — time spent on data processing by the CPU.
•Local Disk IO — time when the processing was blocked by local disk access.
•Remote Disk IO — time when the processing was blocked by remote disk access.
•Network Communication — time when the processing was waiting for the network data
transfer.
•Synchronization — various synchronization activities between participating processes.
•Initialization — time spent setting up the query processing.
•IO — information about the input-output operations performed during the query:
• Scan progress — the percentage of data scanned for a given table so far.
• Bytes scanned — the number of bytes scanned so far.
• Percentage scanned from cache — the percentage of data scanned from the local disk
cache.
• Bytes written — bytes written (e.g. when loading into a table).
• Bytes written to result — bytes written to a result object.
• Bytes read from result — bytes read from a result object.
• External bytes scanned — bytes read from an external object, e.g. a stage.
•DML — statistics for Data Manipulation Language (DML) queries:
• Number of rows inserted — number of rows inserted into a table (or tables).
• Number of rows updated — number of rows updated in a table.
• Number of rows deleted — number of rows deleted from a table.
• Number of rows unloaded — number of rows unloaded during data export.
• Number of bytes deleted — number of bytes deleted from a table.
•Pruning — information on the effects of table pruning:
• Partitions scanned — number of partitions scanned so far.
• Partitions total — total number of partitions in a given table.
•Spilling — information about disk usage for operations where intermediate results do not fit in memory:
• Bytes spilled to local storage — volume of data spilled to local disk.
• Bytes spilled to remote storage — volume of data spilled to remote disk.
•Network — network communication:
• Bytes sent over the network — amount of data sent over the network.
•EXECUTION TIME
•STATISTICS

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Multi-cluster Warehouse

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Micropartion
s
&
Pruning

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Enterprise Grade Security

Module 3
Unstructured Data

Module 4
Loading Data

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• 4 objects for data loading
1) Source
2) Warehouse
3) Database
4) File Format (default CSV)
• 100 mb file is optimum size

Module 5
Cloning / Time Travel

THANK YOU

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USE CASES FOR SNOWFLAKE DATA SHARING
• Nielsen is a global information, data, and
measurement company
• Nielsen knows “What People Watch,
Listen To, and Buy”
• Nielsen Marketing Cloud includes eXelate
DMP which provides unified consumer
profiles
• Nielsen sells selective slices of their DMP
data available to advertisers for particular
marketing campaigns
• Nielsen plans to use data sharing for
making detailed datasets available to
subscribers
• Lower friction, lower cost solution
• Scalable operations

61© 2016 Snowflake Computing Inc. All Rights
Reserved.
CUSTOMER EXAMPLE: BLACKBOARD
Jay White
Director, Software Engineering
Scenario
Provide and perfect over 14 different data
products that help universities facilitate
learning online
Pain Points
Disparate data
Challenges integrating data
Semi-structured data
Solution
Replace existing Hadoop and RDBMS
system with Snowflake
Everything that we did left our jaw on the
table. ‘Wait – we’ve never done anything like
that.’ Or, ‘How did that just run so fast.’ We
are getting 16x performance from
Snowflake.

62© 2016 Snowflake Computing Inc. All Rights
Reserved.
A NEW DATA PIPELINE FOR BLACKBOARD
Snowflake
S
3
Student data
Mobile data
Collaborative data
Intelsuite data
Kafka
Airflow for data orchestration Looker for internal dashboards
Learning Management System
Blackboard Predict
R Prediction Engine
Unified data
Simplified data transformation
Existing tools integrate seamlessly
• 16x performance improvement over SQL
• 1 PB by the end of 2017

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Built-in disaster recover and high availabity

Zero to Snowflake Presentation

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