Snowflake’s Cloud Data Platform and Modern Analytics

Snowflake’s Cloud Data Platform
and Modern Analytics
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Agenda
• Introductions
• Snowflake
• Senturus overview
• Additional resources
• Q&A
4

5
Introductions
Chris Richardson
Sales Engineer
Snowflake
Michael Weinhauer
Director of Training and Content
Senturus, Inc.
5

© 2020 Snowflake Inc. All Rights Reserved
INTRODUCTION
TO SNOWFLAKE
Snowflake Cloud Data Platform Overview Training
Chris Richardson - Sr. Sales Engineer

AGENDA
● Snowflake Overview
● Snowflake Demo
● Architecture Deep Dive
● Q&A

Introduction to
Snowflake

OUR STORY
10
Founded 2012
by industry
veterans
Over 3000
active customers
Over $950M in
venture funding from
leading investors
First customers 2014,
general availability 2015
Gartner and
Forrester “Leader”
Avg # queries
processed per day:
> 72 million
Largest single
table:
> 68 trillion rows
Single customer
most data:
> 7 PB
Single customer
most concurrent users:
> 400
Single customer
most users:
> 10,000
SNOWFLAKE FUN FACTS

COMMON CHALLENGES
11
Siloed, Diverse
Data Increases Security
and Governance
Exposure
Scale and Speed Issues
Limit Timely and
Accurate Business
Decisions
Complex, Costly
Infrastructure Slows
Innovation

Data
Engineering
TRADITIONAL DATA ARCHITECTURE
Complex, Costly & Constrained
Data
Monetization
Operational
Reporting
Ad Hoc
Analysis
Real-time
Analytics
Data Consumers
OLTP
Databases
Enterprise
Applications
Third-Party
Web/Log
Data
IoT
Data Sources
Backup
EDW
Data Lake
Operational
Data Store
Data Marts
Shared Data
ETL
Third Party

Data
Engineering
Data
Monetization
Operational
Reporting
Ad Hoc
Analysis
Real-time
Analytics
Data Consumers
OLTP
Databases
Enterprise
Applications
Third-Party
Web/Log
Data
IoT
Data Sources
Backup
EDW
Data Lake
ETL
Unable to
ingest data
at scale.
Operational
Data Store
Data Marts
Shared Data
Third Party

Data
Engineering
Data
Monetization
Operational
Reporting
Ad Hoc
Analysis
Real-time
Analytics
Data Consumers
OLTP
Databases
Enterprise
Applications
Third-Party
Web/Log
Data
IoT
Data Sources
Backup
EDW
Data Lake
ETL
Integration
is difficult
and time
consuming.
Operational
Data Store
Data Marts
Shared Data
Third Party

Data
Engineering
Data
Monetization
Operational
Reporting
Ad Hoc
Analysis
Real-time
Analytics
Data Consumers
OLTP
Databases
Enterprise
Applications
Third-Party
Web/Log
Data
IoT
Data Sources
Backup
EDW
Data Lake
ETL
Limited
performance &
concurrency.
Operational
Data Store
Data Marts
Shared Data
Third Party

Data
Engineering
Data
Monetization
Operational
Reporting
Ad Hoc
Analysis
Real-time
Analytics
Data Consumers
OLTP
Databases
Enterprise
Applications
Third-Party
Web/Log
Data
IoT
Data Sources
Backup
EDW
Data Lake
ETL
Governance and
security risks from
managing multiple
copies of data.
Operational
Data Store
Data Marts
Shared Data
Third Party

Data
Engineering
Data
Monetization
Operational
Reporting
Ad Hoc
Analysis
Real-time
Analytics
Data Consumers
OLTP
Databases
Enterprise
Applications
Third-Party
Web/Log
Data
IoT
Data Sources
Backup
EDW
Data Lake
ETL
On-premises,
or limited to a
single cloud.
Operational
Data Store
Data Marts
Shared Data
Third Party

MODERN DATA ARCHITECTURE WITH
SNOWFLAKE CLOUD DATA PLATFORM
Data
Monetization
Operational
Reporting
Ad Hoc
Analysis
Real-time
Analytics
OLTP
Databases
Enterprise
Applications
Third-Party
Web/Log
Data
IoT
Data Sources Data Consumers
ETL,Streaming
Data
Warehouse
Data
Lake
Data
Engineering
Data
Exchange
Data
Apps
Data
Science

VALUE OF A CLOUD DATA PLATFORM
Multi-Cluster, Shared
Data Architecture
Secure & Governed
Access to all Data
Near-Zero
Maintenance, as a
Service
One Platform, One
Copy of Data, Many
Workloads

SNOWFLAKE IS A FULLY MANAGED
DATA PLATFORM AS-A-SERVICE
20
Dynamic three-layer service oriented
architecture fully managed by
Snowflake
● Cloud Services are a collection
of independent, scalable, fault-
tolerant stateless services
● Virtual Warehouses are elastic
compute engines that handle the
execution of customer queries
● Storage layer is highly
optimized hybrid columnar
format
Cache Cache Cache Cache
Cloud
Services
Authentication & Access Control
Infrastructure
management
Query planning
& optimization
Metadata
management
Security
Virtual
Warehouses
Database
Storage

© 2018 Snowflake Computing Inc. All Rights Reserved.
M…
Automatic multi-cluster warehouse
Cloud Services
Infrastructure management
Query planning & optimization
Metadata management
Security
Transactional control
Data
Science
Sales
AWS QuickSight
External Data
Consumers
Data
sharing
XS
M
M
Structured &
semi-structured
ETL/ELT
AWS
Glue
Snowpipe
= Snowflake Virtual Warehouse
= Data in Snowflake Account
SXL
On-demand elasticity
Database
clone
Prod/Dev
S
L
Finance
Data protection &
Time Travel

Snowflake Demo

ABOUT CITIBIKE NYC
23
https://www.citibikenyc.com

CITIBIKE SCHEMA
● TRIPS: 76M records, each record represents a single rider trip on the New York City Citibike bike share program.
● WEATHER: 82M weather observations records in JSON format in a variant column.
● STATIONS: 980 records, contains data for the bike stations where trips begin and end.
● PROGRAMS: 61 records with data about the various membership programs that rides are taken under
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SNOWFLAKE DEMO
● Introduction to Snowflake
● Load + Query Structured Data
● Load + Query Semi-Structured Data (JSON)
● Data Sharing
● Cloning & Time Travel
25

A deep dive into
Snowflake Architecture

A NEW ARCHITECTURE FOR DATA
WAREHOUSING
Multi-cluster, shared data, in the cloud
27
Traditional Architectures Snowflake
Additional capacity requires
forklift upgrade
Reads/Writes at the same time
cripples the system
Replication requires
additional hardware
Shared-disk
Resizing cluster requires redistributing
data. Shut down requires unloading
Each cluster requires its own copy of data
(ex: test/dev, HA)
Vacuuming processes needed to maintain
sort and distribution for performance
Shared-nothing Multi-cluster, shared data
• Centralized, scale-out storage that
expands and contracts automatically
• Independent compute clusters can read/write at
the same time and resize instantly
• Backed by eleven 9’s of durability SLA by
underlying cloud providers

SNOWFLAKE ARCHITECTURE:
STORAGE AND COMPUTE
Storage separated from compute
Automatically grows without adding nodes.
Never run out of space.
Resize compute instantly
Scale up/down depending on the business
needs right now or turn off when not in use.
Multiple clusters access data
without contention
ETL, reporting, data science, and
applications all running at the same time
without performance impact.
28

SNOWFLAKE ARCHITECTURE:
GLOBAL SERVICES
Centralized management
Separate metadata from storage
and compute
As soon as data commits, each cluster sees the newest version
Full transactional consistency
across entire system (ACID)
Management Optimization Security Availability Transactions Metadata
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A DEEPER LOOK
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Storage decoupled
from compute
All data in one place
Dynamically combine
storage and compute
JDBC/ODBC
Cache Cache Cache Cache
Cloud
services
Authentication & Access Control
Infrastructure
manager
Optimizer Metadata
manager
Security
Virtual
warehouses
Database
storage
VPC/VNet
A
I
Q
B
J
R
C
K
S
D
L
T
E
M
U
F
N
V
G
O
W
H
P
X
A`
E`
B`
F`
C`
G`
D`
H`

SEPARATE COMPUTE, SAME DATA
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Data science
ETL
Dev/QA
BI/Visualization
(Auto scaling)
Elastic scaling for storage
Low-cost cloud storage, fully replicated and resilient
Elastic scaling for compute
Virtual warehouses scale up & down instantly without
downtime to support workload needs
Dedicated performance SLAs
Each warehouse can access the same tables at the same
time without performance penalty (including ETL)
Test/Dev/Staging/QA
Reference objects in multiple databases with
one SQL statement
Elastic scaling for concurrency
Auto-scaling maintains constant query performance

CAN YOU HANDLE THE 9AM “RUSH HOUR”?
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Provides consistent SLAs and performance
no matter how many users/applications are
accessing the system
Single virtual warehouse
of multiple compute clusters
Automatically scales up and down
transparently depending on changing concurrency
Clusters automatically paused and resumed
to maximize concurrency while minimizing cost
Data science
ETL
BI/Visualization
(Auto scaling)
Dev/QA
BI/Visualization
(Auto scaling)

ADAPTIVE CACHING
Data
Active working set transparently cached
on virtual warehouse SSD
Metadata
Metadata cached for fast access
during query planning
Query results
Results sets cached for reuse
without requiring compute
(e.g., static dashboard queries)
33
Virtual Warehouse(s)
Database Storage
Cloud Services

RELATIONAL DATABASE EXTENDED
TO SEMI-STRUCTURED DATA
34
> SELECT … FROM
…
Semi-structured data
(JSON, Avro, XML, Parquet, ORC)
Structured data
(e.g., CSV, TSV, …)
Storage optimization
Transparent discovery and storage optimization
of repeated elements
Query optimization
Full database optimization for queries
on semi-structured data
+

A BETTER WAY TO SHARE DATA
35
Data Providers Data Consumers
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

SNOWFLAKE SECURE DATA SHARING
A simple, instant and secure way for users to share data
Share data without
moving or copying
Without complex
reconstruction
In a secure, governed,
resilient environment
With full database
capabilities

COMPREHENSIVE DATA PROTECTION
37
Protection against infrastructure failures
All data transparently & synchronously replicated 3+ ways
across independent infrastructure
Long-term data protection
Zero-copy clones + optional export to cloud object storage
enable user-managed data copies
Protection against corruption & user errors
“Time travel” feature enables instant roll-back to any point
in time during chosen retention window

BUILT-IN AVAILABILITY
38
Scale-out of all tiers:
metadata, compute, storage
Resiliency across
redundant independent
infrastructure
backed by cloud provider SLAs
separate power supplies
built for synchronous replication
Fully online updates
& patches
zero downtime
Fully managed
by Snowflake
Cloud
service
s
Virtual
warehouse
s
Database
storage
Service
s
Metadata
Data center Data center Data center

“TIME TRAVEL” FOR DATA
39
Previous versions of data
automatically retained
Retention period selected by customer
Accessed via SQL extensions
AS OF for selection
CLONE to recreate
UNDROP recovers from accidental deletionNew data Modified data
T0 T1 T2
> SELECT * FROM mytable AS OF T0
> SELECT * FROM mytable AS OF T1

ZERO-COPY DATA CLONING
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Instant data cloning operations
Databases, schema, tables, etc
Metadata-only operation
Modified data stored as new blocks
Unmodified data stored only once
No data copying required, no cost!
Instant test/dev environments
Test code on your entire production dataset
Swap tables into production when ready

Snowflake Value
Proposition

FAST ELASTICITY
Pay for only what you use, at cloud economies of scale
42
Pay only for what you use Cloud economies of scale
$10,000
TB/yr
$1000
TB/yr
$360
TB/yr
Snowflake
Cloud data
warehouse
On-prem
Traditional databases
are inflexible
Snowflake uses the
cloud to enable elasticity
Usage
Varies
Pay for only what
you use with no
overprovisioning
Eliminate
overbuy
Scale compute
up and down,
transparently and
automatically
No need for capacity
planning, make
capacity decisions
on the fly

ZERO MANAGEMENT
Load your data, run queries… we do the rest!
43
Category Manual Task On-premises Cloud-Hosted
EDW
Snowflake
Infrastructure
Data center Customer Vendor Vendor
Hardware & software Customer Vendor Vendor
Scaling Customer Customer Vendor
Database
management & tuning
Index management Customer Customer Vendor
Data partitioning Customer Customer Vendor
Metadata & statistics maintenance Customer Customer Vendor
Query optimization Customer Customer Vendor
Data & service
protection
Failure recovery Customer Vendor Vendor
Disaster recovery Customer Customer Vendor
Data protection Customer Customer Vendor
Service monitoring & alerting Customer Customer Vendor
Security
Physical security Customer Vendor Vendor
Deployment security Customer Customer Vendor
Security monitoring Customer Customer Vendor

© 2019 - Snowflake Inc. All Rights Reserved
HOW SNOWFLAKE COMPARES
Snowflake
High performance across a
broad range of relational queries
High performance with native
JSON/semi-structured support
Scale up, down, or off,
without delay/disruption
Multi-warehouse/isolated
workloads against the same data
✓
✓
✓
✓
Unlimited query concurrency
✓
True SaaS service, admin-
free management
Live, secure, data sharing
✓
✓
Criteria
Multi-statement transactions
✓
On-premises
MPP
Yes
Limited
Limited
Yes
No
No
No
No
MPP
Migrated to
Cloud
Yes
Limited
Limited
Yes
No
No
No
No
Cloud
Query
Engines
Limited
Limited
Limited
No
No
Limited
Yes
No
Why
Important?
Delivers faster
business results
Enables 360o views, multi-
channel data analytics
Eliminates queueing
delays
Pipeline and transactional
integrity
Productive teams, w/o
interference
Quickly matches resources to
business need w/o penalty
Simplifies environments, enables
higher strategic focus
Build an economy for data

TRADITIONAL PLATFORMS
Partitioning
Indexing
Ordering
Vacuuming
Statistic Collection
Memory Management
Parallelism
Query Plan Hinting
Workload Management
Initial Setup
Upgrading
Patching
Capacity
Planning
Storage
Security
Query TuningPhysical DesignInfrastructure
Manage Infrastructure, not data.
Availability &
Maintenance
Replication
Backups
Re-Clustering
Account Management

SNOWFLAKE CLOUD DATA PLATFORM
Partitioning
Indexing
Ordering
Vacuuming
Statistic Collection
Memory Management
Parallelism
Query Plan Hinting
Workload Management
Initial Setup
Upgrading
Patching
Capacity
Planning
Storage
Security
Query TuningPhysical DesignInfrastructure
Limited Administration
Availability &
Maintenance
Replication
Backups
Re-Clustering
Account Management
Simply load and query data

EVER EXPANDING ECOSYSTEM
Platform BI/Analytics ETL
Data Science Services

CTA
•title
•copy
•link
50

The authority in
Business Intelligence
Exclusively focused on BI,
Senturus is unrivaled in its
expertise across the BI stack.
51

Decisions & actionsBusiness needs
Bridging the data & decisioning gap
52
Analysis-ready data

Full spectrum BI services
• Dashboards, reporting and visualizations
• Data preparation and modern data warehousing
• Hybrid BI environments (migrations, security, etc.)
• Software to enable bimodal BI and platform migrations
• BI support retainer (expertise on demand)
• Training and mentoring
53

A long, strong history of success
• 19+ years
• 1300+ clients
• 2500+ projects
54

Expand your
knowledge
Find more resources
on the Senturus website:
senturus.com/senturus-resources
55

Upcoming events
•Connecting to and prepping data in Power BI
•Pros & cons of four different data connection scenarios
•Thursday, Mar. 12, 2020, 11am PT/2pm ET
•Cognos 11.1.6
•Live Q&A with IBM offering manager
•Thursday, Apr. 16, 2020, 11am PT/2pm ET
56

Complete BI training
57
Instructor-led online courses Self-paced learning
MentoringTailored group sessions

Additional resources
58
Insider viewpointsTechnical tipsUnbiased product reviews
Product demos Upcoming eventsMore on this subject

Q & A
•If your question or issue
is broader than what we
are able to answer today,
contact us at:
• info@senturus.com
•and we will set up a
free consultation.
59

© 2020 by Senturus, Inc. This presentation may not be reused or distributed without the written consent of Senturus, Inc.
www.senturus.com 888 601 6010 info@senturus.com
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Snowflake’s Cloud Data Platform and Modern Analytics

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