Snowflake Inc. · NYSE:SNOW
Snowflake — Snowflake Data for Breakfast Stockholm (March 2020)
88 slides from Snowflake's conference presentation, March 2020
Slide 1: Snowflake Data for Breakfast
Date per bree Heat $46 snowflake’
Slide 2: Snowflake: The Cloud Data Platform
SNOWFLAKE THE CLOUD DATA PLATFORM © 2020 Snowflake Inc. All Rights Reserved.
Slide 3: Data… The New Frontier
DATA…THE NEW FRONTIER © 2020 Snowflake Inc. All Rights Reserved.
Slide 4: New Technology Changes How We Use Data
NEW TECHNOLOGY CHANGES HOW WE USE DATA Diversification of Explosion of Data Rise of the Cloud Analytics Analytics is growing in IoT, mobile, and social Cloud gives us the importance, everywhere, open up new opportunities ability to scale and and for everyone for insight centralize data © 2020 Snowfl
Slide 5: Taking a Platform Approach Leads to Big Business Impact
TAKING A PLATFORM APPROACH LEADS TO BIG BUSINESS IMPACT Make Better, Quicker Reduce the Cost of Create a Great Customer Business Decisions Scaling Data Management Experience with Data and Analytics © 2020 Snowflake Inc. All Rights Reserved.
Slide 6: Journey to a Cloud Data Platform
JOURNEY TO A CLOUD DATA PLATFORM Value of Data Time On Premises Data Lake, 1st Gen Cloud Cloud Data EDW Hadoop EDW Platform All Data All Users Fast Answers SQL Database © 2020 Snowflake Inc. All Rights Reserved.
Slide 7: Traditional Data Architecture
TRADITIONAL DATA ARCHITECTURE Complex and Costly with Multiple Copies of Data Data Data Normalization Data Analytics Data Sources Integration Transformation & Aggregation Data Consumers OLTP Data Warehouses File Sharing Databases Operational Enterprise Reporting ETL ELT Applications Data Marts Data
Slide 8: Modern Data Architecture with Snowflake
MODERN DATA ARCHITECTURE WITH SNOWFLAKE Data Sources Data Consumers OLTP Data Data Data Data Data Data Data ETL, Streaming Databases Warehouse Lake Engineering Exchange Applications Science Monetization Enterprise Applications Operational Reporting Third-Party Ad Hoc Web/Log Analysis Data IoT Real-t
Slide 9: The Value of a Cloud Data Platform
THE VALUE OF A CLOUD DATA PLATFORM One Platform Unlimited One Copy of Data, Performance Many Workloads and Scale Secure & Near-zero Governed Access Maintenance, to All Data as a Service © 2020 Snowflake Inc. All Rights Reserved.
Slide 10: One Platform, One Copy of Data, Many Workloads
ONE PLATFORM, ONE COPY OF DATA, MANY WORKLOADS Data Warehouse Data Engineering Data Lake Modernize data Rethink transformation Simplify and accelerate warehousing to deliver with robust and integrated your data lake with one faster analytics at scale data pipelines platform for all your data Data Ap
Slide 11: The Impact of a Cloud Data Platform
THE IMPACT OF A CLOUD DATA PLATFORM Productivity Economics Scale Concurrency Performance Elasticity © 2020 Snowflake Inc. All Rights Reserved.
Slide 12: Situation / Value
SITUATION VALUE ● Shift to the cloud with focus ● 60+ analytics teams served on speed and value by single cloud data platform ● Managing infrastructure ● Migrated in < 90 days ● Significant governance, ● Load speeds improving 86% regulatory requirements ● 5x faster complex queries ● Improved governa
Slide 13: Situation / Value
SITUATION VALUE ● Multiple data warehouses, ● Diverse data ingested for some end-of-life analytics, data science ● Data silos caused ambiguity ● Improved agility, scalability and and reporting disparities analytics performance ● Poor consumer insight ● In depth customer knowledge and improved servic
Slide 14: Situation / Pain, Solution / Value
SITUATION / PAIN SOLUTION / VALUE ● Painfully slow analytics cycles ● Hundreds of newly empowered analysts ● Limited ability to answer complex questions ● Improved scalability and faster query times ● Inability to provide business continuity and ensure security ● Guaranteed security and data availab
Slide 15: Proven by Over 3000 Customers
PROVEN BY OVER 3000 CUSTOMERS © 2020 Snowflake Inc. All Rights Reserved.
Slide 16: Ever Expanding Ecosystem
Platform BI/Analytics ETL EVER EXPANDING ECOSYSTEM Data Science Services © 2020 Snowflake Inc. All Rights Reserved.
Slide 17: Technical Deep Dive
TECHNICAL DEEP DIVE © 2020 Snowflake Inc. All Rights Reserved.
Slide 18: Architecture
HOW IS SNOWFLAKE UNIQUE? ARCHITECTURE © 2020 Snowflake Inc. All Rights Reserved.
Slide 19: A Closer Look
A CLOSER LOOK Traditional Architectures Snowflake Shared-disk Shared-nothing Multi-cluster, shared data Additional capacity requires Resizing cluster requires • Centralized, scale-out storage that expands and forklift upgrade redistributing data. Shut down contracts automatically requires unloading.
Slide 20: Snowflake Architecture
SNOWFLAKE ARCHITECTURE Scale Out Services Multi-Cluster Compute Centralized Storage Cloud Agnostic Layer © 2020 Snowflake Inc. All Rights Reserved.
Slide 21: Modern Data Architecture with Snowflake Cloud Data Platform
MODERN DATA ARCHITECTURE WITH SNOWFLAKE CLOUD DATA PLATFORM Data Sources Data Consumers OLTP Data Data Data Data Data Data Data ETL, Streaming Databases Warehouse Lake Engineering Exchange Applications Science Monetization Enterprise Applications Operational Reporting Third-Party Ad Hoc Web/Log Anal
Slide 22: One Platform, One Copy of Data, Many Workloads
ONE PLATFORM, ONE COPY OF DATA, MANY WORKLOADS Data Warehouse Data Engineering Data Lake Modernize data Rethink transformation Simplify and accelerate warehousing to deliver with robust and integrated your data lake with one faster analytics at scale data pipelines platform for all your data Data Ap
Slide 23: L ETL/ELT Sales
L ETL/ELT Sales XS M Snowpipe S S Cloud Services Metadata Management Data Security Science Query Planning & Optimization M Transactional Control © 2020 Snowflake Inc. All Rights Reserved.
Slide 24: ETL/ELT Sales XS
ETL/ELT Sales XS M … M Multi-cluster Snowpipe XL S Cloud Services Metadata Management Data Security Science Query Planning & Optimization Transactional Control © 2020 Snowflake Inc. All Rights Reserved.
Slide 25: External M ETL/ELT
External M ETL/ELT Sales XS Data M … M Sharing Multi-cluster Snowpipe Structured & semi-structured Clone S XL XL Cloud Services Data protection & Metadata Management time travel Data Dev Ops Security Science Query Planning & Optimization L Transactional Control © 2020 Snowflake Inc. All Rights Reser
Slide 26: Secure by Design, Data Availability
SECURE BY DESIGN, DATA AVAILABILITY Authentication Access Data External Data Control Encryption Validation Availability • Embedded multi-factor • IP whitelisting • All data encrypted, • Certified against • User error: Time Travel, authentication always, end-to-end enterprise-class Failsafe, Cloning
Slide 27: More on Workloads
MORE ON WORKLOADS Data Data Data Data Lake Exchange Engineering Science © 2020 Snowflake Inc. All Rights Reserved.
Slide 28: How Does Scalable Cloud Data Platform Enable Data Lakes?
HOW DOES SCALABLE CLOUD DATA PLATFORM ENABLE DATA LAKES? Snowflake Data Lake Attributes Additional Benefits No silos – all data Multiple workloads Governance Global Open formats Cheap storage Transactional Performance Raw Representation Schema on read Data sharing Managed service © 2020 Snowflake In
Slide 29: Augmenting Existing Data Lakes
AUGMENTING EXISTING DATA LAKES Azure S3 Storage GCS Materialized SQL over Views materialized data New Files Notifications SQL over External Snowflake tables Hive Events Tables SQL over Hive external table Metastore Data Lake © 2020 Snowflake Inc. All Rights Reserved.
Slide 30: Low Latency Ingest
LOW LATENCY INGEST Web IoT External Stage Unload (S3, Azure Staging Mobile Blobs, GCS) Snowpipe Tables External Stage (S3, Azure Blobs, Enterprise GCS) Apps Data Sources © 2020 Snowflake Inc. All Rights Reserved.
Slide 31: Scalable Transactional Transformations
SCALABLE TRANSACTIONAL TRANSFORMATIONS Table Streams & Tasks Target Table 1 Unload Target Staging Transformations Table 2 External Stage Tables (S3, Azure Blobs, GCS) © 2020 Snowflake Inc. All Rights Reserved.
Slide 32: Secure Data Sharing
SECURE DATA SHARING Secure Live Frictionless Personalized Global © 2020 Snowflake Inc. All Rights Reserved.
Slide 33: Enabling AI, ML, and Data Science
ENABLING AI, ML, AND DATA SCIENCE • Improving data science speed and efficiency with centralized source of high performance data • Accelerating data exploration and preparation by 10-100x • Connectors to leading and emerging technologies • First class ecosystem of partners © 2020 Snowflake Inc. All
Slide 34: Speaker Q&A
SPEAKER Q&A © 2020 Snowflake Inc. All Rights Reserved.
Slide 35: Join Us After the Break
JOIN US AFTER THE BREAK © 2020 Snowflake Inc. All Rights Reserved.
Slide 36: Thank You
THANK YOU © 2020 Snowflake Inc. All Rights Reserved.
Slide 37: Our Journey to Snowflake
OUR JOURNEY TO SNOWFL AKE Paul Flynn & Jakob Matto
Slide 38: Presenters: Paul Flynn and Jakob Matto
H O O K U S U P – W E R E I N YO U R C O M M U N I T Y N OW pjflynn jakobmatto 2 0 2 0 - 0 3 - 0 6 2
Slide 39: Daniel Wellington Brand Story
iim olOkRY BEAIND On a trip halfway across the world, founder Filip Tysander met an intriguing British gentleman with impeccable yet unpretentious style. The man had a particular fondness for wearing his vintage watches on old, weathered NATO straps. Inspired by his new acquaintance, Filip created h
Slide 40: Today We Are
2020-03-06 TODAY* WE ARE ‘JANUARY 2019 IN 20 OFFICES WORLDWIDE STOCKHOLM, UPPSALA, HONG KONG, TOKYO, SINGAPORE, SEOUL, SYDNEY NEW YORK, MEXICO CITY, SHENZHEN SHANGHAI, LONDON, COLOGNE MILAN, MOSCOW, PARIS MUMBAI, CAPE TOWN, ISTANBUL, DUBAI
Slide 41: Global Presence
GLOBAL PRESENCE DEVELOPMENT OF ESTABLISHED MARKETS SINCE 2011,/ DW OFFICES WORLDWIDE @ ETABLISED MARKET @ OFFICES
Slide 42: pay
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Slide 43: Why Did We Need to Change Anything?
WHY DID WE NEED TO C HANGE ANY THING? 2 0 2 0 - 0 3 - 0 6 1 7
Slide 44: AWS
AW S • No legacy on-prem and born in the cloud • Redshift – Performance : scales as a set of servers, not as a service – Concurrency and table locking – Distribution, Vaccuming, Indexing, patching, uptime, environments – Ultimately Postgres is a conventional server database that wasn’t built for the
Slide 45: AWS Data Warehouse Architecture
AW S D AT A WA R E H O U S E A R C H I T E C T U R E 2 0 2 0 - 0 3 - 0 6 9
Slide 46: How Did We Do It?
HOW D I D WE D O I T ? 2 0 2 0 - 0 3 - 0 6 2 1 0
Slide 47: Snowflake MVP
S N OWFL AKE M V P • Test Snowflake, because curiosity is often free • Build your relationships in the community • Sell the vision • Utilize a business case and get an MVP built 2 0 2 0 - 0 3 - 0 6 1 1
Slide 48: Community – Meet Customers
COMMUNIT Y – MEET CUS TOMERS 2 0 2 0 - 0 3 - 0 6 1 2
Slide 49: What Did We Do?
W H AT D I D W E D O ? 2 0 2 0 - 0 3 - 0 6 3 1 3
Slide 50: Migrate - Phase 1
M I G R AT E - P H A S E 1 • Get across to Snowflake ASAP • Re-use what you can – Snowflake Import • Just go live – we are there right now! • An amazing team helps and some of them are here today 2 0 2 0 - 0 3 - 0 6 1 4
Slide 51: DW Data Warehouse – A Focus on SaaS
D W D AT AWA R E H O U S E – A F O C U S O N S A A S 2 0 2 0 - 0 3 - 0 6 1 5
Slide 52: Phase 2: Where the Focus Should Always Have Been
P H A S E 2 : W H E R E T H E F O C U S S H O U L D A LWAY S H AV E B E E N 1. Integration has been commoditised so adapt! 2. Stronger focus on business logic and processes 1 2 2 0 2 0 - 0 3 - 0 6 1 6
Slide 53: Phase 3: Tackle Analytics Adoption
P H A S E 3 : T A C K L E A N A LY T I C S A D O P T I O N • DW Data Culture • Moving through the BI maturity curve 2 0 2 0 - 0 3 - 0 6 1 7
Slide 54: Tableau + Snowflake
+ableau: + snowflake
Slide 55: Daniel Wellington Website
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Slide 56: Daniel Wellington Checkout
DI DANIEL WELLINGTON ® common YP uc snnne @ coancemn CHECKOUT 1. THE BASICS A ¢ erite asneieto Macum YOUR INFORMATION owt wows DWSHOW:LAKE (15%) 3 (1 Ihe coop ce Ast eaping COUNTRY Und gdm
Slide 57: Thank You
THANK YOU 2 0 2 0 - 0 3 - 0 6 2 1
Slide 58: Use the Following Slides Instead of Live Demo
• In case of demo ghosts à use the following slides instead of live demo 2 0 2 0 - 0 3 - 0 6 2 2
Slide 59: Disclaimer
Explore / Bi Disclaimer | Wi Disclaimer This report has been created for the intention of being presented during the Snowflake Dara for Breakfast- Event @Roots March 5th 2020. This report is a copy of a real report used by the operations team at Daniel Wellington. Since the actval information contai
Slide 60: Warehouse Movements Dashboard
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Slide 61: Snowflake Worksheet Interface
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Slide 62: Snowflake Query History
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Slide 63: Snowflake Warehouses
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Slide 64: Configure Warehouse
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Slide 65: Warehouse Size Options
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Slide 66: Warehouse Size Medium
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Slide 67: Warehouse Alter SQL
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Slide 68: Alter Warehouse Worksheet
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Slide 69: Alter Warehouse Results
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Slide 70: If the Demo Ghosts Are Still Haunting
• If the demo ghosts are still haunting this presentation à use the following slides 2 0 2 0 - 0 3 - 0 6 3 4
Slide 71: Tutorial 1: Sample Queries
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Slide 72: Sample Query with Warehouse Size
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Slide 73: Disable Cached Result
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Slide 74: Sample Query Execution
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Slide 75: Query History
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Slide 76: Query History
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Slide 77: Snowflake & Data @ Voi
Snowflake & Data @ Voi Björn Idrén Head of Business Intelligence & Analytics Voi Technology
Slide 78: Before Voi
BeforeVoi ‣ Ericsson Sales, Tech, Product, Business Intelligence. ‣ IPX, Business Intelligence. ‣ Klarna, Business Intelligence & Data Warehousing. ‣ Soundtrack Your Brand / Spotify Business, Business Intelligence & Analytics
Slide 79: This is VOI
VOI is a fun, safe and green This is VOI transport option that changes how people move in the city. ‣ Startup ‣ Operational Heavy ‣ Founded September 2018 ‣ Intense Need for Data & Insights ‣ 550+ Employees ‣ Fast Changing, Fast Pace ‣ 35+ Cities ‣ {any other words describing a positive ‣ 10 Countri
Slide 80: Being a Startup
Being a Startup ‣ Done is better than perfect ‣ Iteration is innovation ‣ Solving problems with money is not an option ‣ MVP - Minimum Viable Product ‣ Keep it Simple ‣ Keep keeping it simple
Slide 81: Data / BI Setup
Data / BI setup ‣ All Data reside in the Cloud ‣ Everyone on Mac (all but finance..) ‣ Minimalistic Lake -> DW ‣ Snowflake Partner - + 10+ SaaS product for growth
Slide 82: Traditional Business Intelligence
Traditional Business Intelligence ‣ 2-3 teams ‣ Delivery organisations ‣ Requirements given from the business ‣ Mature(r) understanding of BI in the business ‣ Speed < 100% accuracy ‣ Often little to no effort on utilising existing data
Slide 83: Startup Business Intelligence
Startup Business Intelligence ‣ One person at best ‣ Speed > 100% accuracy ‣ Prototyping ‣ BI not an established function ‣ Evangelist mode
Slide 84: Data Driven?
Speed of Iteration Data Driven? Utilize existing Data Share the data with everyone An individual without information can't take responsibility. An individual with information can't help but take responsibility. - Jan Carlzon
Slide 85: Data Driven Prototyping Voi
Data Driven Prototyping Voi Share data First attempt mapping rides on a map
Slide 86: Examples
Examples Data Prototyping Voi
Slide 87: Example - Voi
Example - Vol This shows how Voi served 9 079 Stockholm citizens and 1 009 tourists on June 25th, 2019. All in all - we provided 16 482 rides with our fleet which that day consisted of 2 697 scooters out on the streets of Stockholm. The scooters were ridden for a total dis- tance of 19 430 kilometer
Slide 88: Live Demo - Voi
Live demo - Voi Click Me
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