Demystifying Cloud Data Warehousing - NB SAP HANA Cloud Platform Azure Data Warehouse Vertica DashDB...
Transcript of Demystifying Cloud Data Warehousing - NB SAP HANA Cloud Platform Azure Data Warehouse Vertica DashDB...
1© 2017 Snowflake Computing Inc. All Rights Reserved.
Y O U R D A T A , N O L I M I T S
Nicolas BaretDirector of Pre-Sales EMEA @SnowflakeMatinale de la data, November 2017
Demystifying Cloud Data Warehousing
3© 2017 Snowflake Computing Inc. All Rights Reserved.
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 datawarehouse
built for the cloud
Over 800 customers since GA
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Snowflake: 1st Data Warehouse Built for the Cloud
SQL relational databaseOptimized 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
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Separation of Storage & ComputeSnowflake’s multi-cluster, shared data architecture
Centralized storage
Instant, automatic scalability & elasticity
ServiceComputeStorage
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“We can now run an entire set of monthly reports that used to
take 2 - 3 days in just 45 minutes.”
Ken Wood, Data Team Manager
What customers like about Snowflake
200x
Performance Simplicity
“That we don’t need to do any configuration or tuning is
great: we can focus on analyzing data instead of
managing and tuning a data warehouse.”
Craig Lancaster, CTO
Concurrency
“Consistency of performance and concurrency has been a
huge win for us.”
Erika Bakse, BI Solution Architect
100% Cloud
Complete SQL Database
Zero Management
All of your Users
Pay only for what you use
Data warehouse built for the cloud
All of your Data
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#5 - Standard SQL w/Analytic Functions
select Nation, Customer, Totalfrom (select
n.n_name Nation,c.c_name Customer,sum(o.o_totalprice) Total,rank() over (partition by n.n_nameorder by sum(o.o_totalprice) desc)
customer_rankfrom orders o,customer c,nation nwhere o.o_custkey = c.c_custkeyand c.c_nationkey = n.n_nationkeygroup by 1, 2)
where customer_rank <= 3order 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
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#4 - Connect w/JDBC & ODBC to the cloud
Data Sources
Custom & Packaged Applications
ODBC WEB UIJDBC
Interfaces
Java
>_
Scripting
Reporting & Analytics
Data Modeling, Management & Transformation
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#3 - Support Multiple Workloads
Accelerate the data pipelineRun loading & analytics at any time, concurrently, to get data to users faster
Scale compute to support any workloadScale processing horsepower up and down on-the-fly, with zero downtime or disruption
Scale concurrency without performance impactMulti-cluster “virtual warehouse” architecture scales concurrent users & workloads without contention
Deliver faster analytics at any scale
Loading
Marketing
Finance
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#2 - Secure by Design with Automatic Encryption of Data!
Embedded multi-factor authenticationFederated 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
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#1 - Automatic Query Optimization
Zero ManagementFully managed with no knobs or tuning required
No indexes, distribution keys, partitioning, vacuuming,…
Zero infrastructure costs
Zero admin costs
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#5 - Persistent Result Sets
• No setup• In Query History
• By Query ID• 24 Hours• No re-execution• No Cost for Compute
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#4 - UNDROPUNDROP TABLE <table name>UNDROP SCHEMA <schema name>UNDROP DATABASE <db name>
Part of Time Travel feature: AWESOME!
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#3 - Zero-Copy Clone
Instant copy of table, schema, or database:CREATE OR REPLACE TABLE MyTable_V2
CLONE MyTable;
With Time Travel:CREATE SCHEMA mytestschema_clone_restoreCLONE 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
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#2 - 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": "21 2nd Street", "city": "New York", "state": "NY","postalCode": "10021-3100"
}, "phoneNumbers": [
{ "type": "home", "number": "212 555-1234" }, { "type": "office", "number": "646 555-4567" }
] }
Optimized storageFlexible schema - Native
Relational processing
select v:lastName::string as last_namefrom json_demo;
All Your Data!
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#1 - Data Sharing (The Data “Sharehouse”)
Data Consumers
Data Providers
No data movementShare with unlimited
number of consumers
Live accessData consumers
immediately see all updates
Ready to useConsumers can immediately
start querying
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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
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Delivering compelling results
Simpler data pipelineReplace noSQL database with Snowflake for storing & transforming JSON event data
noSQL data base: 8 hours to prepare data
Snowflake: 1.5 minutes
Data warehouse appliance: > $5M to expand
Snowflake: added 2 new workloads for $50K
Significantly lower costImproved performance while adding new workloads - at a fraction of the cost
Data warehouse appliance: 20+ hours
Snowflake: 45 minutes
Faster analyticsReplace on-premise data warehouse with Snowflake for analytics workload
23© 2017 Snowflake Computing Inc. All Rights Reserved.
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
Robu
stne
ss o
f SQ
L 15%
Built
-in O
ptim
izatio
n 15
%
On-
the-
fly E
lastic
ity 2
5%
Dyna
mic
Envir
onm
ent
Adap
tion
20%
Sepa
ratio
n of
Com
pute
fro
m st
orag
e 15
%
Supp
ort f
or D
ivers
e da
ta
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, featuresand scalability have already caused some to postpone Hadoop adoption.”
William McKnight
GigaomDisruption Vectors
Gigaom Analyst Report: Sector Roadmap: Cloud Analytic Databases 2017
Read the full report on snowflake.net