You can watch the replay for this Geek Sync webcast in the IDERA Resource Center: http://ow.ly/N1d850A58MA Long gone are the days where the only architecture decision you had to make when scaling an environment was deciding which part of the datacenter would store your new server. There is a dizzying array of options available in the SQL Server and Azure ecosystems and those are evolving by the day. Is “the cloud” a fad? Are private datacenters a thing of the past? Could both questions have a kernel of truth in them? Join IDERA and Matt Gordon as he shares real-world scenarios and walks you through solutions that utilize your datacenter, cloud providers, and everything in between to keep your data highly available and your customers happy. This is an interactive Geek Sync you will not want to miss. About Matt: Matt has worked with SQL Server since 2000 and has worked with versions up to and including SQL Server 2017. He is the leader of the Lexington, KY chapter of PASS, a frequent community speaker, an IDERA SQL Superstar, and PASS Summit 2017 speaker. His original data professional role was as a database developer, but that quickly evolved into query tuning work which further evolved into being a full-fledged DBA in the healthcare realm. He has supported critical systems utilizing SQL Server across multiple data centers and managed dozens of 24/7/365 SQL Server implementations. He currently utilizes those years of real world experience as a data platform consultant helping clients design deployment solutions that meet their ever-changing business needs.

1. Where Should
My Data Live
(and Why)?
May 23rd, 2018

2. Speaker Contact Info
How Can You Contact Me?
• Twitter: @sqlatspeed
• LinkedIn: https://www.linkedin.com/in/sqlatspeed/
• Email: mgordon@dminc.com
• Blog: https://www.sqlatspeed.com
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3. Speaker Info
About Me
• 15+ years of SQL Server experience
• Managed 24x7 datacenters
• Worked on development teams
• MCSE: Data Management and Analytics
• Home cook and car geek
• IDERA SQL Superstar
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4. How I Picked My Twitter Handle and Domain Name

5. Agenda
Cloud, On-Premises, or Both?
Where Does Our Data Live Now?
Why Does Our Data Live Where It Does?
Case Studies
3
1
2
5
5
Wrap-up
4

6. Definitions
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7. Discussion Points
• Cloud is just somebody else’s computer in somebody else’s
datacenter
• Rapid development from cloud providers constantly expands
options
• Are you locked into deployment locations for certain platforms?
• Database engines always on-premises
• Hadoop always in cloud
• Blending of technologies and platforms may/may not be right
answer
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8. Where Does Our Data Live Now?

9. Where Does Our Data Live Now?
• How many of us do not have a single data environment in the
cloud?
• How many of us have only test/dev/QA data environments in the
cloud?
• How many of us have a “trial” production data environment in the
cloud?
• How many of us have all production data environments in the
cloud?
• How many of us have all (or nearly all) data environments in the
cloud?
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10. Why Does Our Data Live Where It Does?

11. Where Does Our Data Live Where It Does?
On-Premises Pros
• Cost
• Leveraging “investments”
• Can cost less if uptime is not critical
• Comfort level
• “I can go see it”
• Physical control and security
• Data accessible even when all external telecom is down
• Licensing

12. Where Does Our Data Live Where It Does?
On-Premises Cons
• Generally requires large up-front investment
• Requires corresponding infrastructure
• Rackspace, cooling, cabling, telecom, fire suppression, etc.
• May require backup datacenter
• Depends on uptime requirements
• On-site personnel often needed to maintain operations
• More expensive from a resource perspective

13. Where Does Our Data Live Where It Does?
Cloud Pros
• Cost
• Buy only what you need
• Scalability (vertical and horizontal)
• Global redundancy
• Storage durability
• Data availability from all locations
• PaaS often satisfactory to government security
audits/approvals
• High availability and disaster recovery often built-in*

14. Where Does Our Data Live Where It Does?
Cloud Cons
• Can require robust Internet connectivity
• VPN cost can be significant
• Minimal to no control over underlying infrastructure
• “Noisy neighbors”
• Design apps to deal with connection hiccups more efficiently
• Perception of lighter security
• “Things happen by magic”

15. Cloud, On-Premises, or Both?

16. Cloud Deployment Options (Azure)
• Mimics on-
premises
behavior but
resources are
on Azure
• Full control of
configuration
• Full control of
maintenance
• MPP cloud-
based,
scale-out,
relational
database
• Separates
storage and
compute
• Can pause
compute
capacity
when not
needed
• PaaS flavor
of SQL
Server
database
• Very limited
control of
maintenance
• Limited
control of
configuration
• Microsoft’s
flavor is
known as
HDInsight
• Used for
semi-
structured
data
• Can
connect
from
database
engine
using
PolyBase
SQL Server (IaaS) Azure SQL DW Azure SQL DB Hadoop

17. Cloud Deployment Options (Amazon)
• Mimics on-
premises
behavior but
resources are
on Amazon
EC2
• Full control of
configuration
• Full control of
maintenance
• Amazon
equivalent
of Azure
SQL DW
• Fully
managed
• Easily
scalable
• Amazon
PaaS offering
• Supports six
database
engines
• Minimal
configuration
control
• Amazon’s
HDInsight
equivalent
is EMR
• Supports
traditional
Hadoop
tooling
• Can
connect
from
database
engine
using
PolyBase
SQL Server on EC2 Amazon Redshift Amazon RDS Hadoop

18. Cloud Deployment Options (Google)
• SQL Server
on Google
Cloud
Platform is
IaaS offering
• Full control of
configuration
• Full control of
maintenance
• Multiple
versions and
editions
supported
• No real
Google
equivalent in
this space
yet
(BigQuery?)
• PaaS flavor
of database
engines
• Supports
MySQL and
PostgreSQL
(beta)
• Fully-
managed
• Google’s fully-
managed
flavor is known
as Google
Cloud
Dataproc
• Used for semi-
structured data
• Can connect
from database
engine using
PolyBase
Google Compute Engine
DW? Google Cloud SQL Hadoop

19. On-Premises Deployment Options
• Traditional
deployment
of the
database
engine
• Full control of
configuration
• Full control of
maintenance
• MPP
appliance
• Evolution of
Parallel Data
Warehouse
(PDW)
• Architecture
of Azure
SQL DW
based on
this design
• No true on-
premises
equivalent of
Azure SQL
Database
• Microsoft’s
flavor is
known as
HDInsight
• Many other
non-Microsoft
deployment
options
• Can connect
from
database
engine using
PolyBase
SQL Server Microsoft APS PaaS Database Hadoop

20. Hybrid Deployment Options/Scenarios
• Easy to create and destroy databases as
needed for development and deployment
• Removes management responsibility
from devs
• Good choice if DBA team short on
resources
• Uses Azure as backup datacenter(s)
• Requires robust network infrastructure
• Good for minimum datacenter proximity
requirements
On-Premises App Servers & Azure SQL DB Availability Groups with Azure Replica(s)

21. Hybrid Deployment Options/Scenarios
• Tried and true technology in use
• Identical to doing this on-premises other
than network portion
• Good way to ease into comfort with the
cloud
• Azure SQL Database can be a
replication subscriber
• Eases DBA team into cloud and PaaS
interactions
• Straightforward setup
Replication to Azure IaaS VM Replication to Azure SQL Database

22. Hybrid Deployment Options/Scenarios
• Popular with customers who want a copy
of data stored completely off-site
• Straightforward setup
• Expands environment without requiring
cluster or other complicated infrastructure
• Great for querying large quantities of
semi-structured data
• Good way to introduce team to
PolyBase
• Subject of our first case study
Log Shipping to Azure IaaS VM PolyBase to Azure Blob Storage

23. Case Studies

24. Case Studies
Transportation Planning Agency
• Statistical models generating TBs of output every year
• Storage costs spiraling upward and difficult to manage
• Output stored in relational database tables requiring constant
maintenance
• Output generated as text files which were fed into the relational
tables
• Loaded output files into Azure Blob Storage (cold)
• Query performance increased
• Storage costs decreased by 96% ($2k/year vs. $75K/year)
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25. Case Studies
Geospatial Research Center
• Hosted Hadoop cluster
• Hosted HDFS storage storing Excel, CSV, XML, JSON, etc.
• SQL Server installed on Azure VMs
• Database engine, DQS, MDS, and SSAS in use
• PolyBase used to query semi-structured data from main SQL
Server databases
• Data consumers presented with common interface to access
heterogeneous data
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26. Wrap-up

27. Wrap-up
Discussion Points
• Cloud is just somebody’s computer in somebody else’s datacenter
• Rapid development from cloud providers constantly expands options
• Are you locked into deployment locations for certain platforms?
• Database engine always on-premises
• Hadoop always in cloud
• Blending of technologies and platforms may/may not be the right answer
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28. Wrap-up
Recommendations
• Set expectations on what cloud technologies are and what they can do
• Management
• Team
• HA/DR isn’t done by magic – it’s just different
• Stay abreast of new technologies
• Research
• Training
• Azure Stack
• Embrace it all!
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29. Questions?

30. Thank You!
How Can You Contact Me?
• Twitter: @sqlatspeed
• LinkedIn: https://www.linkedin.com/in/sqlatspeed/
• Email: mgordon@dminc.com
• Blog: https://www.sqlatspeed.com
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