ADV Slides: Strategies for Transitioning to a Cloud-First Enterprise

Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved Slide 1
Unlock Potential
William McKnight
www.mcknightcg.com
214-514-1444
Strategies for Transitioning
to a Cloud-First Enterprise
@williammcknight

Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved Slide 2Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved Slide 2
Introduction
 Companies are shifting their focus to, or entertaining a notion for a first-
time use of the cloud
 Spending on cloud-based Big Data Analytics technology will grow much
faster than spending for on-premises solutions.
 Due to the economics and functionality, use of the cloud should now be
a given in most database selections.
 Cost in purchasing and maintaining an additional appliance can be a
barrier to growing information management and analytical capabilities.
• Other concerns include the real estate needed, the complexity and in-
house talent required.
 Also significant amounts of important data are being sourced from the
cloud.
 On-premises enterprise data warehouses (EDWs) and analytical
platforms are often operating near capacity in terms of storage or
performance.

Decision Points
 Software model
 Development and quality assurance
 Recovery from outage and credit for downtime
 Safe harbor and cross-border restrictions
 Capacity planning and growth
 Security and privacy
 Disaster recovery
 Query performance and service levels
 Data interchange in the cloud
 Staffing levels are not zero; what does my staff still do?
 Organizational change management (to bring people along with
the move)
 Picking first targets for the journey

Software Model
 Just about any software, including databases, can
be placed in a public cloud these days
 Today, several platforms were “born in the cloud”
several others have had major engineering for the
cloud
 Software comes with a wide array of integration
with the cloud from a licensing perspective.
• Infrastructure-as-a-service (IaaS)
• Platform-as-a-service (PaaS)
• Software-as-a-service (SaaS)

Development And Quality
Assurance
 Agile development (and QA) is made possible by being able
to provision quickly, a unique characteristic of cloud
database deployments.
 The ability to provision quickly extends into new software
that you want to try out and not put through a lengthy
procurement process.
 Demand for development usually experiences a much
higher range of need than production support needs.
 The cloud can become a key part of the development cycle,
which begins the process of streamlining application
delivery.
 Development can focus on development and not
environmental challenges.

Understanding Pricing 1/2
 The price-performance metric is dollars per query-hour ($/query-hour).
• This is defined as the normalized cost of running a workload.
• It is calculated by multiplying the rate offered by the cloud platform vendor times the number of computation
nodes used in the cluster and by dividing this amount by the aggregate total of the execution time
 To determine pricing, each platform has different options. Buyers should
be aware of all their pricing options.
 For Azure SQL Data Warehouse, you pay for compute resources as a
function of time.
• The hourly rate for SQL Data Warehouse various slightly by region.
• Also add the separate storage charge to store the data (compressed) at a rate of $
per TB per hour.
 For Amazon Redshift, you also pay for compute resources (nodes) as a
function of time.
• Redshift also has reserved instance pricing, which can be substantially cheaper than
on-demand pricing, available with 1 or 3-year commitments and is cheapest when
paid in full upfront.

Understanding Pricing 2/2
 For Snowflake, you pay for compute resources as a function of time—
just like SQL Data Warehouse and Redshift.
• However you chose the hourly rate based on certain enterprise features you need
(“Standard”, “Premier”, “Enterprise”/multi-cluster, “Enterprise for Sensitive Data”
and “Virtual Private Snowflake”)
 With Google BigQuery, one option is to pay for bytes processed at $ per
TB
• There’s also BigQuery flat rate
 Azure SQL Data Warehouse pricing was found at https://azure.microsoft.com/en-us/pricing/details/sql-
data-warehouse/gen2/.
 Amazon Redshift pricing was found at https://aws.amazon.com/redshift/pricing/.
 Snowflake pricing was found at https://www.snowflake.com/pricing/.
 Google BigQuery pricing was found at https://cloud.google.com/bigquery/pricing.

Recovery From Outage And
Credit For Downtime
“If the Annual Uptime Percentage for a
customer drops below 99.95% for the
Service Year, that customer is eligible to
receive a Service Credit equal to 10% of
their bill (excluding one- time payments
made for Reserved Instances) for the
Eligible Credit Period.”

Safe Harbor And Cross-
Border Restrictions
 For multinational companies, the concern about
safe harbor and country border restrictions for data
keeps many from going to the cloud.
 GDPR
 Also in the last few years, countries have begun to
engage in protectionist measures to restrict the
flow of data across borders.
 Things to look at include where the data centers
used by the vendor are located and whether the
vendor can ensure data locality (i.e. data not being
moved across regions, etc.).

Capacity Planning and
Growth
 The platform should be able to grow or shrink.
 The platform should provision only what is needed.
• Only what is needed is what should factor into the cost equation.
 It should take minutes—not hours—to scale up or down or
create little disruption for migration or repartitioning;
otherwise, one of the key benefits of the cloud is lost.
 The more proactive and involved a customer has to be in
the process of resource determination, the less elastic the
solution is.
 The more granular the growth of the clusters, and the less
of a “step ladder” approach to resources, the more elastic
the solution is.

Security And Privacy
 Security and privacy are the largest areas of
concern today with the cloud.
 You need to do your homework with your security
policy.
 Just as a breach can occur in your data center, it can
also occur at cloud hosting data centers.

Disaster Recovery
 Digestible Cost
 Room to Scale
 Good Fit for Dual-Purpose
 Practically Maintenance Free

Query Performance And
Service Levels
 Cloud query performance will depend on the same
factors as on-premises query performance.
 One of the factors in cloud-specific performance is
that you may be sharing the underlying
infrastructure with others (multi-tenant).
 Guaranteed network bandwidth is the other lever
that is unique to managing cloud performance
 Cloud analytic query performance depends a great
deal on the network latency between users and the
service they are using.

Data Interchange in the
Cloud
 No successful enterprise application sits in total isolation.
 Performance considerations for data interchange are similar
to those above for query performance.
 Cloud setups have different pricing models for ingesting and
moving data in and out of the cloud.
 Data fabric solutions allow you to easily replicate the data
between on-premises and co-location facilities, and to
leverage compute farms from the public cloud.
 While you must know the costs, they are neither prohibitive
to full data use nor are they prohibitive to placement of
data in a proper architecture.

Staffing Reorganization
 What is the escalation for production failures in the middle
of the night?
 Who will manage hardware and software patching?
 How will we make the call/expend the budget for additional
disk/CPU/memory as the implementation grows?
 Roles
• For SaaS, the user company owns who can have access to the
software.
• For PaaS, since the vendor is responsible for the infrastructure, you
need to design with those parameters in mind.
• For IaaS, there is much more responsibility including managing the
network, the servers, the disk, the patching, encryption, backups,
logging, building redundancy, etc.

Organizational Change
Management
 The move to the cloud is fraught with tension and
apprehension and much of it will come from IT
 It is a big change—perhaps the most visceral change many
people will experience in their career.
 Stakeholders of the cloud move come from various parts of
the organization and beyond.
 Job roles will change.
 Furthermore, stakeholders must be trained to behave
differently.
 Communications around status and dates are essential as
well.
 Your cloud move must have a focus beyond technology and
address the people-related risks.

Bandwidth
 We can talk all we want about the benefits of the
cloud, but one critical factor resides squarely on
our organization’s shoulders: bandwidth.
 The cloud can get a good or bad reputation based
on this factor.
 With cloud acceptance, you naturally stop drinking
your data out of a tiny straw.

Picking First Targets For The
Journey
 The first move should be something with the right
amount of criticality.
 Pick a manageable problem to solve.
 Consider technical integration points of the
application with the “legacy” environment.
 Have a strong executive sponsor—one who puts
clout behind the project yet understands perfection
is not possible.
 Get help! Get experience.

Remember
 There is more maturity in moving to the cloud imperfectly
than in merely perfectly defining the shortcomings of the
cloud
 Build internal credibility in the team; the organization must
know it is well taken care of and this is the team that will do
that
 Don’t talk yourself out of starting
 Success is not perfection; you cannot accurately predict
activities for two to three months out, let alone six to nine
months; get started!
 That resistance to the cloud is not about being there, it’s
about getting there; communicate the plan

Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved Slide 20
Unlock Potential
Second Thursday of Every
Month, at 2:00 ET
Presented by: William McKnight
President, McKnight Consulting Group
www.mcknightcg.com (214) 514-1444
#AdvAnalytics

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ADV Slides: Strategies for Transitioning to a Cloud-First Enterprise