The document discusses the differences between on-premises data warehouses and cloud-based data warehouses, known as Data Warehouse as a Service (DWAAS). It outlines the advantages and challenges of both models, including cost, scalability, performance, and security considerations. Organizations need to assess their specific requirements to determine the most suitable data warehousing solution for their needs.

1. Home » Blogs » Cloud Services, Data as an Asset
Cloud vs On­prem Data Warehouse
By Siddharth Jothimani | July 20, 2023
Introduction
A data warehouse is a central repository of data and information businesses can use to analyze and make
informed decisions. These data can come from in­house applications and databases regularly and are
accessed by various people depending on their requirements. Various business intelligence systems and
analytic solutions access these data and give decision­makers meaningful insights. While transactional
database systems (OLTP) systems enable the real­time execution of many transactions across multiple
databases, they may not be more suited for sizeable analytical processing. Data warehouses are best suited
for business intelligence and reporting use cases as they offload the analytical processing from
transactional databases and provide faster processing of large volumes of data through various data storage
modes.
A simple architecture of a data warehouse is below:
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2. Businesses deploy data warehouses in two methodologies:
1. On­premises (on­prem) deployment
2. Cloud­based (SaaS) deployment
On­prem data warehouses
In an on­prem data warehouse model, the customer is entirely responsible for purchasing, deploying, and
maintaining the hardware and software. The on­prem data warehouse is aptly named as it resides within the
customer's data center, ensuring its physical presence at their premises. The customer will have complete
control over the security aspects of the warehouse. This is applicable starting from perimeter security to
prevent any physical damage up to securing the data stored in the warehouse using appropriate encryption
tools.
Some of the commonly used on­prem data warehouse products are:
1. IBM Integrated Analytics System
2. Pivotal Greenplum
3. Teradata
Benefits
Some of the common advantages of on­prem systems include:
1. Control – The organization will control the entire data warehouse operations. This includes:
End­to­end software technology stack, including all customizations and configurations that suit their
requirements.
The type of hardware to buy ­ commodity servers or purpose­built storage and networking
components, power supply, backup options, etc.
Physical access to a data warehouse in case of any failure for inspection and troubleshooting of any
component – be it at the hardware or software layer.
2. Performance – The network latency between various components in an on­prem data warehouse will be
relatively less. This will lead to a marginal increase in the speed or performance of the application.
However, other factors affect latency, and just being on­prem does not guarantee good speed or
performance of the application.
3. Governance – Since the data warehouse is located on a customer’s premises, requirements around data
governance and compliance are much easier to achieve. Regulatory requirements like GDPR or CCPA are
easier to implement as you know where the data is precisely located.
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3. Challenges
Some of the drawbacks of on­prem systems include:
1. High upfront cost – A price is involved for setting up an on­prem warehouse, including the hardware
and software cost and physical building with all required systems to keep the center up and running. This is
also exacerbated by hardware depreciation, recurring maintenance costs for support personnel, etc.
2. Need for support from the team – There should always be a support team that is primarily responsible
for keeping the systems up and running efficiently. This team includes administrators and engineers at
different levels – network, system, database, and application.
3. Cannot quickly scale up or down based on business need – Rapidly adjusting resources to
accommodate unexpected surges in activity poses a significant challenge for data warehouses.
Cloud data warehouse
Businesses are moving towards a Cloud­based data warehousing model to leverage Cloud providers'
advantages, leading to a new service model called Data warehouse­as­a­Service (DWaaS).
Some of the facts that corroborate this include:
• Research Nester published a report titled "Data Warehouse as a Service (DWaaS) Market: Global
Demand Analysis & Opportunity Outlook 2031" – which states that the DWaaS market is estimated to
grow at a CAGR of ~22% during the forecast period 2022 – 2031.
• The DWaaS market size was valued at USD 4.26 Billion in 2021 and is projected to reach USD 29.52
Billion by 2030.
Some of the popular Cloud data warehouses include:
1. Snowflake – Can operate across multiple Cloud providers
2. Google BigQuery
3. AWS RedShift
4. Microsoft Azure Synapse Analytics
Benefits
Some of the essential benefits offered by Cloud warehouses include:
1. Pay­as­you­go pricing model – There is no upfront cost involved in setting up a data warehouse, and
the pricing model almost always depends on the usage of services. This means no capital expenses for
organizations and only running operational costs, thereby saving the TCO.
2. High scalability – Cloud solutions are scalable based on business needs, and handling colossal capacity
or volume of data will never be an issue. The resources will be added and removed based on the load and
can be customized by organizations. For example, the organization can set up a rule to increase
computational resources by 80% over the weekend to account for a promotional sale on its website. And
another rule is to delete the newly added resources once the sale is completed.
3. Quick time to market – Since there is no upfront cost to build the warehouse, organizations can quickly
deploy their application and gather business insights, increasing their time to market.
4. High availability – Most services provide at least 99.9% data availability. This is coupled with high
durability and reliability as the data is stored in multiple data centers across different regions.
5. Security – While security is a common concern towards moving to the Cloud, most providers invest
heavily in security aspects and have various mechanisms to ensure the data is safe. This includes
encryption of the data stored in disk (encryption­at­rest) using multiple options, encryption in transit using

4. encryption of the data stored in disk (encryption­at­rest) using multiple options, encryption in transit using
SSL, diligently following various security certifications like SOC 2, etc.
Challenges
Despite all the advantages offered by migrating to the Cloud, organizations face specific challenges, some
of which are listed below:
1. Most Cloud services expect the users to be aware of their responsibility in using them. Some critical
features like security in the Cloud, cost management, and user access control depend entirely on how the
organizations configure and use such features. Simply put, the security of the Cloud is the Cloud provider's
responsibility, and security in the Cloud is the customers' responsibility.
2. Based on usage, the dynamic pricing model of Cloud services presents a challenge for organizations
once they adapt to this flexible cost structure.
3. Deploying hybrid architectures that need high interoperability and customizations is difficult. Some of
the architectures would have used highly customized or legacy software. Cloud services cannot provide
fine­grained customization, and organizations must follow different approaches to deploy such
architectures.
4. Contractual obligations and technical challenges to change the Cloud provider if required.
Key differences
Here are some key differences between on­prem and DWaaS data warehouses based on common criteria.
Criteria On­prem data warehouse Cloud data warehouse
Cost
Upfront capital expense required.
No upfront capital cost whereas operating expenses
will be incurred.
Cost depreciation over time. Variable monthly cost depending on the usage.
Regular maintenance of hardware. No maintenance overhead
Need dedicated support personnel.
Premium support cost is usually required for
critical applications
No monthly costs.
Scalability
Highly rigid. Any changes to
hardware or software requires heavy
IT time and effort to execute.
Highly elastic. Resources can be added or removed
on the fly without any manual intervention based
on the application load.
Time to market
Higher go to market time as the
infrastructure needs to be built first.
Less go to market time. Businesses can build their
applications quickly and deploy to get user
feedback without worrying about the infrastructure.
Built in
ecosystem
In on­prem environment,
organization should build all
applications for security, user
management, monitoring,
notifications, analytics, etc.
All cloud providers offer services for security, user
management, analytics, etc. so the organization
need not go to different software for each use cases.
Security
Security is sole responsibility of the
organization and in­house IT team
deployed to maintain the on­premises
data warehouse.
Security is a shared responsibility between cloud
service providers and organizations. All necessary
security features are available in cloud and should
be implemented by the organization.
Conclusion

5. Conclusion
Most organizations consider DWaaS an integral step in their architecture landscape, considering the savings
in cost and effort that Cloud solutions offer. However, on­premises data warehouses are optional. Some
industries have highly customized or niche legacy use cases running on their on­premises for decades, for
which Cloud support may need to be higher. So, it is up to organizations to assess their technology
landscape and roadmap and identify which is best suited for their interests.
Mastech helps customers understand their business requirements and guide them to build the right data
warehouse on­premises or in Cloud that they can use to unlock important insights out of the data.
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Siddharth Jothimani
Director ­ Data In Motion
Technology leader with 18+ years of expertise in application architecture, design and Cloud adoption
transforming businesses through innovative data solutions.
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