Cloud vs on-premise data transformation

Successful businesses anticipate customer needs and deliver them. Some businesses, however, go even further, anticipating these needs before customers even express them.

The key to this lies in using data strategically.

In fact, 65% of highly data-driven businesses report financially outperforming their peers, nearly double the share of less data-driven companies. This shows that having the right data and knowing how to use it is one of a company's most substantial competitive advantages.

At the core of this advantage lies data transformation: the essential process of converting raw datasets into meaningful insights that support decision-making.

Teams can run these transformations in the cloud or on-premises. Each approach is beneficial depending on business needs. This article explores both options to help you select one that best supports your team's goals.

What is cloud data transformation?

Cloud data transformation refers to the process of transforming data entirely within cloud platforms, which offer on-demand compute on a pay-as-you-go pricing model. It uses cloud-based services to process, clean, and reshape data for downstream use.

Cloud solutions don't require significant upfront costs or capital expenditure to create a massive data center. They also avoid the problem of overplanning capacity, where compute runs idle during less heavily-trafficked periods.

As such, cloud computing enables automated data transformations for the large data volumes required by modern enterprises due to its scalable architecture and cost-effective, high-performance compute power. These cloud-based solutions provide highly scalable tools that require less setup time and manual work, with most cloud providers providing out-of-the-box support for common tools and technologies such as data warehousing, streaming, data processing, and more.

Cloud data transformations run in cloud data warehouses like Snowflake and Amazon Redshift, where compute resources scale independently of data storage. Cloud service providers like AWS and Microsoft Azure offer these cloud platforms with robust infrastructure. Teams can modernize legacy applications, migrate non-critical data, and streamline IT operations.

What is on-premises data transformation?

On-premises data transformation is the process of transforming data within the organization's local infrastructure.

Organizations gain complete ownership of their environment. They design and implement custom security protocols that meet specific compliance requirements.

Hosting and managing IT infrastructure in-house enables more direct control over hardware security. This helps ensure compliance with regulations and standards such as GDPR, PCI-DSS, and HIPAA. Teams can manage audit trails more effectively by keeping the organization's data on the company's own servers within private data centers.

How cloud and on-premises data transformation differ

Differences between cloud and on-premises data transformation

Both on-premise and cloud data transformation models affect how businesses manage their daily operations, including resource allocation, availability, security, and maintenance. Understanding the key differences helps you evaluate which approach best serves your business needs.

Scalability and cost model

Cloud. Cloud data transformation operates on an operational expense (OPEX) model. Companies pay only for the resources they consume with pay-as-you-go pricing. Compute power and data storage adjust on their own to keep up with changing data volumes and processing needs. This saves businesses from investing in additional hardware.

The scalability of cloud infrastructure allows you to optimize cloud costs by scaling resources up during peak workloads and down during quieter periods. When evaluating the total cost of ownership (TCO), cloud environments often prove more cost-effective for variable workloads.

On-premises. On-prem solutions require capital expenditures (CAPEX) for hardware and ongoing maintenance. Managing high data volumes requires physical hardware upgrades and infrastructure investments.

The downside is that organizations pay for capacity even when it isn't being used. For example, a company must still pay for servers it purchased to accommodate Christmas shopping traffic even after the Christmas season ends. However, on-premise setups can offer better pricing predictability and control over the IT infrastructure lifecycle.

Deployment speed

Cloud. Cloud-based transformations can be provisioned and executed quickly. They use automated workflows and prebuilt services to manage large datasets efficiently. Fast deployment provides businesses with a significant competitive advantage.

One trade-off with cloud-based services is that latency between your network and your cloud provider may slow data-intensive operations. However, cloud environments support real-time data processing for most use cases, and low latency can be achieved through strategic placement of cloud resources.

On-premises. The setup time for on-premises deployments is lengthy due to hardware configuration and manual processes. However, the main reward of on-premise implementations is the control that IT teams gain. Access to on-site servers is also orders of magnitude faster than access to cloud services, making on-prem ideal for low-latency workloads.

Maintenance and management

Cloud. Cloud services transfer infrastructure management responsibilities to the service provider, including updates, patching, and security monitoring. There's no overhead of physical servers to purchase or set up. This makes cloud solutions cost less and easier to manage.

Cloud service providers handle routine upgrades, backups, and disaster recovery, allowing your IT team to focus on higher-value work. The cloud ecosystem provides automation tools that further reduce maintenance overhead.

On-premises. On-premise systems require skilled IT staff for continuous maintenance, monitoring, and troubleshooting. The team has physical access to both hardware and software to resolve issues. While this requires more in-house resources, it provides complete control over the maintenance lifecycle and upgrade schedule.

Security and control

Cloud. Cloud providers offer many of the same security controls used in on-premises environments as fully managed services. They secure the underlying infrastructure, including data centers, hardware, and monitoring systems.

Cloud security follows a shared responsibility model. Organizations are still responsible for securing their own data and applications. Teams must configure identity and access controls while ensuring compliance with both internal policies and industry regulations.

One consideration with cloud systems is that, by default, most workloads run on shared public cloud infrastructure. While cloud providers maintain a logical barrier between customers, this might not be enough reassurance for companies with highly sensitive data workloads. However, private cloud and hybrid cloud options provide additional security and data security layers for sensitive data.

On-premises. On-premises transformation provides full control over data. With physical control over all hardware and infrastructure components, teams can implement customized security protocols and maintain full auditability. This makes it easier for businesses to comply with regulations that require strict handling of financial or personal data.

On-premises infrastructure keeps sensitive data on-site with redundancy measures fully under your control, which can be critical for healthcare and other regulated industries.

Flexibility and innovation

Cloud. Cloud environments support rapid experimentation and integration of new tools in AI and analytics. Teams have the luxury of experimenting and testing at a much faster pace. This makes cloud platforms a perfect option to drive innovation and speed up deployments.

Cloud computing offers a vast ecosystem of services and integrations. The multi-cloud and hybrid approach options give you flexibility to choose best-of-breed solutions without vendor lock-in.

On-premises. On-premises environments are constrained by existing infrastructure limitations and slow adaptation cycles. Advanced analytics access is limited due to hardware restrictions and the workaround of software upgrades.

On the flip side, having full control over the environment allows teams to customize processes when upstream workflows or data sources change. They can develop stable workloads, integrate seamlessly with legacy systems, and avoid service disruption risks. On-prem data stays within your controlled environment, which some organizations prefer for critical systems.

Selecting the right data transformation model for your team

Today's businesses operate increasingly in digital environments. Making the right decision between on-premises and cloud data transformation is becoming increasingly important. This decision can influence everything from operational efficiency to financial outlay.

These considerations will help you select the right model for your team:

• Data sensitivity and location. Businesses managing highly sensitive and confidential data, such as in healthcare or finance, often favor on-premises storage. This ensures no third-party vendor has access to sensitive data, like patient records in healthcare, which is particularly important for compliance. Keeping the organization's data on private servers ensures teams maintain full control and protection.
• Regulatory compliance. Security and compliance are critically important in regulated sectors such as healthcare and finance. Standards like HIPAA, PCI DSS, and GDPR govern how data must be stored, processed, and accessed. Although cloud providers offer configurable deployment options to support compliance requirements, organizations with strict data sovereignty or local residency requirements often prefer on-premises infrastructure. Storing data in specific locations and maintaining direct control ensures compliance with local regulations and simplifies audits, particularly when sensitive data must remain within defined jurisdictions.
• Skills and management approach. Teams should assess whether they have the technical expertise to manage infrastructure themselves. If not, they may rely on managed services. Cloud solutions help businesses with limited IT resources to maintain their infrastructure more easily. Consider what your IT team can realistically support given their current skills and capacity.
• Workflow complexity and automation. The nature of your data transformation workflow matters. If your top goal is to automate and streamline processes, then cloud platforms provide integrated orchestration and scheduling. On-premises systems instead require extra configuration and customization to support complex data pipelines. Cloud-based automation can significantly reduce manual work.
• Scalability and flexibility. Long-term adaptability is important as business requirements and data sources change. Organizations anticipating growth should plan for scalable transformation models. Cloud platforms offer elastic scaling and enable efficient processing of large data volumes, whereas on-premises solutions provide predictable performance for stable demands. The scalability of cloud infrastructure makes it ideal for rapidly growing workloads.
• Performance and latency needs. Consider your performance requirements. On-premises setups can deliver low latency for local workloads, while cloud platforms excel at distributed, high-performance computing. Real-time use cases may benefit from hybrid approaches that optimize for both speed and scalability.
• Total cost considerations. Evaluate both upfront costs and ongoing expenses. Cloud pricing models offer pay-as-you-go flexibility, while on-premise solutions involve higher capital expenditure but potentially lower long-term costs for steady workloads. Understanding TCO helps you make cost-effective decisions.
• Disaster recovery and backup strategy. Cloud platforms typically include built-in disaster recovery and backup capabilities with off-site redundancy. On-premises environments require you to implement and manage your own backup and disaster recovery plans.
• Adopting a hybrid strategy. Companies can implement a hybrid cloud migration if moving everything to the cloud is impractical. This hybrid approach lets you get the best of both worlds. Migrate in phases, keeping sensitive data on-premises while shifting analytics workloads or less critical data to the cloud. This adds cloud scalability and agility for selected tasks while keeping core systems on-premises for security and control. A hybrid cloud strategy offers flexibility to optimize each workload based on its specific requirements.

dbt: the modern standard for data transformation - whether cloud or on-premise

dbt is an open-source data transformation tool. It processes data after it's loaded into a SQL data warehouse. dbt follows the extract, load, transform (ELT) model, where data is first loaded and then transformed within the data warehouse.

dbt provides data analysts with a platform to transform, test, and document datasets using modular, reusable, and version-controlled SQL scripts. These features make collaboration easier for multiple data teams and optimize your data transformation workflow.

dbt also supports automated testing to validate transformation logic and data quality. This helps in maintaining the overall reliability of the data workflow. Additionally, it generates comprehensive documentation that provides visibility into transformation processes and data lineage.

dbt Core vs dbt: Key differences

dbt provides you with both on-premises and cloud data transformation options with dbt Core and dbt, our hosted version of dbt Core.

Below is a table summarizing the key differences between them:

Conclusion

Choosing between on-premises and cloud data transformation requires careful consideration. The best choice depends on how a business wants to operate and produce outcomes.

With cloud data transformation, adjusting computing resources is more scalable and flexible. Businesses can seamlessly scale resources up or down based on current needs and optimize costs through pay-as-you-go pricing models. Cloud platforms offer rapid provisioning, extensive ecosystems, and built-in automation.

In contrast, on-premises data transformation is more suitable for strictly regulated and security-conscious objectives. On-prem solutions provide complete control over data security, infrastructure, and compliance. They work well for organizations with stable workloads, specific latency requirements, or regulatory constraints.

The decision between dbt Core and dbt involves similar trade-offs. Teams can align their choice with their skill sets, security policies, and operational priorities. Either option helps teams run data transformations reliably and at the speed they need. Whether you choose cloud or on-premise, dbt gives you the tools to optimize your data transformation workflow.

To get started with dbt in the cloud, sign up for the dbt service today. Or download and install dbt Core to implement your own on-premises solution