Hybrid and Multi-Cloud Data Movement: Deployment Strategies

Many enterprises end up with hybrid or multi-cloud systems without ever intending to. Basically, they add private cloud infrastructure for internal workloads and adopt public clouds for new products and teams while maintaining their existing on-premises systems.

However, data distributed across different networks and environments creates difficulties that affect both data transfer operations and information management systems. What used to be a simple process in one cloud environment now creates multiple issues that affect performance optimization and overall security.

This article will compare two approaches to multi-cloud data movement that have emerged in response to these challenges. It will explain when a cloud-first ELT model is sufficient, and when a unified platform designed for hybrid and multi-cloud execution becomes necessary. Tools like Fivetran and Estuary take fundamentally different paths: Fivetran prioritizes a cloud-first, SaaS-based ingestion model, while Estuary is engineered from the ground up to operate across hybrid and multi-cloud environments.

Key Drivers of Hybrid and Multi-Cloud Architectures

Hybrid and multi-cloud architectures are typically the result of practical constraints rather than long-term planning. Over time, certain factors make distributed data environments unavoidable.

1. Organizations that operate around the world need their data to be close not only to their users and applications but also to their analytics systems. If they centralize data in a single location, that can create performance issues, which only become more severe as the system grows in size.
2. Organizations cannot perform complete system migrations all at once, which is why modernization must be carried out in stages. Older databases and applications have to operate alongside new, cloud-native systems, and they require uninterrupted data transfer in order to maintain business operations.
3. When secure data is shared, sensitive information needs to remain protected and confined to environments that meet regulatory and contractual requirements.
4. Organizations can enhance the resilience of their systems by operating across multiple locations and service providers. For this to work, information must be able to flow between the system's components. When something goes wrong, this system provides additional security for your data.

These drivers push organizations toward architectures where data can move reliably across clouds, private networks, and regions, rather than staying confined to a single cloud environment.

Limitations of Conventional ETL in Hybrid and Multi-Cloud Environments

Cloud-only ETL tools work best when all your data is stored in a single cloud. Unsurprisingly, data management becomes more challenging when organizations share their data across multiple environments.

Traditional batch ETL pipelines also cause delays, which is problematic because teams need to be able to see the most recent data right away. In fact, collecting real-time data from different system environments is quite difficult because it requires thorough planning in order to be implemented effectively.

In addition, organizations have to follow data storage rules, which can limit the use of tools that are not part of their internal network.

Why Hybrid and Multi-Cloud Environments Require a Unified Data Integration Strategy

The need for deployment flexibility arises when data needs to remain within the organization’s private network, when their pipelines span different cloud environments and geographical areas, or when sub-second response times are necessary.

Right now, engineers are burning a lot of their precious time on keeping existing pipelines running instead of building what comes next. As systems grow more complex, teams are starting to prioritize tools that can adapt to different environments, rather than optimizing for the simplest possible setup.

In that context, this article examines two very different approaches to moving data around: Fivetran, a cloud-first model designed around SaaS and warehouse-centric workflows, and Estuary, a more flexible version that can be used in hybrid and multi-cloud environments.

Two Ways Teams Handle Data Movement Across Multiple Clouds

Most businesses eventually come to the conclusion that multi-cloud and hybrid configurations are here to stay. Once you accept this reality, the problem becomes more practical: how can you reliably transfer data between systems without increasing costs or creating new security risks?

In practice, teams typically take one of the two approaches:

1. Layering cloud-native SaaS tools onto hybrid environments
2. Using a unified platform designed for hybrid and multi-cloud execution

In the first deployment path, a basic cloud-native SaaS platform that carries out simple cloud-to-cloud data transfer operations is established through incremental development. Extra components need to be added during development to support private network and on-premises systems.

By contrast, the second approach treats all data transfer operations as a single challenge from the beginning, regardless of the system environment.

Both of these closely mirror how teams evaluate Fivetran and Estuary for hybrid and multi-cloud data integration.

Path 1: Fivetran’s SaaS Model Extended to Hybrid Environments via HVR

Organizations choose Fivetran because it’s so easy to set up and supports many different connector types.

The Fivetran platform works best when SaaS tools and cloud databases are used for analytics because performance remains strong even when delays occur. However, the system doesn't work well when data is stored across locations that extend beyond the public cloud infrastructure.

Fivetran’s managed SaaS service is limited when pipelines need to operate inside private VPCs or access restricted networks. The service usually needs IP allow-listing, inbound network access, or public exposure of data sources because it runs on Fivetran-managed infrastructure. As a result, supporting environments with stringent network isolation, private-only endpoints, or data residency restrictions become challenging. Furthermore, pipeline execution and latency depend on Fivetran's SaaS scheduling model instead of customer-controlled infrastructure.

By default, Fivetran operates in batch mode rather than real time, with delays ranging from minutes to hours, depending on the pricing tier. Currently, the system requires HVR to function; in fact, after acquiring HVR, which enables real-time data replication from on-premises systems and private networks, Fivetran developed a hybrid use case solution. While this offers new advantages, it also creates specific drawbacks. Namely, the HVR system is a self-hosted solution that requires customers to manage their own virtual machines and demands individual configuration and operational control.

Most teams end up using two tools: one handles data from the cloud, and the other one comes into play for private or on-premises systems. Though this setup is easy at first, it becomes more difficult to handle over time. Connectors don't line up, the tools don't work well together, and expenses soar as engineers spend more time maintaining systems rather than improving them.

As a result, many teams end up with a layered architecture: Fivetran for cloud data ingestion and HVR for private or on-prem systems, each with different operational characteristics.

Path 2: Estuary, a Unified Platform for Hybrid and Multi-Cloud Environments

The second approach starts by acknowledging that transferring data between various cloud systems and environments should be a standard practice.

Estuary's system design separates control plane operations and data plane functions clearly. This enables users to control their data pipelines, schemas, and monitoring functions through a single interface, while selecting the locations for their data transfer operations. Different data plane configurations exist as separate deployment models.

Teams can choose one of the three different ways to set up Estuary while using the same platform:

1. Managed SaaS: Estuary is a software as a service (SaaS) platform that is fully managed and used by many tenants. It provides users with quick access to its features.
2. Private data plane: The private data plane works inside a VPC network, which means all data traffic stays within that network.
3. Bring Your Own Cloud (BYOC): The BYOC model lets customers manage their own infrastructure, while Estuary is responsible for control plane management.

All options use the same platform foundation. This lets organizations choose different deployment models for their pipelines. The system runs three different workflows in SaaS, private VPC, and BYOC environments, all under a single, central management system.

Estuary is the right choice for the job thanks to its flexible deployment features. How pipelines work depends on what you need the platform to do, whether it should process data on schedule or as it arrives. The platform combines streaming transformations, batch imports, and CDC into one system, thereby eliminating the need for separate tools.

Estuary provides over 200 native connectors and supports hundreds of open-source connectors, making sure their behavior remains consistent. The platform's usage based pricing system helps you predict how much extra storage will cost. The end-to-end platform management system reduces maintenance requirements and is more efficient than self-hosted CDC solutions.

Side-by-Side Comparison: Estuary vs. Fivetran

As you could have seen from the detailed look at the two approaches, they both operate in different ways. Organizations can achieve hybrid functionality by combining their self-hosted systems with cloud SaaS tools. As soon as the system is turned on, data is immediately transferred across various cloud services.

If you examine how Estuary and Fivetran work (what they are capable of, what they must be able to do, and how efficiently they operate), it becomes clear how different they are. This evaluation examines the factors that have the biggest impact on business operations during pipeline criticality. For a more in-depth comparison, check out this page.

Deployment Models and Operational Control

The table below displays who is in charge of data plane operations and which environments facilitate pipeline execution. These operations govern decisions about regulated and hybrid environments.

Core Data Movement and Processing Capabilities

Now we’ll focus on the best solution for production environments that support teams tasked with handling complex operations and extensive system connections while complying with strict regulatory standards.

The two options show their strengths when compared side by side.

Fivetran works best for organizations that mainly run batch operations and only use cloud environments. Organizations that run pipelines in real-time, manage their private networks, and make sure their systems always work no matter the environment, need a solution that can connect multiple systems.

On the other hand, Estuary's platform is designed to be used in a number of different ways, while still allowing the pipeline to function.

The next section will explain which approach works best in different situations.

Practical Data Governance Framework for Hybrid and Multi-Cloud Data Pipelines

At this stage, most teams should have a general sense of their preferred direction. The main challenge lies in translating those instincts into specific choices, especially when organizations need to balance present-day operational needs with future, uncertain demands.

It’s often easier to define what you need to do by starting with constraints rather than tools. The following questions highlight the key challenges that prompt teams to adopt hybrid or multi-cloud architectures rather than cloud-only ELT solutions.

Complete Governance Framework Checklist

The questions below reflect practical constraints that frequently push teams toward hybrid and multi-cloud architectures.

• Do you know where your data resides, and is it within private networks? This includes three types of data: regulated data, workloads that must remain within their designated VPC or region, and internal databases that need to be protected from public access.
• Do you need CDC data in real time, or are near-real-time updates good enough? Operating systems, customer-facing features, and downstream services need data to be delivered quickly. Batch-only pipelines are insufficient here.
• Do you currently operate across several clouds or regions, or do you plan to? Mergers, regional expansion, and cloud diversification are all done gradually rather than at once.
• How long does it take an engineer to maintain a pipeline? Things like re-extractions, configuration changes, connector failures, and HVR-style maintenance add up over time.
• Can you predict the cost of transferring data, and should these estimates influence your planning? When prices go up and down because of fluctuating production volume or other business changes, it can be hard to budget.

When two or more of these apply, cloud-only, batch-first data integration tools tend to become limiting.

How to Interpret These Tradeoffs for Hybrid and Multi-Cloud Data Integration

As mentioned above, if you have answered affirmatively to two or more of these questions, then having a system that can use multiple clouds is essential.

The main difference between these two approaches lies in how they are designed.

Although a layered approach (SaaS ELT + self-hosted CDC) works well, it causes data fragmentation since different tools and techniques are needed to manage data in different locations.

Estuary, a unified platform that supports various deployment techniques, will reduce the fragmentation of the current system. Pipelines behave the same whether they are utilized in your company's cloud infrastructure, a public SaaS environment, or a private VPC.

Teams concentrating on batch analytics operations in a single cloud environment benefit from cloud-native ELT. Hybrid and multi-cloud support should be a top priority for organizations that prioritize real-time data, private networks, compliance boundaries, and long-term system design flexibility. In fact, they should treat it as a requirement, not an option.

Choosing a solution that will prevent you from having to redo the entire project when your system environment becomes more complex is preferable to simply selecting the most potent tool.

Conclusion

Hybrid and multi-cloud setups are no longer the exception, as most modern data stacks actually operate this way. They typically comprise a mix of on-premises systems, a primary cloud, and sometimes a second cloud introduced through a merger or a new product team. As a result, the architecture evolves faster than the supporting tools.

Cloud-only ETL platforms seem to be working well when all data is kept in one location and real-time processing isn’t necessary. However, due to modern-day requirements, data needs to be transferred between various locations, clouds, and private networks in a time-sensitive manner. The structure you choose impacts your ability to meet compliance requirements and maintain accurate and up-to-date data.

In hybrid infrastructure environments, pricing models based on row counts and sync frequency rates can lead to unexpected financial struggles. With Estuary, users can move data between managed SaaS, private VPC, and BYOC environments in real time or in batches using the platform. This is done via a single interface that employs a capacity-based pricing structure. Such a model enables teams to manage costs while transferring data between various network locations and cloud environments without encountering any unforeseen issues.