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Cloud Migration Made Easy: Modernizing Data Infrastructure in the Cloud

Jeff Tao
TDengine

In today's digital world, cloud migration is essential for organizations looking to modernize their data infrastructure. According to Gartner, from 2022, 82% of businesses consider cloud migration an essential part of digital transformation, and by 2025, 51% of IT spending will have shifted from traditional solutions to the public cloud. But the process of migrating to the cloud can be intimidating. It's complex, time-consuming, and sometimes risky, leading to potential data loss or even downtime. As a result, it's crucial to have a plan that mitigates the risks and streamlines the cloud migration process.

Let's discuss some practical solutions for a seamless cloud migration experience. Whether you're a small business or a large enterprise, these tips can help organizations overcome the hurdles of cloud migration to modernize their data infrastructure.

Identify Your Assets and Then Your Strategy

Even though most data is moving to the cloud, not all data belongs there. To begin, you need to analyze your current IT infrastructure, reviewing hardware, software, applications, and data to determine what is suitable to migrate. This analysis should show any risks or constraints on the process.

Once you understand what you have, you can now identify a strategy that works. There are several cloud migration strategies, including:

■ Rehost - moving your IT infrastructure to the cloud without significantly changing the application or data. This is the fastest and most cost-efficient strategy but fails to take advantage of cloud-native features.

■ Refactor - making some modifications to applications and data to make the most of cloud-native features. This approach takes longer and is more expensive than rehosting, but the benefits are significant and it is less disruptive than rearchitecting or rebuilding.

■ Rearchitect - redesigning existing applications and data for the cloud, which can be time-consuming and expensive but positively impacts performance, scalability, and resilience in the long run.

■ Rebuild - re-creating applications and data from scratch for the cloud to take full advantage of cloud-native features and scalability.

Identifying the right cloud migration strategy is critical to success. A comprehensive analysis will allow you to choose the best process based on your organization's needs, budget, and the complexity of applications and data.

Exploring Your Cloud Platform Options

When considering a cloud migration, leaders must make two major platform decisions: choosing the cloud hosting provider and the actual cloud database.

For cloud service providers, it's vital to focus on scalability, automation capabilities, and feature flexibility. Additionally, compare pricing models that align with your business and budget needs, and ensure high availability, disaster recovery, and data redundancy features for reliability. Finally, rank providers with solid security measures, compliance, and standards to protect your data.

Choose a database that can quickly handle large amounts of data, scale up or down as needed, and ensure reliability through high availability, data replication, backup, and recovery features. Depending on your application's requirements, specialized databases like cloud-native time-series databases or graph databases are also worth considering. Also, consider hybrid cloud as an option for the gradual migration of critical on-premise systems.

Consider Cloud Native

Hosting a database in the cloud does not make it cloud-native. To leverage the benefits of cloud-native architecture, you should prioritize purpose-built databases designed for the cloud environment. They can take advantage of the unique benefits of cloud computing, including flexibility, scalability, elasticity, and reliability.

By leveraging cloud-native architectures, businesses can modernize their IT infrastructure and better respond to rapidly changing business needs. This is particularly important for organizations that must stay competitive in a fast-paced market. One of the key benefits of cloud-native is its ability to support agile development practices such as DevOps. By enabling closer collaboration between developers and operations teams, cloud-native architectures allow teams to develop, deploy, and scale applications more quickly and efficiently.

Cloud-native databases can handle high volumes of data with minimal latency, scale up and down based on your needs, and ensure high availability, data replication, and backup and recovery features for reliability.

Optimize Cloud Resources

If you don't use cloud resources efficiently, they can quickly become expensive. Be smarter by using automated resource scaling and cost management tools, and regularly monitor your cloud resources to identify and address any inefficiencies or overspending.

It's important to consider the costs associated with ingress and egress when moving data to and from the cloud. This can quickly add up, especially as your business scales and you need to move more data. To mitigate these costs, ensure that your chosen database can scale as your business does and has efficient data transfer mechanisms. By being mindful of these factors, you can control your cloud migration costs and ensure that your business is utilizing cloud resources most efficiently.

By leveraging the cloud, businesses can achieve cost savings, improve scalability, and access a broader range of tools, applications, and services. While moving to the cloud can be complex, careful planning, preparation, and execution can ensure a successful migration. Businesses should seek out experienced cloud service providers to navigate through the challenges and discuss the benefits of the cloud for them personally. With the right strategy and support, cloud migration can be easy and let leaders unlock the full potential of their data and drive growth and innovation for years to come.

Jeff Tao is CEO of TDengine

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

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In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Cloud Migration Made Easy: Modernizing Data Infrastructure in the Cloud

Jeff Tao
TDengine

In today's digital world, cloud migration is essential for organizations looking to modernize their data infrastructure. According to Gartner, from 2022, 82% of businesses consider cloud migration an essential part of digital transformation, and by 2025, 51% of IT spending will have shifted from traditional solutions to the public cloud. But the process of migrating to the cloud can be intimidating. It's complex, time-consuming, and sometimes risky, leading to potential data loss or even downtime. As a result, it's crucial to have a plan that mitigates the risks and streamlines the cloud migration process.

Let's discuss some practical solutions for a seamless cloud migration experience. Whether you're a small business or a large enterprise, these tips can help organizations overcome the hurdles of cloud migration to modernize their data infrastructure.

Identify Your Assets and Then Your Strategy

Even though most data is moving to the cloud, not all data belongs there. To begin, you need to analyze your current IT infrastructure, reviewing hardware, software, applications, and data to determine what is suitable to migrate. This analysis should show any risks or constraints on the process.

Once you understand what you have, you can now identify a strategy that works. There are several cloud migration strategies, including:

■ Rehost - moving your IT infrastructure to the cloud without significantly changing the application or data. This is the fastest and most cost-efficient strategy but fails to take advantage of cloud-native features.

■ Refactor - making some modifications to applications and data to make the most of cloud-native features. This approach takes longer and is more expensive than rehosting, but the benefits are significant and it is less disruptive than rearchitecting or rebuilding.

■ Rearchitect - redesigning existing applications and data for the cloud, which can be time-consuming and expensive but positively impacts performance, scalability, and resilience in the long run.

■ Rebuild - re-creating applications and data from scratch for the cloud to take full advantage of cloud-native features and scalability.

Identifying the right cloud migration strategy is critical to success. A comprehensive analysis will allow you to choose the best process based on your organization's needs, budget, and the complexity of applications and data.

Exploring Your Cloud Platform Options

When considering a cloud migration, leaders must make two major platform decisions: choosing the cloud hosting provider and the actual cloud database.

For cloud service providers, it's vital to focus on scalability, automation capabilities, and feature flexibility. Additionally, compare pricing models that align with your business and budget needs, and ensure high availability, disaster recovery, and data redundancy features for reliability. Finally, rank providers with solid security measures, compliance, and standards to protect your data.

Choose a database that can quickly handle large amounts of data, scale up or down as needed, and ensure reliability through high availability, data replication, backup, and recovery features. Depending on your application's requirements, specialized databases like cloud-native time-series databases or graph databases are also worth considering. Also, consider hybrid cloud as an option for the gradual migration of critical on-premise systems.

Consider Cloud Native

Hosting a database in the cloud does not make it cloud-native. To leverage the benefits of cloud-native architecture, you should prioritize purpose-built databases designed for the cloud environment. They can take advantage of the unique benefits of cloud computing, including flexibility, scalability, elasticity, and reliability.

By leveraging cloud-native architectures, businesses can modernize their IT infrastructure and better respond to rapidly changing business needs. This is particularly important for organizations that must stay competitive in a fast-paced market. One of the key benefits of cloud-native is its ability to support agile development practices such as DevOps. By enabling closer collaboration between developers and operations teams, cloud-native architectures allow teams to develop, deploy, and scale applications more quickly and efficiently.

Cloud-native databases can handle high volumes of data with minimal latency, scale up and down based on your needs, and ensure high availability, data replication, and backup and recovery features for reliability.

Optimize Cloud Resources

If you don't use cloud resources efficiently, they can quickly become expensive. Be smarter by using automated resource scaling and cost management tools, and regularly monitor your cloud resources to identify and address any inefficiencies or overspending.

It's important to consider the costs associated with ingress and egress when moving data to and from the cloud. This can quickly add up, especially as your business scales and you need to move more data. To mitigate these costs, ensure that your chosen database can scale as your business does and has efficient data transfer mechanisms. By being mindful of these factors, you can control your cloud migration costs and ensure that your business is utilizing cloud resources most efficiently.

By leveraging the cloud, businesses can achieve cost savings, improve scalability, and access a broader range of tools, applications, and services. While moving to the cloud can be complex, careful planning, preparation, and execution can ensure a successful migration. Businesses should seek out experienced cloud service providers to navigate through the challenges and discuss the benefits of the cloud for them personally. With the right strategy and support, cloud migration can be easy and let leaders unlock the full potential of their data and drive growth and innovation for years to come.

Jeff Tao is CEO of TDengine

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...