Modern Performant Applications Require Modern Storage

Gary Ogasawara
Cloudian

Modern, cloud-native applications have been steadily expanding beyond development environments to on-premises production workloads. For enterprises, one of the primary drivers for making this move has been to ensure performance and avoid the cost and complexity of moving large workloads to the cloud.

As a result, organizations require a modern storage foundation that can fully support cloud-native environments and emerging technologies, such as Kubernetes, serverless computing and microservices which are significant components of these environments.

The following is an easy-to-follow checklist for building the ideal modern storage foundation:

1. S3 Compatibility

Complete S3 compatibility is critical for today's modern storage foundation as it ensures that applications developed for the public cloud can also work seamlessly on-premises. In addition, S3 compatibility simplifies and streamlines the ability to move applications and data across hybrid cloud environments.

2. Performance

High-level, predictable and scalable performance is a must for today's modern storage foundation. This includes the ability to rapidly complete a read or write operation, execute a substantial number of storage operations per second, and provide high data throughput for storage and retrieval in MB/s or GB/s.

3. Scalability

A modern storage foundation must be highly scalable across four dimensions:

■ Throughput scalability - the ability to run more throughput or process more data per second

■ Client scalability - the ability to increase the number of clients or users accessing the storage system

■ Capacity scalability - the ability to grow storage capacity in a single deployment of storage systems

■ Cluster scalability - the ability to grow a storage cluster by deploying additional components

4. Consistency

Consistency is another key element of modern storage. A storage system can be described as "consistent" if read operations promptly return the correct data after it's written, updated or deleted. If new data is immediately available for read operations by clients after it's been changed, the system is "extremely consistent." However, if there is a lag until read operations return the updated data, the system is just "eventually consistent." In this case, the read delay must be considered against the recovery point objective (RPO) because it represents the maximum amount of data loss in the case of component failure.

5. Durability

A modern storage foundation must be durable and protect against data loss. Truly durable platforms ensure that data can be safely stored for extended periods of time. This requires the inclusion of multiple layers of data protection (including support for numerous backup copies) and multiple levels of redundancy (such as local redundancy, redundancy over regions, redundancy over public cloud availability zones and redundancy to a remote site). To be truly durable, storage platforms must also be capable of identifying data corruption and automatically restoring or reconstructing that data. In addition, the specific storage media that comprises a cloud-native storage platform (e.g., SSDs, spinning disks and tapes) should be inherently physically resilient.

6. Deployability

Cloud-native apps are extremely portable and easily distributed across many locations. As a result, it's critical that the storage foundation supporting such apps be capable of being deployed or provisioned on demand. This requires a software-defined, scale-out approach, which enables organizations to immediately grow storage capacity without adding new systems. A storage architecture that leverages a single namespace is ideal here. Because such an architecture connects all nodes together in a peer-to-peer global data fabric, it's possible to add new nodes (and more capacity) on demand across any location using the existing infrastructure.

7. High Availability (HA)

A modern storage foundation must maintain and deliver uninterrupted access to data in the event of a failure, no matter where that failure occurs. To be considered highly available, storage systems should be able to heal and restore any failed components, maintain redundant data copies on a separate device and handle failover to redundant devices/components.

8. Security

Comprehensive end-to-end security is essential for modern storage. This includes encryption for data in flight and at rest, RBAC/IAM and SAML access controls, integrated firewall and certification with stringent government security requirements such as Common Criteria, Federal Information Processing Standard (FIPS) and SEC Rule 17a-4(f). In addition, modern storage foundations should offer data immutability (i.e., ensure the data cannot be changed/altered/deleted for a designated period of time) to protect data and operations from cyberattacks such as ransomware.

Gary Ogasawara is CTO at Cloudian

Related Links

Hot Topics

The Latest

Alert Fatigue Is No Longer a Morale Problem, It's a Reliability Risk and a System Failure

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

Most Enterprises Think They Can Switch AI Vendors in a Month ... Most Who've Tried Couldn't

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Your Observability Stack Has a Telemetry Pipeline Problem

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

Operator to Orchestrator: 80% of IT Pros See Shift in Role as AI Permeates Workflows

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

Gartner: 40% of Organizations Deploying AI Will Use AI Observability to Monitor Model Performance by 2028

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Almost Half of AI-Generated Code Fails in Production

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

AI Requires Design, Not Edicts

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

How Intelligent Orchestration Enables Enterprises to Move from AI POC to AI Production

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Reliability Is the New Bottleneck of Innovation

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

5 Takeaways from the Observability Forecast for Retail and eCommerce

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Modern Performant Applications Require Modern Storage

Gary Ogasawara
Cloudian

Modern, cloud-native applications have been steadily expanding beyond development environments to on-premises production workloads. For enterprises, one of the primary drivers for making this move has been to ensure performance and avoid the cost and complexity of moving large workloads to the cloud.

As a result, organizations require a modern storage foundation that can fully support cloud-native environments and emerging technologies, such as Kubernetes, serverless computing and microservices which are significant components of these environments.

The following is an easy-to-follow checklist for building the ideal modern storage foundation:

1. S3 Compatibility

Complete S3 compatibility is critical for today's modern storage foundation as it ensures that applications developed for the public cloud can also work seamlessly on-premises. In addition, S3 compatibility simplifies and streamlines the ability to move applications and data across hybrid cloud environments.

2. Performance

High-level, predictable and scalable performance is a must for today's modern storage foundation. This includes the ability to rapidly complete a read or write operation, execute a substantial number of storage operations per second, and provide high data throughput for storage and retrieval in MB/s or GB/s.

3. Scalability

A modern storage foundation must be highly scalable across four dimensions:

■ Throughput scalability - the ability to run more throughput or process more data per second

■ Client scalability - the ability to increase the number of clients or users accessing the storage system

■ Capacity scalability - the ability to grow storage capacity in a single deployment of storage systems

■ Cluster scalability - the ability to grow a storage cluster by deploying additional components

4. Consistency

Consistency is another key element of modern storage. A storage system can be described as "consistent" if read operations promptly return the correct data after it's written, updated or deleted. If new data is immediately available for read operations by clients after it's been changed, the system is "extremely consistent." However, if there is a lag until read operations return the updated data, the system is just "eventually consistent." In this case, the read delay must be considered against the recovery point objective (RPO) because it represents the maximum amount of data loss in the case of component failure.

5. Durability

A modern storage foundation must be durable and protect against data loss. Truly durable platforms ensure that data can be safely stored for extended periods of time. This requires the inclusion of multiple layers of data protection (including support for numerous backup copies) and multiple levels of redundancy (such as local redundancy, redundancy over regions, redundancy over public cloud availability zones and redundancy to a remote site). To be truly durable, storage platforms must also be capable of identifying data corruption and automatically restoring or reconstructing that data. In addition, the specific storage media that comprises a cloud-native storage platform (e.g., SSDs, spinning disks and tapes) should be inherently physically resilient.

6. Deployability

Cloud-native apps are extremely portable and easily distributed across many locations. As a result, it's critical that the storage foundation supporting such apps be capable of being deployed or provisioned on demand. This requires a software-defined, scale-out approach, which enables organizations to immediately grow storage capacity without adding new systems. A storage architecture that leverages a single namespace is ideal here. Because such an architecture connects all nodes together in a peer-to-peer global data fabric, it's possible to add new nodes (and more capacity) on demand across any location using the existing infrastructure.

7. High Availability (HA)

A modern storage foundation must maintain and deliver uninterrupted access to data in the event of a failure, no matter where that failure occurs. To be considered highly available, storage systems should be able to heal and restore any failed components, maintain redundant data copies on a separate device and handle failover to redundant devices/components.

8. Security

Comprehensive end-to-end security is essential for modern storage. This includes encryption for data in flight and at rest, RBAC/IAM and SAML access controls, integrated firewall and certification with stringent government security requirements such as Common Criteria, Federal Information Processing Standard (FIPS) and SEC Rule 17a-4(f). In addition, modern storage foundations should offer data immutability (i.e., ensure the data cannot be changed/altered/deleted for a designated period of time) to protect data and operations from cyberattacks such as ransomware.

Gary Ogasawara is CTO at Cloudian

Related Links

Hot Topics

The Latest

Alert Fatigue Is No Longer a Morale Problem, It's a Reliability Risk and a System Failure

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

Most Enterprises Think They Can Switch AI Vendors in a Month ... Most Who've Tried Couldn't

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Your Observability Stack Has a Telemetry Pipeline Problem

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

Operator to Orchestrator: 80% of IT Pros See Shift in Role as AI Permeates Workflows

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

Gartner: 40% of Organizations Deploying AI Will Use AI Observability to Monitor Model Performance by 2028

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Almost Half of AI-Generated Code Fails in Production

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

AI Requires Design, Not Edicts

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

How Intelligent Orchestration Enables Enterprises to Move from AI POC to AI Production

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Reliability Is the New Bottleneck of Innovation

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

5 Takeaways from the Observability Forecast for Retail and eCommerce

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...