Transforming Log Management with Object Storage

Stela Udovicic
Era Software

Logs produced by your IT infrastructure contain hidden gems — information about performance, user behavior, and other data waiting to be discovered. Unlocking the value of the array of log data aggregated by organizations every day can be a gateway to uncovering all manner of efficiencies. Yet, the challenge of analyzing and managing the mountains of log data organizations have is growing more complex by the day.

Cloud adoption, application modernization, and other technology trends have put pressure on log management solutions to support a diverse infrastructure generating log data that can reach petabyte scale and beyond. As the volume of data spikes, the cost of ingesting, storing, and analyzing it does as well. Traditional log management solutions cannot keep pace with the demands of the environments many organizations are now responsible for, which forces IT teams to make decisions about log collection and retention that can hamper their ability to get the most value out of the data.

Whether they choose to buy or build their solution, the same challenges remain. The decision to develop their own solutions based on open-source tools brings new demands to allocate the engineering resources needed to maintain them. Homegrown or not, legacy architectures designed without the cloud in mind cannot handle the necessary volume of data.

This new reality requires a new approach, one that can handle the scalability, access, and analysis needs of the modern digital-minded enterprises.

A New Architecture for a New Day

Digital transformation has become more than just a buzzword; it is a concept that has touched essentially every aspect of business and IT operations. Log management is no exception. In the face of DevOps, cloud computing, and an ever-growing tsunami of structured and unstructured data, organizations have no choice but to adjust their approach to meet the needs of their increasingly cloud-first and hybrid infrastructure.

The explosion of data creates issues that cannot be solved by simply adding more storage, compute, or nodes. At certain scales, it simply becomes cost-prohibitive. The tactical impact of this reality is that it leaves insights that can be potentially gleaned from that data on the table. For example, we have seen some organizations place quotas on the logs for their DevOps teams, which can slow release cycles as developers wait for performance-related logs. This situation is a recipe for creating friction. Log management needs to be a service that reduces complexity, not an impediment to velocity or IT operations.

Increasing cost is not the only challenge facing log management for many organizations. The sheer amount of data can also make effective indexing impossible, further hurting historical data analysis and visibility. What organizations need is a way to index and analyze data in real-time and with the level of scalability they require. The larger the amount of data organizations want to regularly access is, the more capacity they will need for their hot storage tier and the higher the cost.

Object Storage Removes Scale and Cost Significant Barriers

In an ideal world, organizations would not have to make cost-driven decisions including setting quotas on what logs to send to cold storage. However, the reality many organizations face is one where compute and storage are tightly coupled, increasing the price tag attached to log management.

Separating storage and compute, however, gives organizations the scalability and flexibility to address the needs of their hybrid and cloud infrastructure. Object storage manages data as objects, eliminating the hierarchical file structure of traditional databases. Log management solutions built on top of object storage eliminate the need to manage data within storage clusters or resize it manually. Each object is organized using unique identifiers and includes customizable metadata that allows for much richer analysis. All data can be accessed via an API or UI making objects easier to query and find, and queries, reads, and writes can happen almost instantaneously.

This approach makes it easier for organizations to search out — and quickly get value from — relevant information and historical logs. The result is faster, highly optimized search queries that deliver accurate insights for high-volume log data. This capability should be further supported by analytics-driven alerting that enables organizations to proactively detect and resolve any application, infrastructure, operational, or code issue quickly. By utilizing machine learning, log management solutions can augment troubleshooting efforts by IT teams, uncovering problems by correlating and examining information about the logs in your environment.

These facts are only scratching the surface in the ways next-generation log management platforms can be transformative. Organizations need to feel secure that their log management strategy will not crumble under the stress of their IT environment. Solutions that are built using cloud-native constructs can enable each storage tier to scale up or down as needed, addressing the scalability and elasticity concerns created by the massive amounts of data from containers, microservices, Internet-of-Things (IoT) devices, and other sources.

All this, of course, must be done without compromising data hygiene. The durability of object storage is typically touted as 11 nines durable (99.999999999), which is achieved through redundancy and the use of metadata to identify any corruption. Through the use of synchronized caching, log management platforms can ensure the creation and maintenance of a single source of truth for log data throughout the environment.

Transforming Log Management

In the digital world, yesterday's solutions almost always reach a point where they can no longer solve today's problems. And tomorrow's problems? Not likely.

To address the challenges posed by today's complex IT environments requires rethinking log management for cloud-scale infrastructure. Whatever approach organizations adopt needs to deliver the flexibility and scalability necessary to deal with massive amounts of data generated. Every piece of log data can have a value if properly analyzed but realizing that potential may require IT leaders to rethink how log management is architected.

Observability has become a cornerstone of modern IT organizations, but the biggest challenge is to keep data organized so you can retrieve it efficiently. Legacy approaches have reached their breaking point. As data volumes continue to grow, the key to unlocking business value from that data will reside in adopting a strategy optimized for the cloud and the scalability needs of the modern business. Only when enterprises solve the log management conundrum will they be able to fully take advantage to improve operational efficiency, improve customer experiences to build loyalty and deliver new revenue streams to increase profitability.

Stela Udovicic is SVP, Marketing, at Era Software

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Transforming Log Management with Object Storage

Stela Udovicic
Era Software

Logs produced by your IT infrastructure contain hidden gems — information about performance, user behavior, and other data waiting to be discovered. Unlocking the value of the array of log data aggregated by organizations every day can be a gateway to uncovering all manner of efficiencies. Yet, the challenge of analyzing and managing the mountains of log data organizations have is growing more complex by the day.

Cloud adoption, application modernization, and other technology trends have put pressure on log management solutions to support a diverse infrastructure generating log data that can reach petabyte scale and beyond. As the volume of data spikes, the cost of ingesting, storing, and analyzing it does as well. Traditional log management solutions cannot keep pace with the demands of the environments many organizations are now responsible for, which forces IT teams to make decisions about log collection and retention that can hamper their ability to get the most value out of the data.

Whether they choose to buy or build their solution, the same challenges remain. The decision to develop their own solutions based on open-source tools brings new demands to allocate the engineering resources needed to maintain them. Homegrown or not, legacy architectures designed without the cloud in mind cannot handle the necessary volume of data.

This new reality requires a new approach, one that can handle the scalability, access, and analysis needs of the modern digital-minded enterprises.

A New Architecture for a New Day

Digital transformation has become more than just a buzzword; it is a concept that has touched essentially every aspect of business and IT operations. Log management is no exception. In the face of DevOps, cloud computing, and an ever-growing tsunami of structured and unstructured data, organizations have no choice but to adjust their approach to meet the needs of their increasingly cloud-first and hybrid infrastructure.

The explosion of data creates issues that cannot be solved by simply adding more storage, compute, or nodes. At certain scales, it simply becomes cost-prohibitive. The tactical impact of this reality is that it leaves insights that can be potentially gleaned from that data on the table. For example, we have seen some organizations place quotas on the logs for their DevOps teams, which can slow release cycles as developers wait for performance-related logs. This situation is a recipe for creating friction. Log management needs to be a service that reduces complexity, not an impediment to velocity or IT operations.

Increasing cost is not the only challenge facing log management for many organizations. The sheer amount of data can also make effective indexing impossible, further hurting historical data analysis and visibility. What organizations need is a way to index and analyze data in real-time and with the level of scalability they require. The larger the amount of data organizations want to regularly access is, the more capacity they will need for their hot storage tier and the higher the cost.

Object Storage Removes Scale and Cost Significant Barriers

In an ideal world, organizations would not have to make cost-driven decisions including setting quotas on what logs to send to cold storage. However, the reality many organizations face is one where compute and storage are tightly coupled, increasing the price tag attached to log management.

Separating storage and compute, however, gives organizations the scalability and flexibility to address the needs of their hybrid and cloud infrastructure. Object storage manages data as objects, eliminating the hierarchical file structure of traditional databases. Log management solutions built on top of object storage eliminate the need to manage data within storage clusters or resize it manually. Each object is organized using unique identifiers and includes customizable metadata that allows for much richer analysis. All data can be accessed via an API or UI making objects easier to query and find, and queries, reads, and writes can happen almost instantaneously.

This approach makes it easier for organizations to search out — and quickly get value from — relevant information and historical logs. The result is faster, highly optimized search queries that deliver accurate insights for high-volume log data. This capability should be further supported by analytics-driven alerting that enables organizations to proactively detect and resolve any application, infrastructure, operational, or code issue quickly. By utilizing machine learning, log management solutions can augment troubleshooting efforts by IT teams, uncovering problems by correlating and examining information about the logs in your environment.

These facts are only scratching the surface in the ways next-generation log management platforms can be transformative. Organizations need to feel secure that their log management strategy will not crumble under the stress of their IT environment. Solutions that are built using cloud-native constructs can enable each storage tier to scale up or down as needed, addressing the scalability and elasticity concerns created by the massive amounts of data from containers, microservices, Internet-of-Things (IoT) devices, and other sources.

All this, of course, must be done without compromising data hygiene. The durability of object storage is typically touted as 11 nines durable (99.999999999), which is achieved through redundancy and the use of metadata to identify any corruption. Through the use of synchronized caching, log management platforms can ensure the creation and maintenance of a single source of truth for log data throughout the environment.

Transforming Log Management

In the digital world, yesterday's solutions almost always reach a point where they can no longer solve today's problems. And tomorrow's problems? Not likely.

To address the challenges posed by today's complex IT environments requires rethinking log management for cloud-scale infrastructure. Whatever approach organizations adopt needs to deliver the flexibility and scalability necessary to deal with massive amounts of data generated. Every piece of log data can have a value if properly analyzed but realizing that potential may require IT leaders to rethink how log management is architected.

Observability has become a cornerstone of modern IT organizations, but the biggest challenge is to keep data organized so you can retrieve it efficiently. Legacy approaches have reached their breaking point. As data volumes continue to grow, the key to unlocking business value from that data will reside in adopting a strategy optimized for the cloud and the scalability needs of the modern business. Only when enterprises solve the log management conundrum will they be able to fully take advantage to improve operational efficiency, improve customer experiences to build loyalty and deliver new revenue streams to increase profitability.

Stela Udovicic is SVP, Marketing, at Era Software

Related Links

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Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

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Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

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If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

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Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

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Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

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Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

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Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

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