The Future of Big Data APM in 2022

Recent data shows that the global APM (application performance management) market is booming. Currently valued at $6.3 billion, the global APM industry is expected to reach $12 billion by 2026. This growth indicates the increasing importance of monitoring, diagnosing, and improving application performance.

Visibility and automation is key to sustaining the growth and evolution of APM. Speaking to APMdigest, Pepperdata CEO Ash Munshi delved deeper into why he thinks visibility and automation are the future of APM well into 2022.

The Big Shift to the Cloud Continues

By the end of 2021, nearly 70% of all enterprises' infrastructure was slated to become cloud based. By now, more than 80% of business organizations say they already have implemented or are planning to implement a multi-cloud strategy. In addition, 82% of workloads will be moved to the cloud.

Drivers of this shift include the ubiquity of mobile devices, as well as the growing need for online collaboration, remote work, and new digital services to support a hybrid workforce. Recent disruptions and the need to accelerate digital transformation hard pressed 90% of enterprises to increase their cloud usage.

Ash Munshi expects to see this trend continue to expand in 2022. Global cloud adoption will continue on a very rapid and massive scale in the immediate future. Global spend on public cloud services will go over $480 billion in 2022.

The Cloud's Complexity Will Grow, Too

"In the beginning, the cloud made everything easier. However, cloud complexity has increased dramatically," Ash Munshi told APMdigest. Enterprises are forced to increase their spending because they failed to anticipate the extra capacity needed to run their current cloud-based applications. As their cloud usage intensifies, so do the compute requirements of their applications and workloads.

Some key reasons why organizations accelerate their cloud migration are reducing their headcount, eliminating the difficulties of accessing data center facilities, and avoiding hardware supply chain delays.

However, many enterprises fail to realize how extremely complex cloud computing is. This has caused organizations to exceed their cloud budget by as much as 40%. In very extreme cases, enterprises that can't handle the challenges of cloud computing are forced to repatriate workloads and applications to their previous settings.

Adding to the cloud's complexity is the daunting number of application stack choices. Enterprises not only struggle to pick the right cloud vendor and the ideal application stack to run, but the current crop of APM solutions are also lacking the visibility and depth needed to help them fully optimize their cloud infrastructure, improve the performance of their big data stacks, and enjoy the promised benefits of cloud computing.

"For compute, there are over 400 different instance types on AWS alone. Add on to that a hybrid solution, and the choices companies need to make to move their data and application explodes," Munshi lamented. Managing application performance in the cloud while staying within their budget is also a formidable challenge for many enterprises.

The Future of APM

The increasing ubiquity of cloud computing and big data, along with its growing complications, necessitate a big overhaul in approach.

"Our approach to the cloud and application performance management must change in response," Ash emphasized.

According to Pepperdata's recent survey, approximately 42% of enterprises rely on their cloud vendors' solutions to monitor their cloud processes and manage their application performance. But this is problematic, as most APM tools are designed to track and measure surface metrics. These solutions don't have the depth and granularity needed by enterprises to look at the application level and truly perform powerful resource allocation and performance optimization at scale.

But Ash Munshi believes that enterprises will recognize this stumbling block and evolve their approach to APM.

The Impact of Visibility and Automation on APM

Most APM tools on the market don't have comprehensive, application-level visibility. When you can't see into your applications, their performance, resource utilization, and more, you can't gain deeper context into your applications. Your big data stack in the cloud is riddled with blind spots.

In one of our earlier surveys, 64% of enterprises highlighted "cost management and containment" as their biggest, most pressing concern with running cloud big data stacks and applications. On top of that, the majority of respondents said they wanted to "better optimize current cloud resources."

"This research shows us the importance of visibility into big data workloads. It also highlights the need for automated optimization as a means to control runaway costs," Ash stressed. APM tools that provide users with visibility and automated optimization help in getting their cloud costs under control.

"Future APM solutions will no longer be just about debugging and tuning on an application-by-application basis," Ash told APMDigest. "The future of application performance management needs visibility and automation to manage your compute, software stack, and ensure that your costs are within budget."

Hot Topics

The Latest

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

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

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

Empowering the Human Side of the Autonomous IT Age

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 Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

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 Future of Big Data APM in 2022

Recent data shows that the global APM (application performance management) market is booming. Currently valued at $6.3 billion, the global APM industry is expected to reach $12 billion by 2026. This growth indicates the increasing importance of monitoring, diagnosing, and improving application performance.

Visibility and automation is key to sustaining the growth and evolution of APM. Speaking to APMdigest, Pepperdata CEO Ash Munshi delved deeper into why he thinks visibility and automation are the future of APM well into 2022.

The Big Shift to the Cloud Continues

By the end of 2021, nearly 70% of all enterprises' infrastructure was slated to become cloud based. By now, more than 80% of business organizations say they already have implemented or are planning to implement a multi-cloud strategy. In addition, 82% of workloads will be moved to the cloud.

Drivers of this shift include the ubiquity of mobile devices, as well as the growing need for online collaboration, remote work, and new digital services to support a hybrid workforce. Recent disruptions and the need to accelerate digital transformation hard pressed 90% of enterprises to increase their cloud usage.

Ash Munshi expects to see this trend continue to expand in 2022. Global cloud adoption will continue on a very rapid and massive scale in the immediate future. Global spend on public cloud services will go over $480 billion in 2022.

The Cloud's Complexity Will Grow, Too

"In the beginning, the cloud made everything easier. However, cloud complexity has increased dramatically," Ash Munshi told APMdigest. Enterprises are forced to increase their spending because they failed to anticipate the extra capacity needed to run their current cloud-based applications. As their cloud usage intensifies, so do the compute requirements of their applications and workloads.

Some key reasons why organizations accelerate their cloud migration are reducing their headcount, eliminating the difficulties of accessing data center facilities, and avoiding hardware supply chain delays.

However, many enterprises fail to realize how extremely complex cloud computing is. This has caused organizations to exceed their cloud budget by as much as 40%. In very extreme cases, enterprises that can't handle the challenges of cloud computing are forced to repatriate workloads and applications to their previous settings.

Adding to the cloud's complexity is the daunting number of application stack choices. Enterprises not only struggle to pick the right cloud vendor and the ideal application stack to run, but the current crop of APM solutions are also lacking the visibility and depth needed to help them fully optimize their cloud infrastructure, improve the performance of their big data stacks, and enjoy the promised benefits of cloud computing.

"For compute, there are over 400 different instance types on AWS alone. Add on to that a hybrid solution, and the choices companies need to make to move their data and application explodes," Munshi lamented. Managing application performance in the cloud while staying within their budget is also a formidable challenge for many enterprises.

The Future of APM

The increasing ubiquity of cloud computing and big data, along with its growing complications, necessitate a big overhaul in approach.

"Our approach to the cloud and application performance management must change in response," Ash emphasized.

According to Pepperdata's recent survey, approximately 42% of enterprises rely on their cloud vendors' solutions to monitor their cloud processes and manage their application performance. But this is problematic, as most APM tools are designed to track and measure surface metrics. These solutions don't have the depth and granularity needed by enterprises to look at the application level and truly perform powerful resource allocation and performance optimization at scale.

But Ash Munshi believes that enterprises will recognize this stumbling block and evolve their approach to APM.

The Impact of Visibility and Automation on APM

Most APM tools on the market don't have comprehensive, application-level visibility. When you can't see into your applications, their performance, resource utilization, and more, you can't gain deeper context into your applications. Your big data stack in the cloud is riddled with blind spots.

In one of our earlier surveys, 64% of enterprises highlighted "cost management and containment" as their biggest, most pressing concern with running cloud big data stacks and applications. On top of that, the majority of respondents said they wanted to "better optimize current cloud resources."

"This research shows us the importance of visibility into big data workloads. It also highlights the need for automated optimization as a means to control runaway costs," Ash stressed. APM tools that provide users with visibility and automated optimization help in getting their cloud costs under control.

"Future APM solutions will no longer be just about debugging and tuning on an application-by-application basis," Ash told APMDigest. "The future of application performance management needs visibility and automation to manage your compute, software stack, and ensure that your costs are within budget."

Hot Topics

The Latest

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

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

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

Empowering the Human Side of the Autonomous IT Age

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 Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

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.