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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."

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Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

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IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

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Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

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

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...