Q&A: HP Talks About App Development and DevOps - Part 3

Pete Goldin
APMdigest

In Part 3 of APMdigest's exclusive interview, John Jeremiah, Technology Evangelist for HP's Software Research Group, outlines the future of application development.

Start with Part 1 of the interview

Start with Part 2 of the interview

APM: Tell me about HP Software's portfolio of app development and ops tools.

JJ: HP delivers a comprehensive suite of products and services to facilitate the entire application lifecycle, including products for developers, testers and IT ops teams. Going back to HP's Four Transformative Areas, in addition to empowering the data-driven organization, these areas include protecting the digital enterprise; enabling workplace productivity; and transforming the hybrid infrastructure. Each of these areas can be incorporated into application development for added benefit.

For example, HP's Application Lifecycle suite of tools provides agile/DevOps teams a collaborative platform in Agile Manager; and automated and continuous testing with HP LeanFT and HP LoadRunner. Integrating HP's security solutions, namely Fortify, properly incorporates security in the DevOps delivery process.

Since speed and velocity are vital in a DevOps team, being able to virtualize infrastructure and services can greatly accelerate delivery and this is where both Service Virtualization and Network Virtualization are indispensable.

Regarding the data-driven organization, HP's Haven platform offers advanced data analytics in a SaaS model, enabling DevOps teams to analyze and derive actionable insights from development and production data.

Transforming the hybrid infrastructure entails creating a responsive, dynamic environment — comprising on-premise as well as cloud platforms — that can adapt quickly to changing application needs. Using HP's Helion Cloud, in concert with (or without) one's own platforms, gives DevOps teams maximum flexibility to run various jobs and apps where they need to be run, depending on factors like time-sensitivity and the level of required computing power.

Additionally, numerous testing tools like load testing are available in HP StormRunner Load, and they allow DevOps teams to test using load generated from other clouds as well. This allows DevOps teams to test application performance under load for various geographies and end-user segments.

Finally, DevOps teams need to have their finger on the pulse of what users are experiencing. Only with insightful feedback are they able to react, respond and deliver world class apps. HP's AppPulse family of products gives developers exactly this kind of insight, leveraging big data analytics to provide actionable insight.

APM: Can you give an example of HP's value proposition in action?

JJ: Consider the case of SpeechTrans, a leading speech translation company. SpeechTrans uses HP AppPulse mobile to apply Big Data analytics to end-user experience data — for example, what screens are end-users getting stuck on the most; where are end users exiting the app — so developers can focus specifically on improving these app areas.

Previously, it took SpeechTrans up to three weeks to fix an app issue; now, they can address most problems in less than two days. This approach has enabled SpeechTrans to achieve consistent 5-star ratings in mobile app stores, a key driver of new customers and revenues.

APM: Where do you see application development headed next?

JJ: We don't foresee the relentless focus on app velocity and quality abating anytime soon. Consider mobile apps in particular. According to Rubin Research, in 2004, the average mobile user executed one mobile transaction per day. Today, that number is 37, and by 2025, the number of mobile transactions conducted around the world will increase 5x. In addition, the number of smartphone users around the world is exploding. It's a land-grab right now, and whoever rolls out the best applications the fastest, will win. We expect organizations will get creative, using technologies like Big Data analytics to glean critical insights for identifying development roadblocks, enhancing processes and fine-tuning apps in production.

APM: Are there new upcoming challenges on the horizon that developers must look out for?

JJ: Competition will not only increase, but it will do so on a global scale, as regions like China and India explode for consumer e-commerce and smartphone use. DevOps teams will need to be able to test and roll-out applications quickly, across more geographies and device types.

In addition, security is growing as a development challenge. The pace of DevOps means that developers are often using pre-existing code, which may have security vulnerabilities. In reality, security is a fourth component of application quality, and it must become a fourth leg of the DevOps stool. Security audits and validating security requirements must keep up with the speed of DevOps. For this reason, like testing, basic security audits could benefit from automation as part of the DevOps process.

APM: How do you see big data and analytics playing a bigger role in application development going forward?

JJ: Moving forward, Big Data and analytics will play an increasingly important role in application development – both in terms of refining and improving development processes, as well as improving application functionality, end-user performance and resource-efficiency.

Consider this example – a DevOps team may analyze social sentiment as a means of determining what new functionality end users want most. Or, an IT Ops team may identify usage patterns for an app – for example, when during the day does an application come under heavy load, and when does this load taper off. This enables development teams to code applications accordingly – for example, architecting applications to accommodate longer or shorter queues (depending on time of day) which can free up CPU resources.

Finally, a testing team may detect that problem with a certain app function only occurs on a specific browser type. By correlating this with an information breakdown on end-user profiles, they may determine if users of this browser type are prevalent enough to warrant the time and effort required for a fix. With Big Data, the possibilities are truly endless, and we've only just started.

ABOUT John Jeremiah

John Jeremiah is a Technology Evangelist for HP’s Software Research Group. His background as a software developer and IT leader with over 20 years of experience includes roles in IT consulting, at Fortune 500 IT organizations, and with the US Navy. His previous positions span from application developer, project, and program manager, Director of Testing Services, and Delivery Process and Methodology Director.

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Q&A: HP Talks About App Development and DevOps - Part 3

Pete Goldin
APMdigest

In Part 3 of APMdigest's exclusive interview, John Jeremiah, Technology Evangelist for HP's Software Research Group, outlines the future of application development.

Start with Part 1 of the interview

Start with Part 2 of the interview

APM: Tell me about HP Software's portfolio of app development and ops tools.

JJ: HP delivers a comprehensive suite of products and services to facilitate the entire application lifecycle, including products for developers, testers and IT ops teams. Going back to HP's Four Transformative Areas, in addition to empowering the data-driven organization, these areas include protecting the digital enterprise; enabling workplace productivity; and transforming the hybrid infrastructure. Each of these areas can be incorporated into application development for added benefit.

For example, HP's Application Lifecycle suite of tools provides agile/DevOps teams a collaborative platform in Agile Manager; and automated and continuous testing with HP LeanFT and HP LoadRunner. Integrating HP's security solutions, namely Fortify, properly incorporates security in the DevOps delivery process.

Since speed and velocity are vital in a DevOps team, being able to virtualize infrastructure and services can greatly accelerate delivery and this is where both Service Virtualization and Network Virtualization are indispensable.

Regarding the data-driven organization, HP's Haven platform offers advanced data analytics in a SaaS model, enabling DevOps teams to analyze and derive actionable insights from development and production data.

Transforming the hybrid infrastructure entails creating a responsive, dynamic environment — comprising on-premise as well as cloud platforms — that can adapt quickly to changing application needs. Using HP's Helion Cloud, in concert with (or without) one's own platforms, gives DevOps teams maximum flexibility to run various jobs and apps where they need to be run, depending on factors like time-sensitivity and the level of required computing power.

Additionally, numerous testing tools like load testing are available in HP StormRunner Load, and they allow DevOps teams to test using load generated from other clouds as well. This allows DevOps teams to test application performance under load for various geographies and end-user segments.

Finally, DevOps teams need to have their finger on the pulse of what users are experiencing. Only with insightful feedback are they able to react, respond and deliver world class apps. HP's AppPulse family of products gives developers exactly this kind of insight, leveraging big data analytics to provide actionable insight.

APM: Can you give an example of HP's value proposition in action?

JJ: Consider the case of SpeechTrans, a leading speech translation company. SpeechTrans uses HP AppPulse mobile to apply Big Data analytics to end-user experience data — for example, what screens are end-users getting stuck on the most; where are end users exiting the app — so developers can focus specifically on improving these app areas.

Previously, it took SpeechTrans up to three weeks to fix an app issue; now, they can address most problems in less than two days. This approach has enabled SpeechTrans to achieve consistent 5-star ratings in mobile app stores, a key driver of new customers and revenues.

APM: Where do you see application development headed next?

JJ: We don't foresee the relentless focus on app velocity and quality abating anytime soon. Consider mobile apps in particular. According to Rubin Research, in 2004, the average mobile user executed one mobile transaction per day. Today, that number is 37, and by 2025, the number of mobile transactions conducted around the world will increase 5x. In addition, the number of smartphone users around the world is exploding. It's a land-grab right now, and whoever rolls out the best applications the fastest, will win. We expect organizations will get creative, using technologies like Big Data analytics to glean critical insights for identifying development roadblocks, enhancing processes and fine-tuning apps in production.

APM: Are there new upcoming challenges on the horizon that developers must look out for?

JJ: Competition will not only increase, but it will do so on a global scale, as regions like China and India explode for consumer e-commerce and smartphone use. DevOps teams will need to be able to test and roll-out applications quickly, across more geographies and device types.

In addition, security is growing as a development challenge. The pace of DevOps means that developers are often using pre-existing code, which may have security vulnerabilities. In reality, security is a fourth component of application quality, and it must become a fourth leg of the DevOps stool. Security audits and validating security requirements must keep up with the speed of DevOps. For this reason, like testing, basic security audits could benefit from automation as part of the DevOps process.

APM: How do you see big data and analytics playing a bigger role in application development going forward?

JJ: Moving forward, Big Data and analytics will play an increasingly important role in application development – both in terms of refining and improving development processes, as well as improving application functionality, end-user performance and resource-efficiency.

Consider this example – a DevOps team may analyze social sentiment as a means of determining what new functionality end users want most. Or, an IT Ops team may identify usage patterns for an app – for example, when during the day does an application come under heavy load, and when does this load taper off. This enables development teams to code applications accordingly – for example, architecting applications to accommodate longer or shorter queues (depending on time of day) which can free up CPU resources.

Finally, a testing team may detect that problem with a certain app function only occurs on a specific browser type. By correlating this with an information breakdown on end-user profiles, they may determine if users of this browser type are prevalent enough to warrant the time and effort required for a fix. With Big Data, the possibilities are truly endless, and we've only just started.

ABOUT John Jeremiah

John Jeremiah is a Technology Evangelist for HP’s Software Research Group. His background as a software developer and IT leader with over 20 years of experience includes roles in IT consulting, at Fortune 500 IT organizations, and with the US Navy. His previous positions span from application developer, project, and program manager, Director of Testing Services, and Delivery Process and Methodology Director.

Related Links

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

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