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Delphix Partners with AppDynamics

Delphix, the pioneer in programmable data infrastructure, announces the availability of an integrated solution aimed at driving application downtime to zero.

The integration combines production application data from Delphix with a customer’s use of application performance monitoring to accelerate service recovery.

“The most serious application issues are the hardest to reproduce and fix,” said Jedidiah Yueh, CEO of Delphix. “They often involve software, integrated applications, and data. Companies need to be able to automatically reproduce application states prior to, during, or after events occur to get services back online.”

Over the last decade, Delphix has built a data platform that collects data across all enterprise apps, from mainframes to cloud-native, and fuels data for cloud, CI/CD, and AI/ML, and other digital transformation programs. Delphix is building on this foundation by providing an integration between its programmable data infrastructure platform with the application performance monitoring insights from AppDynamics.

Once AppDynamics detects an application issue, the integration solution can trigger Delphix to automatically provision the right databases for the affected application from the right point in time. With this new integration solution, customers can leverage Delphix data provisioning within CI/CD and testing environments to help reproduce issues, perform root cause analysis, develop and test fixes, and drastically shorten the time to restore services.

“It is critical for developers and testers to have access to the right datasets within the right data sources in order to quickly reproduce application data and state-related issues,” said Renato Quedas, director of enterprise architecture and strategy, AppDynamics. “Delphix’s programmable data infrastructure makes it significantly faster for their customers to identify, reproduce, and recover from unexpected application issues.”

The integration delivers critical features to improve production operations and site reliability engineering workflows:

- Data Immutability: An immutable data time machine to recreate data in an application environment before, during, or after an event occurs.

- Automated Data for Environments: The ability to automatically provision data into production support environments, combined with APIs to refresh, clean up, bookmark, branch, and share data.

- Topology Based Provisioning and Event Data Analysis: Delphix receives application topology from AppDynamics to determine what data to provision and sends analytics and event data to AppDynamics for dashboarding and querying.

- Environments Deployed via Application Toolchains: When triggered by AppDynamics, Delphix can be proactively integrated with build and automation tools to quickly provide environments to reproduce and fix issues.

- Data Observability: Ability to identify, track, and resolve data-related application issues, such as data loss, data errors, and malicious changes to data, both within applications and across integrated systems.

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Delphix Partners with AppDynamics

Delphix, the pioneer in programmable data infrastructure, announces the availability of an integrated solution aimed at driving application downtime to zero.

The integration combines production application data from Delphix with a customer’s use of application performance monitoring to accelerate service recovery.

“The most serious application issues are the hardest to reproduce and fix,” said Jedidiah Yueh, CEO of Delphix. “They often involve software, integrated applications, and data. Companies need to be able to automatically reproduce application states prior to, during, or after events occur to get services back online.”

Over the last decade, Delphix has built a data platform that collects data across all enterprise apps, from mainframes to cloud-native, and fuels data for cloud, CI/CD, and AI/ML, and other digital transformation programs. Delphix is building on this foundation by providing an integration between its programmable data infrastructure platform with the application performance monitoring insights from AppDynamics.

Once AppDynamics detects an application issue, the integration solution can trigger Delphix to automatically provision the right databases for the affected application from the right point in time. With this new integration solution, customers can leverage Delphix data provisioning within CI/CD and testing environments to help reproduce issues, perform root cause analysis, develop and test fixes, and drastically shorten the time to restore services.

“It is critical for developers and testers to have access to the right datasets within the right data sources in order to quickly reproduce application data and state-related issues,” said Renato Quedas, director of enterprise architecture and strategy, AppDynamics. “Delphix’s programmable data infrastructure makes it significantly faster for their customers to identify, reproduce, and recover from unexpected application issues.”

The integration delivers critical features to improve production operations and site reliability engineering workflows:

- Data Immutability: An immutable data time machine to recreate data in an application environment before, during, or after an event occurs.

- Automated Data for Environments: The ability to automatically provision data into production support environments, combined with APIs to refresh, clean up, bookmark, branch, and share data.

- Topology Based Provisioning and Event Data Analysis: Delphix receives application topology from AppDynamics to determine what data to provision and sends analytics and event data to AppDynamics for dashboarding and querying.

- Environments Deployed via Application Toolchains: When triggered by AppDynamics, Delphix can be proactively integrated with build and automation tools to quickly provide environments to reproduce and fix issues.

- Data Observability: Ability to identify, track, and resolve data-related application issues, such as data loss, data errors, and malicious changes to data, both within applications and across integrated systems.

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In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...

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 ...

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

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...