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Datadog Launches Certification Program

Datadog announced the launch of its Datadog Certification Program, building on the Datadog Learning Center to help developers further uplevel their observability skills.

The Datadog Certification Program offers professionals a path to build and demonstrate their knowledge of both Datadog’s platform and industry best practices. The program also allows partners to showcase their Datadog competency to potential customers.

By achieving certifications in Datadog, Log Management and APM fundamentals, cloud professionals can validate their practiced competency in monitoring with Datadog while also earning badges based on the exams they’ve completed. Successful certification signals to peers and employers that they have proficiency in the industry’s leading observability platform and have demonstrated the ability to apply that knowledge.

“These exams were purpose-built to test users for the skills and knowledge that DevOps and observability teams value most,” said Jeremy Garcia, Senior Director of Technical Community and Open Source at Datadog. “Our goal with this program is to help cloud professionals showcase their experience with observability and the Datadog platform to ultimately help them grow their careers.”

The three certifications available at launch include:

- Datadog Fundamentals: This exam is for entry-level users who can exhibit a working knowledge of Datadog’s products and common use cases, including how the Datadog ecosystem works and how to use the platform effectively. Expected competencies include Linux basics, common scripting languages and other computer fundamentals, along with Datadog Agent configuration and commands. Attendees of Dash, Datadog’s annual conference, can take the Datadog Fundamentals exam onsite at the event.

- Log Management Fundamentals: This exam is for individuals with at least six months of experience using Datadog. This certification tests for users’ functional knowledge of log collection in Datadog, including how to use log parsing and associated rules, how to search and filter logs and how to use log analytics for troubleshooting.

- APM & Distributed Tracing Fundamentals: Like the Log Management Fundamentals course, this exam requires at least six months of experience with the Datadog platform and validates the user’s understanding of different tracing architectures and their ability to instrument applications manually and use Datadog APM features.

The Datadog Certification Program is proctored by third-party provider PSI, a global leader in delivering best-in-class assessment content through technology.

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Datadog Launches Certification Program

Datadog announced the launch of its Datadog Certification Program, building on the Datadog Learning Center to help developers further uplevel their observability skills.

The Datadog Certification Program offers professionals a path to build and demonstrate their knowledge of both Datadog’s platform and industry best practices. The program also allows partners to showcase their Datadog competency to potential customers.

By achieving certifications in Datadog, Log Management and APM fundamentals, cloud professionals can validate their practiced competency in monitoring with Datadog while also earning badges based on the exams they’ve completed. Successful certification signals to peers and employers that they have proficiency in the industry’s leading observability platform and have demonstrated the ability to apply that knowledge.

“These exams were purpose-built to test users for the skills and knowledge that DevOps and observability teams value most,” said Jeremy Garcia, Senior Director of Technical Community and Open Source at Datadog. “Our goal with this program is to help cloud professionals showcase their experience with observability and the Datadog platform to ultimately help them grow their careers.”

The three certifications available at launch include:

- Datadog Fundamentals: This exam is for entry-level users who can exhibit a working knowledge of Datadog’s products and common use cases, including how the Datadog ecosystem works and how to use the platform effectively. Expected competencies include Linux basics, common scripting languages and other computer fundamentals, along with Datadog Agent configuration and commands. Attendees of Dash, Datadog’s annual conference, can take the Datadog Fundamentals exam onsite at the event.

- Log Management Fundamentals: This exam is for individuals with at least six months of experience using Datadog. This certification tests for users’ functional knowledge of log collection in Datadog, including how to use log parsing and associated rules, how to search and filter logs and how to use log analytics for troubleshooting.

- APM & Distributed Tracing Fundamentals: Like the Log Management Fundamentals course, this exam requires at least six months of experience with the Datadog platform and validates the user’s understanding of different tracing architectures and their ability to instrument applications manually and use Datadog APM features.

The Datadog Certification Program is proctored by third-party provider PSI, a global leader in delivering best-in-class assessment content through technology.

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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 ...