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Datadog Announces Integration with Amazon Elastic File System for AWS Lambda

Datadog announced support for Amazon Elastic File System (Amazon EFS) for AWS Lambda on Amazon Web Services (AWS).

This new integration is now available with the launch of Amazon EFS for AWS Lambda.

Datadog’s integration with Amazon EFS for AWS Lambda brings single-click correlation between AWS Lambda and the underlying Elastic File System. This allows developers to have visibility across the serverless components that power their business and troubleshoot potential issues quickly.

“Datadog is committed to delivering an end-to-end monitoring solution for environments adopting to evolving serverless use cases,” said Ilan Rabinovitch, VP, Product & Community at Datadog. “Visibility into the health of Amazon EFS is critical for these newer serverless use cases. By combining this data with Datadog’s serverless monitoring, our customers are able to accelerate their adoption of serverless workflows with confidence.”

Ajay Nair, Director of Product Management, AWS Lambda, at Amazon Web Services said: “As our shared customers can take advantage of the new persistent file system functionality to enable new application patterns, they will do so with support in their preferred monitoring tool.”

Monitoring Amazon EFS for AWS Lambda is included as part of Datadog Serverless Monitoring, which provides engineering teams with end-to-end visibility across their serverless infrastructure by bringing together correlated metrics, traces, and logs. Developers now have access to enhanced tagging to slice and dice Lambda metrics by the mounted Elastic File System and a new default out-of-the-box EFS dashboard to monitor key metrics and computation costs.

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Datadog Announces Integration with Amazon Elastic File System for AWS Lambda

Datadog announced support for Amazon Elastic File System (Amazon EFS) for AWS Lambda on Amazon Web Services (AWS).

This new integration is now available with the launch of Amazon EFS for AWS Lambda.

Datadog’s integration with Amazon EFS for AWS Lambda brings single-click correlation between AWS Lambda and the underlying Elastic File System. This allows developers to have visibility across the serverless components that power their business and troubleshoot potential issues quickly.

“Datadog is committed to delivering an end-to-end monitoring solution for environments adopting to evolving serverless use cases,” said Ilan Rabinovitch, VP, Product & Community at Datadog. “Visibility into the health of Amazon EFS is critical for these newer serverless use cases. By combining this data with Datadog’s serverless monitoring, our customers are able to accelerate their adoption of serverless workflows with confidence.”

Ajay Nair, Director of Product Management, AWS Lambda, at Amazon Web Services said: “As our shared customers can take advantage of the new persistent file system functionality to enable new application patterns, they will do so with support in their preferred monitoring tool.”

Monitoring Amazon EFS for AWS Lambda is included as part of Datadog Serverless Monitoring, which provides engineering teams with end-to-end visibility across their serverless infrastructure by bringing together correlated metrics, traces, and logs. Developers now have access to enhanced tagging to slice and dice Lambda metrics by the mounted Elastic File System and a new default out-of-the-box EFS dashboard to monitor key metrics and computation costs.

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Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

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 ...