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Datadog Announces Support for Streaming Logs with Amazon Kinesis Data Firehose

Datadog announced support for ingesting log data via Amazon Kinesis Data Firehose, a solution from Amazon Web Services (AWS) that reliably loads streaming data into data lakes, data stores and analytics tools.

Customers can now send logs from Amazon CloudWatch and other services to Datadog without setting up and managing log forwarders in their environment.

Amazon Kinesis Data Firehose receives logs from services such as Amazon CloudWatch, Amazon API Gateway, AWS Lambda, and Amazon Elastic Compute Cloud (Amazon EC2) in one place, and routes them to third-party tools and systems. With support for Amazon Kinesis Data Firehose, Datadog allows customers to stream logs from AWS services to Datadog directly from the AWS Management Console and APIs.

This new capability will enable analysis of AWS log data within Datadog by utilizing new and existing Log Management features, including:

● Real-time visibility with Live Tail and dynamic indexing based on easy-to-create filters with Datadog’s Logging without Limits™.

● Automatic parsing and enrichment with additional metadata.

● Log Analytics for high cardinality analysis and generated metrics for understanding long-term trends.

● Correlation of log data with metrics and traces from other parts of your infrastructure.

"Our customers rely on Datadog to help them understand the security, performance and availability of their cloud infrastructure,” said Ilan Rabinovitch, Vice President, Product and Community at Datadog. “With support for this new feature, customers can now send their AWS logs and telemetry to Datadog without setting up any additional infrastructure, which will make analysis and troubleshooting within Datadog’s cloud monitoring platform easier.”

Doug Yeum, Head of Worldwide Channel and Alliances, at Amazon Web Services, said: “Combined with Amazon Kinesis Data Firehose, Datadog’s log management offering can help customers gain a better understanding of the availability and security of their cloud infrastructure.”

Customers can start streaming their AWS logs to Datadog today by navigating to the AWS Management Console and setting up a new Amazon Kinesis Data Firehose Delivery stream.

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Datadog Announces Support for Streaming Logs with Amazon Kinesis Data Firehose

Datadog announced support for ingesting log data via Amazon Kinesis Data Firehose, a solution from Amazon Web Services (AWS) that reliably loads streaming data into data lakes, data stores and analytics tools.

Customers can now send logs from Amazon CloudWatch and other services to Datadog without setting up and managing log forwarders in their environment.

Amazon Kinesis Data Firehose receives logs from services such as Amazon CloudWatch, Amazon API Gateway, AWS Lambda, and Amazon Elastic Compute Cloud (Amazon EC2) in one place, and routes them to third-party tools and systems. With support for Amazon Kinesis Data Firehose, Datadog allows customers to stream logs from AWS services to Datadog directly from the AWS Management Console and APIs.

This new capability will enable analysis of AWS log data within Datadog by utilizing new and existing Log Management features, including:

● Real-time visibility with Live Tail and dynamic indexing based on easy-to-create filters with Datadog’s Logging without Limits™.

● Automatic parsing and enrichment with additional metadata.

● Log Analytics for high cardinality analysis and generated metrics for understanding long-term trends.

● Correlation of log data with metrics and traces from other parts of your infrastructure.

"Our customers rely on Datadog to help them understand the security, performance and availability of their cloud infrastructure,” said Ilan Rabinovitch, Vice President, Product and Community at Datadog. “With support for this new feature, customers can now send their AWS logs and telemetry to Datadog without setting up any additional infrastructure, which will make analysis and troubleshooting within Datadog’s cloud monitoring platform easier.”

Doug Yeum, Head of Worldwide Channel and Alliances, at Amazon Web Services, said: “Combined with Amazon Kinesis Data Firehose, Datadog’s log management offering can help customers gain a better understanding of the availability and security of their cloud infrastructure.”

Customers can start streaming their AWS logs to Datadog today by navigating to the AWS Management Console and setting up a new Amazon Kinesis Data Firehose Delivery stream.

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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 ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...