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Datadog Database Monitoring Now Observes MongoDB

Datadog unveiled that its Database Monitoring product now observes MongoDB databases.

With this announcement, Datadog Database Monitoring supports the five most popular database types—MongoDB, Postgres, MySQL, SQL Server and Oracle.

Datadog Database Monitoring enables application developers and database administrators to troubleshoot and optimize inefficient queries across database environments. With it, teams can easily understand database load, pinpoint long-running and blocking queries, drill into precise execution details and optimize query performance to help prevent incidents and spiraling database costs.

"Replication failures or misconfigurations can result in significant downtime and data inconsistencies for companies, which may impact their application performance and reliability. That's why maintaining high availability across clusters with multiple nodes and replicas is critical," said Omri Sass, Director of Product Management at Datadog. "With support for the top five database types in the industry, Datadog Database Monitoring gives teams complete visibility into their databases, queries and clusters so that they can maintain performant databases and tie them to the health of their applications and success of their businesses."

Datadog Database Monitoring helps teams:

- Ensure high availability of databases: By providing a comprehensive list of database clusters alongside critical metrics like queries per second, reads and writes per second and replication details, teams can monitor overall cluster performance at a glance, detect potential issues early and take preventative measures.

- Optimize query and database performance: Teams track key query performance metrics—like latency, execution time and volume of data queried—to quickly detect long-running transactions, high-impact blockers and missing indices while receiving proactive recommendations to fix these issues.

- Resolve database and application issues faster: By integrating database monitoring and application performance monitoring, Datadog's unified platform correlates health metrics and distributed traces with query metrics and explain plans in one view in order to accelerate root cause analysis of high latency, leading to faster triage and resolution of issues.

MongoDB is a leading modern document database provider. MongoDB's document model streamlines the process of building data-driven applications with a developer-friendly query language and a flexible data model that is easy to work with and easy to scale. The newly added support for MongoDB by Datadog Database Monitoring makes it easier for joint customers to maximize performance by optimizing deployment and infrastructure allocation, for example, by analyzing resource usage and overlapping workloads to make the most of available resources.

"As enterprises take advantage of today's increasingly data-intensive workloads, it's critical that they have the tools needed to deploy high-performing applications with complete confidence," said Will Winn, Senior Director of Partners at MongoDB. "Customers trust MongoDB for its superior performance and flexibility, and now that Datadog Database Monitoring supports MongoDB, ensuring high availability and seamless performance of MongoDB database clusters is even easier."

Datadog Database Monitoring's support for MongoDB is now generally available.

The Latest

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Datadog Database Monitoring Now Observes MongoDB

Datadog unveiled that its Database Monitoring product now observes MongoDB databases.

With this announcement, Datadog Database Monitoring supports the five most popular database types—MongoDB, Postgres, MySQL, SQL Server and Oracle.

Datadog Database Monitoring enables application developers and database administrators to troubleshoot and optimize inefficient queries across database environments. With it, teams can easily understand database load, pinpoint long-running and blocking queries, drill into precise execution details and optimize query performance to help prevent incidents and spiraling database costs.

"Replication failures or misconfigurations can result in significant downtime and data inconsistencies for companies, which may impact their application performance and reliability. That's why maintaining high availability across clusters with multiple nodes and replicas is critical," said Omri Sass, Director of Product Management at Datadog. "With support for the top five database types in the industry, Datadog Database Monitoring gives teams complete visibility into their databases, queries and clusters so that they can maintain performant databases and tie them to the health of their applications and success of their businesses."

Datadog Database Monitoring helps teams:

- Ensure high availability of databases: By providing a comprehensive list of database clusters alongside critical metrics like queries per second, reads and writes per second and replication details, teams can monitor overall cluster performance at a glance, detect potential issues early and take preventative measures.

- Optimize query and database performance: Teams track key query performance metrics—like latency, execution time and volume of data queried—to quickly detect long-running transactions, high-impact blockers and missing indices while receiving proactive recommendations to fix these issues.

- Resolve database and application issues faster: By integrating database monitoring and application performance monitoring, Datadog's unified platform correlates health metrics and distributed traces with query metrics and explain plans in one view in order to accelerate root cause analysis of high latency, leading to faster triage and resolution of issues.

MongoDB is a leading modern document database provider. MongoDB's document model streamlines the process of building data-driven applications with a developer-friendly query language and a flexible data model that is easy to work with and easy to scale. The newly added support for MongoDB by Datadog Database Monitoring makes it easier for joint customers to maximize performance by optimizing deployment and infrastructure allocation, for example, by analyzing resource usage and overlapping workloads to make the most of available resources.

"As enterprises take advantage of today's increasingly data-intensive workloads, it's critical that they have the tools needed to deploy high-performing applications with complete confidence," said Will Winn, Senior Director of Partners at MongoDB. "Customers trust MongoDB for its superior performance and flexibility, and now that Datadog Database Monitoring supports MongoDB, ensuring high availability and seamless performance of MongoDB database clusters is even easier."

Datadog Database Monitoring's support for MongoDB is now generally available.

The Latest

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...