惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

K
Kaspersky official blog
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
AI
AI
SecWiki News
SecWiki News
宝玉的分享
宝玉的分享
Scott Helme
Scott Helme
D
Darknet – Hacking Tools, Hacker News & Cyber Security
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
Engineering at Meta
Engineering at Meta
博客园 - 叶小钗
The GitHub Blog
The GitHub Blog
Microsoft Azure Blog
Microsoft Azure Blog
N
News and Events Feed by Topic
Cloudbric
Cloudbric
B
Blog
Cisco Talos Blog
Cisco Talos Blog
V
Vulnerabilities – Threatpost
N
News and Events Feed by Topic
V
Visual Studio Blog
A
Arctic Wolf
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
U
Unit 42
S
Security @ Cisco Blogs
博客园 - 聂微东
T
Threat Research - Cisco Blogs
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Apple Machine Learning Research
Apple Machine Learning Research
Y
Y Combinator Blog
G
GRAHAM CLULEY
L
LINUX DO - 热门话题
量子位
NISL@THU
NISL@THU
Webroot Blog
Webroot Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
T
Troy Hunt's Blog
Application and Cybersecurity Blog
Application and Cybersecurity Blog
T
Tenable Blog
月光博客
月光博客
S
Security Affairs
K
KPMG report finds enterprise disconnect between AI and its ROI | CIO
The Hacker News
The Hacker News
Spread Privacy
Spread Privacy
D
Docker
www.infosecurity-magazine.com
www.infosecurity-magazine.com
雷峰网
雷峰网
博客园 - 司徒正美
T
The Exploit Database - CXSecurity.com
Hugging Face - Blog
Hugging Face - Blog
Help Net Security
Help Net Security
D
DataBreaches.Net

Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - February 2026 Amazon EC2 security: How misconfigured and public AMIs expand your cloud attack surface Enable end-to-end visibility into your Java apps with a single command Measure and improve mobile app startup performance with Datadog RUM Evaluating our AI Guard application to improve quality and control cost Identify untested code across every level of your codebase Make use of guardrail metrics and stop babysitting your releases Monitor Versa Networks SD-WAN performance in Datadog Improve performance and reliability with APM Recommendations Remediate transitive vulnerabilities faster with Datadog Software Composition Analysis Generate audit-ready vulnerability and compliance reports with Datadog Sheets Monitor Fortinet FortiManager performance in Datadog Improve test coverage across codebases with Datadog Code Coverage Move fast, don’t break things: Consistent testing standards at scale Enrich logs with ServiceNow CMDB context before routing to any SIEM or logging tool Monitor Lustre with Datadog Make faster, better product decisions with Datadog Product Analytics Surface and remediate runtime posture issues with Workload Protection Findings Protect agentic AI applications with Datadog AI Guard How to optimize JavaScript code with CSS Trace Google Pub/Sub workloads in Cloud Run with Datadog Detect human names in logs with ML in Sensitive Data Scanner How we cut our NLQ agent debugging time from hours to minutes with LLM Observability Debug PostgreSQL query latency faster with EXPLAIN ANALYZE in Datadog Database Monitoring Datadog acquires Propolis Unify and correlate frontend and backend data with retention filters Scale compliance across global frameworks with Datadog Cloud Security Monitor Arista VeloCloud SD-WAN performance with Datadog Building reliable dashboard agents with Datadog LLM Observability Simplify log collection and aggregation for MSSPs with Datadog Observability Pipelines Mitigation for Node.js denial-of-service vulnerability affecting Datadog APM Automate flaky test fixes with the Bits AI Dev Agent and Test Optimization How we built an AI SRE agent that investigates like a team of engineers Datadog integrations 2025 recap: Observability for AI, security, and hybrid cloud Design effective executive dashboards with Datadog Implement dbt data quality checks with dbt-expectations Bring faster visibility into AWS Lambda functions with remote instrumentation Troubleshoot faster with the GitLab Source Code integration in Datadog How Cambia Health Solutions saved $30,000 monthly with Cloud Cost Management and the Datadog Resource Catalog Normalize any logs for Cloud SIEM with Datadog's OCSF processor Optimizing Datadog at scale: Cost-efficient observability at Zendesk Detect, diagnose, and resolve network issues easily with CNM Network Health Connect engineering errors to user impact in early-stage products Cilium configuration for Kubernetes operations at scale Designing feedback loops for progressive delivery Ship features faster and safer with Datadog Feature Flags Choosing the right OpenTelemetry Collector distribution Route your monitor alerts with Datadog monitor notification rules Automate Cloud SIEM investigations with Bits AI Security Analyst Cloud threat detection: How to identify risky activity across control and data planes Collecting Kafka performance metrics Monitoring Kafka with Datadog Monitoring Kafka performance metrics
Improve developer experience and collaboration with Software Catalog
2025-02-14 · via Datadog | The Monitor blog

As software ecosystems grow more complex and fragmented, organizations are finding it harder to manage the thousands of interdependencies that make up their environments. For starters, engineers are collectively struggling to uphold security and reliability standards throughout their organizations because they lack a shared view of these complex software landscapes. They also find it challenging to work together during incidents as they look for upstream and downstream software components related to the ones directly impacted. And engineers who are new to these organizations have trouble onboarding, learning how these complex systems work, and contributing to incident resolution.

Datadog Service Catalog has long helped solve these problems with respect to all the services in an organization’s software ecosystem. But engineers need even more flexibility in how they define the software assets that make up their tech stacks, to allow their colleagues to discover important elements beyond just services during incident investigations or onboarding. Engineers also need more flexibility in how they define the contextual information (such as ownership) about all these various assets. For example, some components, such as databases and messaging queues, normally have shared ownership across multiple teams—and it’s important that engineers can quickly recognize complex realities like these.

That’s why we’re evolving Service Catalog into Software Catalog. Software Catalog provides teams with an improved framework for modeling their software environments. It helps eliminate knowledge gaps and silos by making it possible for teams to easily view, discover, and understand the many interrelated components—services and others—that make up their organization’s software ecosystem. Through enhancements such as extended entity types and the ability to define relationships manually, Software Catalog helps engineers gain an even clearer view, shared by all teams, of their tech stacks. This in turn helps teams better cooperate during incidents and more easily communicate architecture knowledge to new team members.

In this blog post, you will learn how you can do the following with Software Catalog:

Use developer knowledge of complex architectures to improve collaboration across teams

Software Catalog gathers information about services automatically from APM and USM, which helps teams gain a quick overview of their environments. Now users can supplement and override this automatically derived data. Teams can update Software Catalog metadata to better reflect institutional knowledge and their organization’s shared understanding of their software environment.

Define a range of entity types

Software Catalog allows you to define additional types of software entities beyond services. In this newer schema version, services are just one of four possible component types that you can define, alongside datastores, queues, and APIs. These four component types can also be added to the definitions of new system entities, which act as logical containers to help sort and organize components. Finally, all these various entities—services, datastores, queues, APIs, and systems—are defined within metadata as values assigned to the kind field:

  • kind:service
  • kind:datastore
  • kind:queue
  • kind:api
  • kind:system
Adding a new entry in Service Catalog

For instance, the configuration in the following YAML file defines a datastore component:

apiVersion: v3

kind: datastore

metadata:

name: mydatastore

displayName: My Datastore

owner: AppTeam

additionalOwners:

- name: DbaTeam

type: operator

This ability to define a range of entity types gives your teams greater flexibility to define your Sofware Catalog in whichever way makes the most sense within the context of your organization. And it lets you give all teams more granular detail about the essential components that make up your organization’s software environment. These features benefit incident response by promoting more efficient research, better information sharing, and faster MTTR.

Define systems, components, and inheritance

Previously, in Service Catalog, metadata had to be defined at the service level, i.e., via individual service definitions. With Software Catalog, we now allow inheritance, so you can specify the ownership information at the system level and have the system’s child components inherit its metadata.

To define components within a system, you can specify values for the components key in the spec field of the entity’s definition. An example of this is shown in the entity.datadog.yaml file below, which creates a system entity product-recommendation and defines four components:

apiVersion: v3

kind: system

metadata:

name: product-recommendation

description: Surfaces personalized product suggestions in Shopist

displayName: "Product Recommendation"

tags:

- product:recommendations

- business-line:shared-components

owner: shopist

additionalOwners:

- name: Shopist Support Team

type: Operator

spec:

lifecycle: production

tier: "0"

components:

- service: product-recommendation

- service: orders-app

- api: products

- system: shopist-user-trends

The following image shows what appears in Software Catalog as a result of this configuration: The user-defined system Product Recommendation is made up of the four components defined in the YAML file.

Components defined as part of a system

Components that are defined within a system’s metadata automatically inherit that system’s metadata. You can also explicitly define inheritance from another component by specifying a value for the inheritFrom key in an item’s metadata definition in the metadata field.

These new inheritance features make it much easier for teams to populate data for entities at scale because now, it’s no longer necessary to configure metadata for each entity individually.

Map relationships

In Service Catalog, relationships and dependencies were mapped automatically by using APM and USM data. But now, Software Catalog supports manual configuration for services, datastores, and queues to augment the auto-detected application topology. This ability to update relationships manually helps ensure that relationships among these entities are represented in the way that best reflects common knowledge among teams.

For services, datastores, queues, and APIs, you can manually define dependency information by specifying values for the following new keys within the spec section:

  • dependsOn (specifies a dependency, e.g., serviceA depends on serviceB)
  • ownedBy (e.g., serviceA is owned by TeamA)
  • partOf (e.g., serviceA is a part of systemA)

For example, the following service definition specifies a value for the dependsOn key:

apiVersion:v3

kind:service

metadata:

name:auth-dotnet

spec:

dependsOn:

- service:user-auth

Because the dependency auth-dotnet is configured manually in the config file, it appears as a dependency in Software Catalog even when the Detected Connections option is disabled, as shown below:

Dependency appears when Detected Connections option is off

And when the Detected Connections option is enabled, auth-dotnet-postgres appears as an auto-detected dependent service, as shown here:

Specify multi-ownership

Software Catalog allows you to assign multiple owners to any entity within your system by defining values for owner and additionalOwners in the metadata section, as shown below:

apiVersion: v3

kind: service

metadata:

name: shopping-cart

displayName: Shopping Cart

inheritFrom: service:otherService

tags:

- tag:value

owner: myteam

additionalOwners:

- name: opsTeam

type: operator

This new feature helps ensure that when components have multiple owners, the ownership can be accurately captured. It also helps ensure that teams have multiple people to contact when any one contact is unavailable.

Configure custom filters for logs and events

Within the datadog section, you can now also specify custom filters for logs and events associated with each entity. For example, a custom filter such as status:warn AND system:shopist would allow you to display the results of that complex search in Software Catalog and retrieve the associated logs and events for that system. This new capability enables teams to easily surface the telemetry data that they need the most.

apiVersion: v3

datadog:

events:

- name: "Elastic Search Activities"

query: "site:shopist.io source:elasticsearch"

- name: "Chef Pipelines"

query: "env:prod source:chef role:corp-site"

logs:

- name: "critical logs"

query: "source:java status:error env:prod"

- name: "ops logs"

query: "service:(web-store OR address-service) status:info"

Note that all systems come with a default query out of the box. This default query collects logs and events linked to the service tag values. When you customize the queries, Software Catalog will automatically display the queried logs and events for a tailored view of your systems, as in the following example:

Filtered query and results

Create and automatically validate your entity definitions with the IDE plugin

Datadog’s IDE plugin follows the JSON schema specification, which enables your IDE to autocomplete and validate your metadata as you edit it. The plugin has been updated to support the new Software Catalog and includes information that will tell your IDEs which filenames it applies to—specifically, the files entity.datadog.yaml, entity.datadog.yml, and entity.datadog.json. This update makes it easy to quickly and accurately complete your entity definition file without needing to switch contexts and consult documentation. In the example below, you can see how the IDE automatically creates keys as you fill out the entity definition.

The IDE plugin also automatically validates your entity definition to prevent errors and help ensure that your values are formatted correctly. This helps you get ahead of issues with your entity definition before it reaches Software Catalog. In the screenshot below, for example, Visual Studio Code has recognized an issue with the contact email address, allowing you to fix the formatting before publishing the file.

VS Code recognizing problem to fix

Automate new entity setup with self-service actions

As engineering teams define and enrich their software components with more contextual information through Software Catalog, it’s important that teams define the right metadata for their components. But with more manually declared data allowed in Software Catalog, there’s more potential for human error. Through the new Self-Service tab in Software Catalog, you can automate end-to-end processes that (among other capabilities) enable dynamic and self-service workflows integrated with Software Catalog. Self-service actions allow engineers to create entities through a template that can pre-populate information and ensure that metadata is captured correctly.

The new Self-Service tab in Software Catalog

You can use these self-service actions, for example, to set up a new software template and guide developers through a process of creating a new component according to your best practices.

In the example below, a simple form created through App Builder and made available through the Self-Service tab helps guide a developer through the process of creating a new software component with the proper metadata. This form populates data fields based on the user inputs, which are automatically translated to proper syntax. This reduces the possibility for misconfigurations and enables the new component to correctly appear in Software Catalog on day one.

A self-service action to create a new service from a template

Give developers a comprehensive and customizable view of your software environment

The expanded capabilities of Datadog’s Software Catalog, formerly Service Catalog, give your teams a more comprehensive approach to monitoring their software environments. By extending observability to a wider range of software assets, introducing more manual metadata controls to supplement automatic detection, and enabling metadata inheritance for components, Software Catalog empowers teams to spot and address problems in their environments more quickly and accurately.

You can use our documentation to learn more about the current features of Software Catalog. If you want to read about the Software Catalog product roadmap to stay informed about its upcoming features, sign up for our Internal Developer Portal product preview program.

And if you’re not yet a Datadog user, you can sign up for a 14-day free trial.