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

S
SegmentFault 最新的问题
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
B
Blog RSS Feed
Y
Y Combinator Blog
T
Tailwind CSS Blog
博客园 - 三生石上(FineUI控件)
J
Java Code Geeks
Stack Overflow Blog
Stack Overflow Blog
aimingoo的专栏
aimingoo的专栏
Jina AI
Jina AI
The GitHub Blog
The GitHub Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
A
About on SuperTechFans
H
Hackread – Cybersecurity News, Data Breaches, AI and More
D
Docker
酷 壳 – CoolShell
酷 壳 – CoolShell
C
Check Point Blog
M
MIT News - Artificial intelligence
Last Week in AI
Last Week in AI
V
V2EX
腾讯CDC
F
Fortinet All Blogs
博客园 - 叶小钗
T
The Blog of Author Tim Ferriss

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
Track detailed run-time performance data with mParticle a...
2015-04-20 · via Datadog | The Monitor blog
Clark Kibler

Clark Kibler

This is a guest post from Clark Kibler, Director of Product Management at mParticle.

About mParticle

mParticle is a mobile technology company dedicated to making it easier and faster for app marketers and developers to integrate with the mobile service ecosystem. By centralizing first party app data collection through a single, lightweight SDK, mParticle customers can implement new partners without changing code or waiting for app store approval. In addition to improving app stability and security, this approach enables app developers to spend less time on integrations and more time building features that delight their users.

Thanks to Datadog and mParticle, mobile app run-time performance is no longer a blind-spot for app publishers’ real-time system monitoring efforts. It’s now possible to create a complete, unified view of system activity and performance that encompasses mobile, desktop, and server.

Track detailed run-time performance data on your mobile apps in real-time

Track detailed run-time performance data with mParticle and Datadog
Track detailed run-time performance data with mParticle and Datadog

The mParticle SDK automatically collects detailed run-time performance data such as CPU load, memory usage, and battery level. Developers can leverage the mParticle integration with Datadog to monitor and alert on these stats in their Datadog dashboards. You can also track the latency of any network requests made by your apps. All of these metrics can be broken down by detailed technographic information, such as OS version, app version, device model, and location.

Overlay mobile app crash alerts on all of your Datadog graphs

Track detailed run-time performance data with mParticle and Datadog
Track detailed run-time performance data with mParticle and Datadog

mParticle forwards all app crashes and unhandled exceptions as events to Datadog, with aggregation keys that group similar error messages into a single item in your Datadog Events Stream. These can then be shared with and commented on by team members, or plotted alongside any other metric you’re tracking in Datadog.

Correlate mobile user activity with other data in your system

mParticle forwards real-time active session counts to Datadog, enabling app developers to correlate app user activity with metrics from any other part of your infrastructure. Session activity can be broken down by a variety of dimensions, including mobile platform, OS version, app version, device model, and location.

Does your team use mParticle but not Datadog? You can get a free 14-day trial of Datadog and see how easy it is to track all of your app performance metrics in one place. If you’re already a Datadog customer, get started with the integration here.

Alternatively, does your team use Datadog but not mParticle? You can get started with mParticle by signing up for a free 14-day trial on our website. For more information about mParticle, drop us a line at info@mparticle.com and we’ll get right back to you!