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Under the hood
Building blocks: Introducing observability through monitoring
Learn how you can build a culture of observability in your organization
Metric graphs 101: Graphing anti-patterns
In this post, we explore three ways that metric graphs are commonly misused and then suggest better solutions for clearly visualizing monitoring data
Metric graphs 101: Summary graphs
Learn how to effectively use summary graphs: visualizations that flatten a particular span of time to provide a summary window into your infrastructure
The power of tagged metrics
Tagged metrics let you add infrastructural dimensions to your metrics on the fly—without modifying the way your metrics are collected.
Metric graphs 101: Timeseries graphs
To help you effectively visualize your metrics, this post explores 4 types of timeseries graphs: Line graphs, stacked area graphs, bar graphs, and heat maps
Why 2016 is the year of monitoring at scale
Since launching Datadog, we've seen our thesis validated on a far broader scale than we had originally anticipated. 2016 is the year of monitoring at scale.
Monitoring 101: Investigating performance issues
Once your monitoring system has notified you of real performance issues that require attention, its next job is to help you diagnose the root cause. Often this is the least structured aspect of monitoring, driven largely by hunches and guess-and-check.
Monitoring 101: Alerting on what matters
Automated alerts allow you to spot problems anywhere in your infrastructure, so that you can rapidly identify their causes and minimize service disruptions
Monitoring 101: Collecting the right data
Collect metrics and classify data so that you can receive meaningful, automated alerts about potential problems, and quickly get to the bottom of performance issues
Crossing Streams: a love letter to Go io.Reader
The Go io.reader allows for better control buffering resulting in faster code that uses less memory. Learn more.
Go Performance Tales
Looking for performance tips for Go applications? In this blog, read about one software engineer's quest to achieve good Go performance.
PyData Talk - Building High-Volume Data Systems in the Python Ecosystem
This talk, given at PyData 2013, goes through building high throughput pipelines in the Python ecosystem with the Python / Numpy / Cython stack we use at Datadog.
Learning from AWS failure
Failures are a fact of life. AWS failure just gets more publicity. Instead let's focus on t he more interesting question: What can we learn from these failures?
Triggered Alerts in Datadog - providing context to alerts
Datadog's new Triggered Alert screen works to make available important contextual information with one click by showing you all your triggered alerts.
StatsD, what it is and how it can help you
Learn what StatsD is, how it works, what sets it apart from the rest and what problems it solves.
AWS EBS latency and IOPS: The surprising truth
Performance issues with Amazon Web Services' Elastic Block Storage (EBS) are complex. Learn how to detect and resolve AWS EBS performance issues.
Getting optimal performance with AWS EBS Provisioned IOPS
Optimize your AWS EBS performance by using Provisioned IOPS. Learn more!
Where is open source monitoring going?
We are proud to sponsor the very first edition of Monitorama, the conference of open source monitoring that took place in Boston on March 28th and 29th.
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