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Datadog

Unify logs, metrics, and traces from across your distributed infrastructure.
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What is Datadog?

Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog!
Datadog is a tool in the Performance Monitoring category of a tech stack.

Who uses Datadog?

Companies
723 companies reportedly use Datadog in their tech stacks, including Airbnb, Facebook, and Spotify.

Developers
1388 developers on StackShare have stated that they use Datadog.

Datadog Integrations

GitHub, PHP, nginx, Git, and Node.js are some of the popular tools that integrate with Datadog. Here's a list of all 143 tools that integrate with Datadog.

Why developers like Datadog?

Here’s a list of reasons why companies and developers use Datadog
Datadog Reviews

Here are some stack decisions, common use cases and reviews by companies and developers who chose Datadog in their tech stack.

Noah Zoschke
Noah Zoschke
Engineering Manager at Segment · | 28 upvotes · 121K views
atSegmentSegment
Go
Go
gRPC
gRPC
Envoy
Envoy
TypeScript
TypeScript
Datadog
Datadog
#Framework
#REST
#Json
#Security
#Reliability
#Observability

We just launched the Segment Config API (try it out for yourself here) — a set of public REST APIs that enable you to manage your Segment configuration. Behind the scenes the Config API is built with Go , GRPC and Envoy.

At Segment, we build new services in Go by default. The language is simple so new team members quickly ramp up on a codebase. The tool chain is fast so developers get immediate feedback when they break code, tests or integrations with other systems. The runtime is fast so it performs great at scale.

For the newest round of APIs we adopted the GRPC service #framework.

The Protocol Buffer service definition language makes it easy to design type-safe and consistent APIs, thanks to ecosystem tools like the Google API Design Guide for API standards, uber/prototool for formatting and linting .protos and lyft/protoc-gen-validate for defining field validations, and grpc-gateway for defining REST mapping.

With a well designed .proto, its easy to generate a Go server interface and a TypeScript client, providing type-safe RPC between languages.

For the API gateway and RPC we adopted the Envoy service proxy.

The internet-facing segmentapis.com endpoint is an Envoy front proxy that rate-limits and authenticates every request. It then transcodes a #REST / #JSON request to an upstream GRPC request. The upstream GRPC servers are running an Envoy sidecar configured for Datadog stats.

The result is API #security , #reliability and consistent #observability through Envoy configuration, not code.

We experimented with Swagger service definitions, but the spec is sprawling and the generated clients and server stubs leave a lot to be desired. GRPC and .proto and the Go implementation feels better designed and implemented. Thanks to the GRPC tooling and ecosystem you can generate Swagger from .protos, but it’s effectively impossible to go the other way.

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Robert Zuber
Robert Zuber
CTO at CircleCI · | 8 upvotes · 130.9K views
atCircleCICircleCI
Datadog
Datadog
PagerDuty
PagerDuty
Honeycomb
Honeycomb
Rollbar
Rollbar
Segment
Segment
Amplitude
Amplitude
PostgreSQL
PostgreSQL
Looker
Looker

Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

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StackShare Editors
StackShare Editors
Grafana
Grafana
StatsD
StatsD
Airflow
Airflow
PagerDuty
PagerDuty
Datadog
Datadog
Celery
Celery
AWS EC2
AWS EC2
Flask
Flask

Data science and engineering teams at Lyft maintain several big data pipelines that serve as the foundation for various types of analysis throughout the business.

Apache Airflow sits at the center of this big data infrastructure, allowing users to “programmatically author, schedule, and monitor data pipelines.” Airflow is an open source tool, and “Lyft is the very first Airflow adopter in production since the project was open sourced around three years ago.”

There are several key components of the architecture. A web UI allows users to view the status of their queries, along with an audit trail of any modifications the query. A metadata database stores things like job status and task instance status. A multi-process scheduler handles job requests, and triggers the executor to execute those tasks.

Airflow supports several executors, though Lyft uses CeleryExecutor to scale task execution in production. Airflow is deployed to three Amazon Auto Scaling Groups, with each associated with a celery queue.

Audit logs supplied to the web UI are powered by the existing Airflow audit logs as well as Flask signal.

Datadog, Statsd, Grafana, and PagerDuty are all used to monitor the Airflow system.

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Julien DeFrance
Julien DeFrance
Principal Software Engineer at Tophatter · | 3 upvotes · 70.6K views
atStessaStessa
New Relic
New Relic
Datadog
Datadog
#APM

Which #APM / #Infrastructure #Monitoring solution to use?

The 2 major players in that space are New Relic and Datadog Both are very comparable in terms of pricing, capabilities (Datadog recently introduced APM as well).

In our use case, keeping the number of tools minimal was a major selection criteria.

As we were already using #NewRelic, my recommendation was to move to the pro tier so we would benefit from advanced APM features, synthetics, mobile & infrastructure monitoring. And gain 360 degree view of our infrastructure.

Few things I liked about New Relic: - Mobile App and push notificatin - Ease of setting up new alerts - Being notified via email and push notifications without requiring another alerting 3rd party solution

I've certainly seen use cases where NewRelic can also be used as an input data source for Datadog. Therefore depending on your use case, it might also be worth evaluating a joint usage of both solutions.

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Dan Ambrisco
Dan Ambrisco
Senior Software Engineer at MachineShop · | 3 upvotes · 24.5K views
atMachineShopMachineShop
Datadog
Datadog

One of the very first tools I pulled in when I joined MachineShop was Datadog. We were lacking monitoring and Datadog was my go-to and in the subsequent years its thoroughly proven itself as reliable and informative. We use Datadog to both detect a wide variety of system anomalies and errors as well as provide highly detailed dashboards that help to indicate our system's health at a glance.

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Nikola Yovchev
Nikola Yovchev
Head of Engineering at Relay42 · | 2 upvotes · 61.2K views
atRelay42Relay42
Datadog
Datadog
Pingdom
Pingdom
Terraform
Terraform
#Datadog
#Relay42
#Monitoring

#Datadog #Relay42 #Monitoring

With Datadog unveiling their Synthetics product (https://www.datadoghq.com/blog/introducing-synthetic-monitoring/), we at Relay42 are considering moving out of Pingdom.

The rationale is simple:

  • 90% of our monitoring is on Datadog, apart from the external requests. It'd be nice to identify regional issues in one place, so this is great in our monitoring consolidation efforts.

  • The lack of a non-community Terraform provider for Pingdom

We have yet to get in the beta and test it out but we feel very excited about this announcement.

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Datadog's Features

  • 14-day Free Trial for an unlimited number of hosts
  • 200+ turn-key integrations for data aggregation
  • Clean graphs of StatsD and other integrations
  • Slice and dice graphs and alerts by tags, roles, and more
  • Easy-to-use search for hosts, metrics, and tags
  • Alert notifications via e-mail and PagerDuty
  • Receive alerts on any metric, for a single host or an entire cluster
  • Full API access in more than 15 languages
  • Overlay metrics and events across disparate sources
  • Out-of-the-box and customizable monitoring dashboards
  • Easy way to compute rates, ratios, averages, or integrals
  • Sampling intervals of 10 seconds
  • Mute all alerts with 1 click during upgrades and maintenance
  • Tools for team collaboration

Datadog Alternatives & Comparisons

What are some alternatives to Datadog?
New Relic
New Relic is the all-in-one web application performance tool that lets you see performance from the end user experience, through servers, and down to the line of application code.
Splunk
Splunk Inc. provides the leading platform for Operational Intelligence. Customers use Splunk to search, monitor, analyze and visualize machine data.
Prometheus
Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.
Grafana
Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins.
AppDynamics
AppDynamics develops application performance management (APM) solutions that deliver problem resolution for highly distributed applications through transaction flow monitoring and deep diagnostics.
See all alternatives

Datadog's Followers
1511 developers follow Datadog to keep up with related blogs and decisions.
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