Alternatives to Amazon CloudWatch logo

Alternatives to Amazon CloudWatch

Datadog, Splunk, New Relic, Prometheus, and AWS CloudTrail are the most popular alternatives and competitors to Amazon CloudWatch.
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What is Amazon CloudWatch and what are its top alternatives?

It helps you gain system-wide visibility into resource utilization, application performance, and operational health. It retrieve your monitoring data, view graphs to help take automated action based on the state of your cloud environment.
Amazon CloudWatch is a tool in the Cloud Monitoring category of a tech stack.

Amazon CloudWatch alternatives & related posts

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Robert Zuber
Robert Zuber
CTO at CircleCI · | 8 upvotes · 261K views
atCircleCICircleCI
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PagerDuty
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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
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StatsD
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Airflow
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Celery
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AWS EC2
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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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Splunk logo

Splunk

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Search, monitor, analyze and visualize machine data
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    Kibana
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    Splunk
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    Grafana
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    I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

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    New Relic logo

    New Relic

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    SaaS Application Performance Management for Ruby, PHP, .Net, Java, Python, and Node.js Apps.
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    Sebastian Gębski
    Sebastian Gębski
    CTO at Shedul/Fresha · | 4 upvotes · 322.1K views
    atFresha EngineeringFresha Engineering
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    CircleCI
    Jenkins
    Jenkins
    Git
    Git
    GitHub
    GitHub
    New Relic
    New Relic
    AppSignal
    AppSignal
    Sentry
    Sentry
    Logentries
    Logentries

    Regarding Continuous Integration - we've started with something very easy to set up - CircleCI , but with time we're adding more & more complex pipelines - we use Jenkins to configure & run those. It's much more effort, but at some point we had to pay for the flexibility we expected. Our source code version control is Git (which probably doesn't require a rationale these days) and we keep repos in GitHub - since the very beginning & we never considered moving out. Our primary monitoring these days is in New Relic (Ruby & SPA apps) and AppSignal (Elixir apps) - we're considering unifying it in New Relic , but this will require some improvements in Elixir app observability. For error reporting we use Sentry (a very popular choice in this class) & we collect our distributed logs using Logentries (to avoid semi-manual handling here).

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    Julien DeFrance
    Julien DeFrance
    Principal Software Engineer at Tophatter · | 3 upvotes · 122.8K 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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    Prometheus logo

    Prometheus

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    An open-source service monitoring system and time series database, developed by SoundCloud
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    Conor Myhrvold
    Conor Myhrvold
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    Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

    By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

    To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

    https://eng.uber.com/m3/

    (GitHub : https://github.com/m3db/m3)

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    Raja Subramaniam Mahali
    Raja Subramaniam Mahali
    Prometheus
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    We have Prometheus as a monitoring engine as a part of our stack which contains Kubernetes cluster, container images and other open source tools. Also, I am aware that Sysdig can be integrated with Prometheus but I really wanted to know whether Sysdig or sysdig+prometheus will make better monitoring solution.

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    AWS CloudTrail

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