Alternatives to Dashbird logo

Alternatives to Dashbird

IOpipe, Epsagon, Datadog, Thundra, and New Relic are the most popular alternatives and competitors to Dashbird.
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What is Dashbird and what are its top alternatives?

Dashbird gives full visibility into serverless architectures. Failure detection, monitoring and debugging capabilities for AWS Lambda and event sources. Get up and running in 5 minutes without any performance overhead or code changes.
Dashbird is a tool in the Performance Monitoring category of a tech stack.

Dashbird alternatives & related posts

IOpipe logo

IOpipe

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Observability for serverless applications on AWS Lambda
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IOpipe
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Dashbird
Epsagon logo

Epsagon

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Automated Monitoring & Troubleshooting for Cloud Applications
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Epsagon
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Dashbird

related Datadog posts

Robert Zuber
Robert Zuber
CTO at CircleCI · | 8 upvotes · 463.2K views

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

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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Thundra logo

Thundra

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End to end serverless observability providing test, debug, monitor and alerting capabilities.
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Thundra
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Dashbird
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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New Relic
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Dashbird

related New Relic posts

Sebastian Gębski
Sebastian Gębski
CTO at Shedul/Fresha · | 4 upvotes · 470.2K views

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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Jerome Dalbert
Jerome Dalbert
Senior Backend Engineer at StackShare · | 4 upvotes · 202.3K views

We currently monitor performance with the following tools:

  1. Heroku Metrics: our main app is Hosted on Heroku, so it is the best place to get quick server metrics like memory usage, load averages, or response times.
  2. Good old New Relic for detailed general metrics, including transaction times.
  3. Skylight for more specific Rails Controller#action transaction times. Navigating those timings is much better than with New Relic, as you get a clear full breakdown of everything that happens for a given request.

Skylight offers better Rails performance insights, so why use New Relic? Because it does frontend monitoring, while Skylight doesn't. Now that we have a separate frontend app though, our frontend engineers are looking into more specialized frontend monitoring solutions.

Finally, if one of our apps go down, Pingdom alerts us on Slack and texts some of us.

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ruxit logo

ruxit

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Full stack availability and performance monitoring powered by artificial intelligence
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ruxit
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Dashbird
Azure Application Insights logo

Azure Application Insights

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It is an extensible Application Performance Management (APM) service for web developers
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Azure Application Insights
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Dashbird