Datadog vs Logstash

Overview

Datadog

Stacks9.8K

Followers8.2K

Votes861

Logstash

Stacks12.3K

Followers8.8K

Votes103

GitHub Stars14.7K

Forks3.5K

Datadog vs Logstash: What are the differences?

Introduction

This article aims to highlight the key differences between Datadog and Logstash, two popular tools used for log management and analysis.

Architecture: Datadog is a SaaS-based monitoring platform that collects and analyzes log data in real-time from various sources. It provides a centralized platform for monitoring and visualizing logs. On the other hand, Logstash is an open-source log pipeline tool that ingests, processes, and forwards logs to multiple destinations. It works in conjunction with Elasticsearch and Kibana to provide a complete log management solution.
Ease of Use: Datadog offers a user-friendly interface with intuitive dashboards and pre-built log management features, making it easier for users to set up and navigate the platform. Logstash, being an open-source tool, requires more manual configuration and setup, which may require some technical expertise to fully utilize its capabilities.
Flexibility: Datadog provides built-in integrations with a wide range of popular services and technologies, allowing users to easily collect logs from various sources. It also offers support for custom log formats and structured logging. Logstash, being highly customizable, allows users to define complex ingestion pipelines and perform advanced data transformations.
Scalability: Datadog is a scalable SaaS solution that can handle large volumes of log data without significant performance degradation. It offers various pricing plans to accommodate different needs. Logstash, being part of the Elastic Stack, can be horizontally scaled by adding more nodes to handle increasing log volumes. It leverages Elasticsearch's distributed nature to handle large-scale log data processing.
Alerting and Monitoring: Datadog offers advanced alerting and monitoring capabilities, allowing users to set up real-time alerts based on log data anomalies or specific events. It provides various notification channels and integrates with popular chat and collaboration platforms. Logstash, being a data pipeline tool, focuses more on log ingestion and transformation rather than providing built-in alerting and monitoring features. However, it can work in tandem with other tools like Elasticsearch and Kibana to achieve similar functionalities.
Community and Support: Datadog has a strong community and a dedicated support team that provides prompt assistance to users. It offers comprehensive documentation, tutorials, and online resources to help users get started and troubleshoot issues. Logstash, being an open-source tool, has an active community of contributors and users. It has extensive documentation and forums where users can seek help and share their experiences.

In summary, Datadog is a SaaS-based log management platform that provides a user-friendly interface, comprehensive integrations, and advanced alerting capabilities. Logstash, being an open-source log pipeline tool, offers greater flexibility and customization options, allowing users to define complex ingestion pipelines.

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Advice on Datadog, Logstash

Farzeem Diamond

Software Engineer at IVP

Jul 21, 2020

Needs adviceon

Datadog

Dynatrace

AppDynamics

Hey there! We are looking at Datadog, Dynatrace, AppDynamics, and New Relic as options for our web application monitoring.

Current Environment: .NET Core Web app hosted on Microsoft IIS

Future Environment: Web app will be hosted on Microsoft Azure

Tech Stacks: IIS, RabbitMQ, Redis, Microsoft SQL Server

Requirement: Infra Monitoring, APM, Real - User Monitoring (User activity monitoring i.e., time spent on a page, most active page, etc.), Service Tracing, Root Cause Analysis, and Centralized Log Management.

Please advise on the above. Thanks!

1.59M views1.59M

Comments

Medeti

Jun 27, 2020

Needs adviceon

Amazon EKS

Kubernetes

AWS Elastic Load Balancing (ELB)

We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.

We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.

We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.

You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?

1.51M views1.51M

Comments

Benoit

Principal Engineer at Sqreen

Sep 17, 2019

Decided

I chose Datadog APM because the much better APM insights it provides (flamegraph, percentiles by default).

The drawbacks of this decision are we had to move our production monitoring to TimescaleDB + Telegraf instead of NR Insight

NewRelic is definitely easier when starting out. Agent is only a lib and doesn't require a daemon

457k views457k

Comments

Detailed Comparison

Datadog	Logstash
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!	Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana.
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	Centralize data processing of all types;Normalize varying schema and formats;Quickly extend to custom log formats;Easily add plugins for custom data source
Statistics
GitHub Stars -	GitHub Stars 14.7K
GitHub Forks -	GitHub Forks 3.5K
Stacks 9.8K	Stacks 12.3K
Followers 8.2K	Followers 8.8K
Votes 861	Votes 103
Pros & Cons
Pros 140 Monitoring for many apps (databases, web servers, etc) 107 Easy setup 87 Powerful ui 84 Powerful integrations 70 Great value Cons 20 Expensive 4 No errors exception tracking 2 External Network Goes Down You Wont Be Logging 1 Complicated	Pros 69 Free 18 Easy but powerful filtering 12 Scalable 2 Kibana provides machine learning based analytics to log 1 Great to meet GDPR goals Cons 4 Memory-intensive 1 Documentation difficult to use
Integrations
NGINX Google App Engine Apache HTTP Server Java Docker Pingdom MySQL Ruby Python Memcached	Kibana Elasticsearch Beats

What are some alternatives to Datadog, Logstash?

New Relic

The world’s best software and DevOps teams rely on New Relic to move faster, make better decisions and create best-in-class digital experiences. If you run software, you need to run New Relic. More than 50% of the Fortune 100 do too.

Papertrail

Papertrail helps detect, resolve, and avoid infrastructure problems using log messages. Papertrail's practicality comes from our own experience as sysadmins, developers, and entrepreneurs.

Logmatic

Get a clear overview of what is happening across your distributed environments, and spot the needle in the haystack in no time. Build dynamic analyses and identify improvements for your software, your user experience and your business.

Raygun

Raygun gives you a window into how users are really experiencing your software applications. Detect, diagnose and resolve issues that are affecting end users with greater speed and accuracy.

Loggly

It is a SaaS solution to manage your log data. There is nothing to install and updates are automatically applied to your Loggly subdomain.

Logentries

Logentries makes machine-generated log data easily accessible to IT operations, development, and business analysis teams of all sizes. With the broadest platform support and an open API, Logentries brings the value of log-level data to any system, to any team member, and to a community of more than 25,000 worldwide users.

AppSignal

AppSignal gives you and your team alerts and detailed metrics about your Ruby, Node.js or Elixir application. Sensible pricing, no aggressive sales & support by developers.

Graylog

Centralize and aggregate all your log files for 100% visibility. Use our powerful query language to search through terabytes of log data to discover and analyze important information.

AppDynamics

AppDynamics develops application performance management (APM) solutions that deliver problem resolution for highly distributed applications through transaction flow monitoring and deep diagnostics.

Stackify

Stackify offers the only developers-friendly innovative cloud based solution that fully integrates application performance management (APM) with error and log. Allowing them to easily monitor, detect and resolve application issues faster

Related Comparisons

Datadog vs Logstash: What are the differences?

Introduction

This article aims to highlight the key differences between Datadog and Logstash, two popular tools used for log management and analysis.

Architecture: Datadog is a SaaS-based monitoring platform that collects and analyzes log data in real-time from various sources. It provides a centralized platform for monitoring and visualizing logs. On the other hand, Logstash is an open-source log pipeline tool that ingests, processes, and forwards logs to multiple destinations. It works in conjunction with Elasticsearch and Kibana to provide a complete log management solution.
Ease of Use: Datadog offers a user-friendly interface with intuitive dashboards and pre-built log management features, making it easier for users to set up and navigate the platform. Logstash, being an open-source tool, requires more manual configuration and setup, which may require some technical expertise to fully utilize its capabilities.
Flexibility: Datadog provides built-in integrations with a wide range of popular services and technologies, allowing users to easily collect logs from various sources. It also offers support for custom log formats and structured logging. Logstash, being highly customizable, allows users to define complex ingestion pipelines and perform advanced data transformations.
Scalability: Datadog is a scalable SaaS solution that can handle large volumes of log data without significant performance degradation. It offers various pricing plans to accommodate different needs. Logstash, being part of the Elastic Stack, can be horizontally scaled by adding more nodes to handle increasing log volumes. It leverages Elasticsearch's distributed nature to handle large-scale log data processing.
Alerting and Monitoring: Datadog offers advanced alerting and monitoring capabilities, allowing users to set up real-time alerts based on log data anomalies or specific events. It provides various notification channels and integrates with popular chat and collaboration platforms. Logstash, being a data pipeline tool, focuses more on log ingestion and transformation rather than providing built-in alerting and monitoring features. However, it can work in tandem with other tools like Elasticsearch and Kibana to achieve similar functionalities.
Community and Support: Datadog has a strong community and a dedicated support team that provides prompt assistance to users. It offers comprehensive documentation, tutorials, and online resources to help users get started and troubleshoot issues. Logstash, being an open-source tool, has an active community of contributors and users. It has extensive documentation and forums where users can seek help and share their experiences.

Datadog vs Logstash

Overview

Datadog vs Logstash: What are the differences?