What is Dynatrace and what are its top alternatives?
Dynatrace is a leading software intelligence platform that provides monitoring and management capabilities for cloud-native architectures. It offers full-stack observability, AIOps, and digital experience management to optimize performance and user satisfaction. However, Dynatrace can be complex to set up and may come with a higher price tag compared to other alternatives.
- New Relic: New Relic is a cloud-based application performance monitoring tool that offers real-time analytics, error tracking, and infrastructure monitoring. It provides a user-friendly interface and customizable dashboards. Pros: Easy to use, scalable, and great support. Cons: Cost may be a concern for smaller businesses.
- AppDynamics: AppDynamics is an APM solution that helps businesses monitor the performance of their applications in real-time. It provides deep visibility into application performance and user interactions. Pros: Strong analytics capabilities and robust monitoring. Cons: Complex setup and pricing.
- Splunk: Splunk is a data analytics platform that can be used for monitoring and troubleshooting applications and IT infrastructure. It offers log management, real-time monitoring, and machine learning capabilities. Pros: Powerful analytics, customizable dashboards. Cons: Steep learning curve, high cost.
- Datadog: Datadog is a cloud monitoring and observability platform that provides infrastructure monitoring, application performance monitoring, and log management. It offers integrations with various cloud services and technologies. Pros: Easy to use, great visualization tools. Cons: Pricing can add up quickly.
- Azure Monitor: Azure Monitor is a cloud monitoring service provided by Microsoft Azure. It offers comprehensive monitoring and alerting capabilities for applications and infrastructure hosted on Azure. Pros: Seamless integration with Azure services, cost-effective for Azure users. Cons: Limited support for non-Azure environments.
- Prometheus: Prometheus is an open-source monitoring and alerting toolkit designed for cloud-native environments. It collects metrics from various sources, stores them, and provides a querying language for analysis. Pros: Open-source and highly customizable. Cons: Requires expertise to set up and maintain.
- Grafana: Grafana is an open-source visualization and monitoring platform that can be used with various data sources, including Prometheus, InfluxDB, and Graphite. It offers customizable dashboards and alerts. Pros: Highly customizable, community support. Cons: Can be challenging to set up.
- LogicMonitor: LogicMonitor is a SaaS-based performance monitoring platform that offers infrastructure monitoring, application performance monitoring, and log management. It provides automatic discovery and alerting capabilities. Pros: Easy to deploy, automated monitoring. Cons: Pricing may be a drawback for some users.
- Raygun: Raygun is an application performance monitoring tool that provides real-time insights into application performance and user experiences. It offers error tracking, crash reporting, and performance monitoring. Pros: Easy to set up, great error reporting. Cons: Limited integrations compared to other tools.
- SolarWinds AppOptics: SolarWinds AppOptics is a cloud-based infrastructure and application performance monitoring tool that offers real-time visibility into performance metrics and dependencies. It provides monitoring for cloud, on-premises, and hybrid environments. Pros: Cloud monitoring capabilities, easy setup. Cons: Pricing may be a concern for some users.
Top Alternatives to Dynatrace
- 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! ...
- AppDynamics
AppDynamics develops application performance management (APM) solutions that deliver problem resolution for highly distributed applications through transaction flow monitoring and deep diagnostics. ...
- 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. ...
- Splunk
It provides the leading platform for Operational Intelligence. Customers use it 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. ...
- Solarwinds
Developed by network and systems engineers who know what it takes to manage today's dynamic IT environments, SolarWinds has a deep connection to the IT community. ...
- SigNoz
SigNoz is an open-source application performance monitoring tool(APM) tool. It helps developers monitor their application and troubleshoot problems. It can be self-hosted, so it's a great tool for privacy focused companies. ...
- JavaScript
JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...
Dynatrace alternatives & related posts
Datadog
- Monitoring for many apps (databases, web servers, etc)139
- Easy setup107
- Powerful ui87
- Powerful integrations84
- Great value70
- Great visualization54
- Events + metrics = clarity46
- Notifications41
- Custom metrics41
- Flexibility39
- Free & paid plans19
- Great customer support16
- Makes my life easier15
- Adapts automatically as i scale up10
- Easy setup and plugins9
- Super easy and powerful8
- In-context collaboration7
- AWS support7
- Rich in features6
- Docker support5
- Cute logo4
- Source control and bug tracking4
- Monitor almost everything4
- Cost4
- Full visibility of applications4
- Simple, powerful, great for infra4
- Easy to Analyze4
- Best than others4
- Automation tools4
- Best in the field3
- Free setup3
- Good for Startups3
- Expensive3
- APM2
- Expensive19
- No errors exception tracking4
- External Network Goes Down You Wont Be Logging2
- Complicated1
related Datadog posts
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.
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.
- Deep code visibility21
- Powerful13
- Real-Time Visibility8
- Great visualization7
- Easy Setup6
- Comprehensive Coverage of Programming Languages6
- Deep DB Troubleshooting4
- Excellent Customer Support3
- Expensive5
- Poor to non-existent integration with aws services2
related AppDynamics posts
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!
We are evaluating an APM tool and would like to select between AppDynamics or Datadog. Our applications are largely hosted on Microsoft Azure but we would keep the option to move to AWS or Google Cloud Platform in the future.
In addition to core Azure services, we will be hosting other components - including MongoDB, Keycloak, PagerDuty, etc. Our applications are largely C# and React-based using frontend for Backend patterns and Azure API gateway. In addition, there are close to 50+ external services integrated using both REST and SOAP.
New Relic
- Easy setup415
- Really powerful344
- Awesome visualization244
- Ease of use194
- Great ui151
- Free tier107
- Great tool for insights80
- Heroku Integration66
- Market leader55
- Peace of mind49
- Push notifications21
- Email notifications20
- Heroku Add-on17
- Error Detection and Alerting16
- Multiple language support13
- Server Resources Monitoring11
- SQL Analysis11
- Transaction Tracing9
- Azure Add-on8
- Apdex Scores8
- Detailed reports7
- Analysis of CPU, Disk, Memory, and Network7
- Application Response Times6
- Performance of External Services6
- Application Availability Monitoring and Alerting6
- Error Analysis6
- JVM Performance Analyzer (Java)5
- Most Time Consuming Transactions5
- Top Database Operations4
- Easy to use4
- Browser Transaction Tracing4
- Application Map3
- Weekly Performance Email3
- Custom Dashboards3
- Pagoda Box integration3
- App Speed Index2
- Easy to setup2
- Background Jobs Transaction Analysis2
- Time Comparisons1
- Access to Performance Data API1
- Super Expensive1
- Team Collaboration Tools1
- Metric Data Retention1
- Metric Data Resolution1
- Worst Transactions by User Dissatisfaction1
- Real User Monitoring Overview1
- Real User Monitoring Analysis and Breakdown1
- Free1
- Best of the best, what more can you ask for1
- Best monitoring on the market1
- Rails integration1
- Incident Detection and Alerting1
- Cost0
- Exceptions0
- Price0
- Proce0
- Pricing model doesn't suit microservices20
- UI isn't great10
- Expensive7
- Visualizations aren't very helpful7
- Hard to understand why things in your app are breaking5
related New Relic posts
I've used more and more of New Relic Insights here in my work at Kong. New Relic Insights is a "time series event database as a service" with a super-easy API for inserting custom events, and a flexible query language for building visualization widgets and dashboards.
I'm a big fan of New Relic Insights when I have data I know I need to analyze, but perhaps I'm not exactly sure how I want to analyze it in the future. For example, at Kong we recently wanted to get some understanding of our open source community's activity on our GitHub repos. I was able to quickly configure GitHub to send webhooks to Zapier , which in turn posted the JSON to New Relic Insights.
Insights is schema-less and configuration-less - just start posting JSON key value pairs, then start querying your data.
Within minutes, data was flowing from GitHub to Insights, and I was building widgets on my Insights dashboard to help my colleagues visualize the activity of our open source community.
#GitHubAnalytics #OpenSourceCommunityAnalytics #CommunityAnalytics #RepoAnalytics
Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.
I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.
For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.
Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.
Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.
Future improvements / technology decisions included:
Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic
As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.
One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.
- API for searching logs, running reports3
- Alert system based on custom query results3
- Dashboarding on any log contents2
- Custom log parsing as well as automatic parsing2
- Ability to style search results into reports2
- Query engine supports joining, aggregation, stats, etc2
- Splunk language supports string, date manip, math, etc2
- Rich GUI for searching live logs2
- Query any log as key-value pairs1
- Granular scheduling and time window support1
- Splunk query language rich so lots to learn1
related Splunk posts
I am designing a Django application for my organization which will be used as an internal tool. The infra team said that I will not be having SSH access to the production server and I will have to log all my backend application messages to Splunk. I have no knowledge of Splunk so the following are the approaches I am considering: Approach 1: Create an hourly cron job that uploads the server log file to some Splunk storage for later analysis. - Is this possible? Approach 2: Is it possible just to stream the logs to some splunk endpoint? (If yes, I feel network usage and communication overhead will be a pain-point for my application)
Is there any better or standard approach? Thanks in advance.
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.
Prometheus
- Powerful easy to use monitoring47
- Flexible query language38
- Dimensional data model32
- Alerts27
- Active and responsive community23
- Extensive integrations22
- Easy to setup19
- Beautiful Model and Query language12
- Easy to extend7
- Nice6
- Written in Go3
- Good for experimentation2
- Easy for monitoring1
- Just for metrics12
- Bad UI6
- Needs monitoring to access metrics endpoints6
- Not easy to configure and use4
- Supports only active agents3
- Written in Go2
- TLS is quite difficult to understand2
- Requires multiple applications and tools2
- Single point of failure1
related Prometheus posts
Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.
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...
(GitHub : https://github.com/m3db/m3)
related Solarwinds posts
- Based on OpenTelemetry1
- Open Source1
related SigNoz posts
JavaScript
- Can be used on frontend/backend1.7K
- It's everywhere1.5K
- Lots of great frameworks1.2K
- Fast896
- Light weight745
- Flexible425
- You can't get a device today that doesn't run js392
- Non-blocking i/o286
- Ubiquitousness236
- Expressive191
- Extended functionality to web pages55
- Relatively easy language49
- Executed on the client side46
- Relatively fast to the end user30
- Pure Javascript25
- Functional programming21
- Async15
- Full-stack13
- Setup is easy12
- Its everywhere12
- Future Language of The Web12
- JavaScript is the New PHP11
- Because I love functions11
- Like it or not, JS is part of the web standard10
- Expansive community9
- Everyone use it9
- Can be used in backend, frontend and DB9
- Easy9
- Easy to hire developers8
- No need to use PHP8
- For the good parts8
- Can be used both as frontend and backend as well8
- Powerful8
- Most Popular Language in the World8
- Popularized Class-Less Architecture & Lambdas7
- It's fun7
- Nice7
- Versitile7
- Hard not to use7
- Its fun and fast7
- Agile, packages simple to use7
- Supports lambdas and closures7
- Love-hate relationship7
- Photoshop has 3 JS runtimes built in7
- Evolution of C7
- 1.6K Can be used on frontend/backend6
- Client side JS uses the visitors CPU to save Server Res6
- It let's me use Babel & Typescript6
- Easy to make something6
- Can be used on frontend/backend/Mobile/create PRO Ui6
- Promise relationship5
- Stockholm Syndrome5
- Function expressions are useful for callbacks5
- Scope manipulation5
- Everywhere5
- Client processing5
- Clojurescript5
- What to add5
- Because it is so simple and lightweight4
- Only Programming language on browser4
- Test21
- Easy to learn1
- Easy to understand1
- Not the best1
- Hard to learn1
- Subskill #41
- Test1
- Hard 彤0
- A constant moving target, too much churn22
- Horribly inconsistent20
- Javascript is the New PHP15
- No ability to monitor memory utilitization9
- Shows Zero output in case of ANY error8
- Thinks strange results are better than errors7
- Can be ugly6
- No GitHub3
- Slow2
related JavaScript posts
Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.
But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.
But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.
Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark