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Elastic Observability

Elastic Observability


What is Elastic Observability?

Elastic Observability, from Elastic, the makers of Elasticsearch, is a solution that aims to bring logs, metrics, and APM based on the former Opbeat (acquired by Elastic in 2017) traces together at scale in a single stack so users can…

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Recent Reviews

TrustRadius Insights

Users and customers have found the Elastic Observability software to be invaluable for analyzing and monitoring various aspects of their …
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What is Elastic Observability?

Elastic Observability, from Elastic, the makers of Elasticsearch, is a solution that aims to bring logs, metrics, and APM based on the former Opbeat (acquired by Elastic in 2017) traces together at scale in a single stack so users can monitor and react to events happening anywhere in an IT…

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What is Splunk Application Performance Monitoring (APM)?

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SolarWinds NPM is a monitoring and performance management platform. It provides performance troubleshooting support, auto network discovery, customizable thresholds, and can be rapidly deployed.

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Product Demos

Standardising APM with OpenTelemetry and Elastic Observability - 2 Feb, 2021 Elastic Meetup

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Product Details

Elastic Observability Technical Details

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Reviews and Ratings


Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

Users and customers have found the Elastic Observability software to be invaluable for analyzing and monitoring various aspects of their environment. By leveraging different dashboards such as time series or geographical maps, users have been able to analyze millions of traces from customer devices. Additionally, reviewers have utilized Elasticsearch as a data sink to benchmark and troubleshoot application performance, building visualizations on top of the events for optimization purposes. The software has also been praised for its ability to unify logs from different applications, generate log metrics, and consolidate costs through its unified dashboard. In terms of Kubernetes clusters, users have successfully monitored their performance globally and resolved issues efficiently, reducing the need for dedicated engineers in the monitoring team. Kibana, a key component of Elastic Observability, has served as a bridge between monitoring and support lifecycles for applications, capturing production failures faster for better response times. Users have utilized Kibana for a range of purposes including data gathering from databases, logging API activities, troubleshooting problems, and setting up alerts. Its intuitive interface and powerful dashboards have made it easy to use while providing valuable insights to developers and operations teams. Finally, Elastic Observability's integration with Elasticsearch has allowed users to effectively capture and derive insights from various logs including API logs, facilitating auditing and monitoring of user activities. Overall, Elasticsearch and Kibana have emerged as trusted solutions for log management, real-time data analysis, capacity planning, infrastructure expansion, bug analysis, log searching, and much more. The product's scalability, efficiency, ease of deployment in Kubernetes environments, and integration capabilities with other tools make it an attractive choice for businesses looking to manage large volumes of data effectively.

Simplicity and Ease of Use: Users have praised Elastic Observability and Elasticsearch for their simplicity and ease of use. They appreciate how easy it is to set up these tools in their environment with minimal research, trial, and error. The combination of Elasticsearch and Kibana is particularly liked for its user-friendly interface, making data analysis convenient.

Powerful Visualizations: Several reviewers have mentioned the powerful visualizations provided by Elasticsearch and Kibana. Users find the various visualizations, including graphs, charts, and tables, very helpful in analyzing data. They appreciate the ability to create interactive charts and easily visualize large volumes of data in one place quickly.

Efficient Log Searching and Analysis: Many users have highlighted the efficient log searching and analysis capabilities of Elasticsearch. They find Elasticsearch's log filtering and modification feature easy to use and modify. Users also appreciate the option to sort logs in ascending or descending order, which facilitates data analysis. The ability to search for multiple fields in a single query is another feature that has been positively mentioned by reviewers.

Lack of options for combining logs: Several users have expressed dissatisfaction with the lack of options to combine logs with log files and view all application information in one place. They find this limitation to be a hindrance in performing their tasks efficiently.

Confusing user interface: The user interface has been criticized by multiple reviewers, who find it confusing and difficult to navigate. This confusion adds an extra layer of complexity to their tasks and makes it harder for them to utilize the software effectively.

Challenging integration with Python as a data source: Some users have found it challenging to integrate Elasticsearch with Python as a data source. This difficulty can hinder their ability to work with the desired data connections and visualization requirements, making the process more time-consuming and complex than anticipated.

Users commonly recommend the following:

  1. Learning about ELK (Elasticsearch, Logstash, Kibana) before using Kibana is suggested. This can help understand how data flows from the application server to the dashboards, especially when transitioning from another vendor.

  2. Taking advantage of the latest Add-ons for Elasticsearch is recommended to optimize performance under heavy load. Users also suggest utilizing a SQL interface if available to simplify usage. While the free version of Elasticsearch is suitable for simple use cases, the paid version offers additional features like security, machine learning, and alerting.

  3. Using the complete ELK stack (Elasticsearch, Logstash, Kibana) provides the best overview and experience. It is also advised to enforce standards for logging useful information and avoid unnecessary logs to maximize the value of Elasticsearch.

Overall, users recommend Kibana and Elasticsearch for various purposes such as application monitoring, data visualization, log analysis, and search engine functionality. They highlight ease of use, flexibility, and informative visualizations as key advantages. However, some users mention the need for improved customer support and performance considerations under heavy load.


(1-4 of 4)
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Score 8 out of 10
Vetted Review
Verified User
Lets us monitor the performance and health of our mission-critical services with the speed of Elasticsearch. I use it for web server log analytics, searching for vulnerabilities and changes to the file logs and system metrics traffic. Elastic Observability has great search functionality and dashboard visualizations and ElastiFlow to monitor the real-time traffic. We save a lot of time. It does the job extremely well.
  • Fast and speedy search engine
  • Indexes large amount of data
  • Fault tolerance and high availability OOB
  • Difficult to setup/maintain
  • Search pattern bar could be more user-friendly
  • Premium subscription features are very expensive
Great platform for managing application and server logs at a large scale. Elasticsearch can be integrated into 3rd-party software. For example, when combined with Kibana, beats, and logstash to build a full ELK stack it is very powerful and extremely useful for log evaluation, analysis showing detailed information, and creating a monitoring system.
  • Logging and Monitoring
  • API driven Scalable multi-node architecture
  • High availability
  • Migration between versions could require some additional efforts
  • Default log format is often difficult to parse
  • Understanding the tool for a beginner would be challenging
Score 8 out of 10
Vetted Review
Verified User
Elastic observability is mainly used in main problems like Managing many servers of a production deployment. It becomes very difficult to correlate logs and view performance metrics very easily. And Having no ability to detect and resolve these issues by users before they are reported. these are the problems that can be solved by this elastic observability in our company.
  • Open source codebase.
  • APM integration.
  • Documentation.
  • User Interface.
  • Dashboarding.
  • Charting issues.
We can use this Elastic Observability in our business problems such as Creating internal/operational efficiencies issues, customer relations/service, and business process outcomes issues. This product has a lot of features for the above problems. But this product may be having some issues when charting purposes. But it can adjust for that purpose.
  • Integration
  • Deployment
  • Customer service.
  • Cost management.
  • Good customer increment.
  • Time management.
Elastic observability has a lot of features and good customer support. And Overall cost is good. Product functionality and performance are good but have some charting issues. But it is good. Elastic observability has a product roadmap and future vision. And it also has a good and strong user community with a lot of people engaging with good customer support for all needs.
Score 10 out of 10
Vetted Review
Verified User
Managing many servers in a production deployment makes it very difficult to correlate logs and view performance metrics. Tracking issues reported by the end-user is nearly impossible (if at all) in a timely manner. These issues cannot be detected and resolved before users report them.
  • Open source code base
  • Community support
  • Is fast in processing
  • No aspect that interferes negatively.
When data is fully correlated, more products are integrated to enrich the overall experience, but data sources need to be standardized to take full advantage of this. Elasticsearch is very efficient. It is more useful for data analysis and anomaly detection than distributed analysis or application debugging. I don't know how to manage mass production deployments without this tool, without the metrics, correlation, and monitoring provided by the elastic stack, we would be blind to system operation and severely limit our ability to respond to questions in a timely manner.
  • Data analysis
  • Anomaly detection
  • Open Telemetry compatibility
  • Flexibility to store, search and aggregate any type of data, regardless of data source.
  • Price
  • Product Features
  • Product Usability
  • Product Reputation
Invest more time and resources to make the most of available resources as quickly as possible. Use additional resources to provide benefits to other parts of the organization.
Score 7 out of 10
Vetted Review
Verified User
We utilise Elastic in our organisation to keep track of all the logs generated by the various internal services that we have running; we utilise it for monitoring in general but a frequent use case involves looking at logs for incident response purposes to figure out what is actually happening and try to understand any potential impact to the application so that we can take steps to avoid any downtime or negative consquences
  • Licensing model is fair compared with other vendors that charge much more
  • Ability to scale and ingest a lot of data without having to worry too much about performance issues that may crop up
  • Searches return very fast
  • GUI searching interface and filters are intuitive and suitable for new users
  • The DSL advanced search syntax query language can be confusing to use as you have to maintain correct JSON formatting at all times
  • More integrations with other common alerting/monitoring/ticketing platforms
  • GUI hasn't had an update in a while, could benefit from an overhaul with more modern elements
  • Default dashboards are suitable but there could be room for improvements e.g. more advanced custom dashboards
Elastic is a great solution if you want to self-manage your data collection, don't want to pay excessive licensing costs to other vendors for features which are only rarely used and want a competent log aggregation system that returns results very quickly. Scalability is not an afterthought since you can easily grow your log searching and retention resources as the needs of the organisation grow. More and more vendors are building their proprietary solutions on top of Elastic so I believe the open source product will only keep on growing in adoption and getting better each year
  • Ability to relatively cheaply scale your log collection infrastructure as the needs of your organisation grow
  • Search results return quicker than in comparable platforms from other vendors
  • Reliable operation without crashes or downtime
  • Search query language is suitable for most use cases
  • GUI interface is not hard to navigate and can be learned quickly by new colleagues without previous experience working with the product
  • Stopped worrying about unexpected licensing costs arising from all the extra logging our applications will generate in the future as our company grows its customer base
  • Engineers are happy since product is stable and maintenance is not painful
  • Users are happy because search results return quickly
  • We stopped having to make concessions in terms of having to filter out certain data which could turn out later on to be valuable and required
Splunk is a very good product but the licensing costs are high; we utilise the best of both worlds by using both products for slightly different purposes. We put the voluminous data with simple use cases in Elastic where it doesn't cost too much and can be searched quickly while putting the less voluminous data with more complex use cases in Splunk so we can take advantage of Splunk's very comprehensive but often much slower SPL search query language
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