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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

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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)?

Splunk APM is an application performance monitor that uses AI to drive troubleshooting of issues that arise within applications.

What is SolarWinds NPM?

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

Operating SystemsUnspecified
Mobile ApplicationNo
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Reviews and Ratings



(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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