Name: Elastic Observability
Rating: 8.8 (9 reviews)
Author: Elastic

Help Desk

Web and Video Conferencing

Customer Support

Agile Development

Image Recognition

Kubernetes Monitoring

Service Mesh

Development

Academic Advising

Classroom Management

Online Learning

Education

Barcode

Business Intelligence (BI)

Collaboration

Email Spam Filter

Interactive Voice Response (IVR)

VoIP

Enterprise

Accounting

Digital Banking

Finance and Accounting

Applicant Tracking

Benefits Administration

Corporate Learning Management

HR Management

Payroll

Talent Intelligence

Talent Management

Workforce Analytics

Workforce Management

Human Resources

Cloud Brokers

Cloud Storage

Converged Infrastructure

Data Center Infrastructure Management

Data Lakehouse

Data Observability

Database Load Balancing

Desktop Outsourcing

Ethernet Switches

File Sync and Share

Fraud Detection

Governance, Risk & Compliance

Integration Platform as a Service (iPaaS)

Machine Learning

Network Performance Monitoring

Network Troubleshooting

Operating Systems

Packet Analyzer

Software Defined Storage (SDS)

Tape Storage

Threat Intelligence

Information Technology

A/B Testing

AI Writing Assistant Software

Ad Serving & Retargeting

All-in-One Marketing

Content Management

Digital Signage

Email Management

Email Marketing

Event Registration

Heatmap and Session Recording

Marketing Automation

Social Media Management

Survey & Forms Building

Web Analytics

Marketing

Project Management

Professional Services

Customer Relationship Management (CRM)

Presales

Sales Acceleration

Sales Gamification

Sales

Animation

Auto Repair

HIPAA Compliance

Insurance Suites

Investment Portfolio Management

PCB Design

Public Sector Software

Tax Practice Management

Vertical-Specific

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### Log Management
Users generally find Elastic Observability's log management capabilities to be a strong point, with many praising its ease of viewing, analyzing, and collecting logs from various sources. While some users appreciate the ability to unify logs from different applications and generate log metrics, others express concerns about the inability to combine logs with log files in one place. Despite some challenges with query times and potential system hang-ups due to heavy log volumes, users acknowledge the benefits of reducing engineers' time in reading Kubernetes cluster logs and quickly identifying issues. Additionally, the platform's ability to provide exact exception messages for errors and warnings is highly valued, although some users find it challenging to retrieve old data logs beyond two months. The consensus leans towards Elastic Observability being a reliable tool for log management, offering valuable insights and efficient log processing capabilities.

### Monitoring and Alerting
Users generally praise Elastic Observability's monitoring and alerting capabilities. They appreciate the ease of setting up watchers in Kibana to receive timely alerts for critical events, enabling proactive monitoring and swift response to issues. The integration with Kubernetes clusters has been particularly beneficial, allowing for quick identification and resolution of issues at the cluster level. While some users mention the need for improvements in monitoring older data sources and the initial setup complexity, overall, Elastic Observability's monitoring and alerting features are seen as valuable assets in enhancing operational efficiency and ensuring system reliability.

### Kubernetes Observability
Users generally appreciate Elastic Observability's strong Kubernetes observability capabilities. The ability to efficiently search and analyze Kubernetes logs has been highlighted as a key feature that streamlines issue identification and resolution within Kubernetes clusters. The integration of Elasticsearch within Kubernetes environments has been commended for its ease of deployment and performance benefits. Despite some initial complexity in setting up Elasticsearch, users find that its functionality significantly enhances the monitoring and troubleshooting processes within Kubernetes infrastructures.

### Search and Query Tools
Users generally praise Elastic Observability's search and query tools for their flexibility, ease of use, and powerful analytics capabilities. The ability to search with any text from log records, apply various filters, and quickly identify errors or issues within the logs is highly valued by users. Despite some challenges with query complexity and query language nuances, users appreciate the efficiency and time-saving aspects of Elastic Observability's search and query tools. The platform's integration with various tools and components, along with the support for distributed computing, further enhances its appeal for users seeking robust search and query functionalities.

### API and Integrations
Reviewers generally praise Elastic Observability's API and integration capabilities. Users appreciate the flexibility and ease of integration with various tools and components. The developer-friendly APIs and query mechanisms are highlighted as strong points, allowing for quick and efficient data retrieval. While some users mention minor issues such as slow response times with large log files, overall, Elastic Observability's API and integrations are seen as valuable assets for enhancing search and query functionalities.

### Data Visualization and Dashboards
Users generally appreciate Elastic Observability's data visualization and dashboards capabilities. The tool is commended for its user-friendly interface, offering a variety of visualizations such as pie charts, histograms, and more. However, some users find the UI features slightly challenging and suggest room for improvement in terms of visualization compared to other tools like Grafana. Despite this, the majority of users find Elastic Observability's data visualization and dashboards to be a valuable asset in monitoring and analyzing their data effectively.

### Real-Time Analytics
Reviewers are consistently impressed by Elastic Observability's real-time data processing capabilities. Users have highlighted the tool's ability to handle millions of records effortlessly and execute complex queries efficiently in real-time. The flexibility and speed of Elastic Observability's real-time analytics have garnered positive feedback, with users expressing satisfaction with the tool's performance in processing and visualizing data instantaneously. Additionally, the option for horizontal scaling to accommodate potential future load increases has been noted as a valuable feature by users.

### Performance and Scaling
Users consistently highlight Elastic Observability's exceptional performance when handling vast amounts of data, particularly in the millions, and its efficient scaling capabilities. The platform's ability to manage and analyze logs from diverse sources across multiple environments has garnered positive feedback for its reliability and ease of use. Additionally, users appreciate the open-source nature of Elastic Observability, which not only provides cost-effective solutions but also ensures strong community support for addressing any challenges that may arise during implementation and utilization.

### Data Storage and Indexing
Users generally praise Elastic Observability's efficient document storage and indexing capabilities. They appreciate the flexibility in storing various document types and the automatic recognition of new document formats. The clustering and backup features provide a sense of security for the stored data. While some users find the bulk storage speed to be slow, overall, the consensus leans towards Elastic Observability being a reliable choice for NO-SQL search engine needs. The availability of REST APIs for easy search requests and JSON output for front-end compatibility are highlighted as significant advantages by users.

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.

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 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.

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 environment. It's free and open to start, and adds the Logs, Metrics, APM (formerly Opbeat), and Uptime modules to the Elastic (ELK) Stack.

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

SolarWinds Network Performance Monitor (NPM)

Amazon CloudWatch is a native AWS monitoring tool for AWS programs. It provides data collection and resource monitoring capabilities.

First 10,000 Metrics

Next 240,000 Metrics

Next 750,000 Metrics

Over 1,000,000 Metrics

Dashboard

Alarm - Standard Resolution (60 Sec)

Alarm - High Resolution (10 Sec)

Alarm - Composite

Logs - Collect (Data Ingestion)

Logs - Store (Archival)

Logs - Analyze (Logs Insights queries)

Events - Custom

Events - Cross-account

Contributor Insights

Contributor Insights - Matched Log Events

Canaries

CloudWatch Evidently - Events

CloudWatch Evidently - Analysis Units

CloudWatch RUM

Amazon CloudWatch

Elastic Observability

Observability

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

Likelihood to Recommend

Eastic (NYSE: ESTC) is a search company. As the creators of the Elastic Stack (Elasticsearch, Kibana, Beats, and Logstash), Elastic builds self-managed and SaaS offerings that make data usable in real time and at scale for use cases like application search, site search, enterprise search, logging, APM, metrics, security, business analytics.

Amazon Web Services

Splunk Enterprise

Datadog

Logstash

Dynatrace

SolarWinds Database Mapper

Software Engineer

Elastic Observability 2022-05-01 12:59:36

Elastic Observability 2022-06-21 20:36:29

Microsoft SQL Server

AWS Lambda

Elastic Observability 2022-05-02 11:53:22

Looker

Google BigQuery

Splunk Cloud Platform

Prometheus

Kibana

Grafana

Elastic Observability 2022-04-28 10:02:30

TrustRadius is a technology and business research firm based in Austin, Texas. The company is known as a review platform for verified B2B technology and software reviews through a proprietary algorithm and human verification.

TrustRadius

Query language

Confusing to use

Room for improvements

Log files

Custom dashboards

User friendly

User interface

Mobile applications

Experienced user

Advanced search

Open source

Ability to scale

Code base

Lot of data

Community support

Performance issues

Search and find

Log file

Root causes

Bit complex

Home

Observability Tools

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.

Cost management.
Good customer increment.
Time management.

Amazon Web Services, SolarWinds Database Mapper, Logstash

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

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

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

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.

Elastic Observability

Overview

What is Elastic Observability?