Elastic Observability

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.

• Integration
• Deployment
• Customer service.

• Cost management.
• Good customer increment.
• Time management.

Datadog, Dynatrace and Splunk Enterprise

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.

• 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.

• Data analysis
• Anomaly detection
• Open Telemetry compatibility

• Flexibility to store, search and aggregate any type of data, regardless of data source.

Microsoft SQL Server, Docker, AWS Lambda

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

• 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 Cloud

Looker, Google BigQuery, Kibana, Grafana, Prometheus