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AI Logging Power House
What do you like best about the product?
The bulk logging features and an ability to index, store and search data with ease
What do you dislike about the product?
Complexities involved in having ready out of the box solution for deep dive Observability and log based metrics and insights.
What problems is the product solving and how is that benefiting you?
A single Logging Repository store for IOT workloads and thousands of stateless infra elements used in our product architecture.
Nice product
What do you like best about the product?
Easy of use, reliable and good customer support
What do you dislike about the product?
Dashboard with using big index takes time to load
What problems is the product solving and how is that benefiting you?
Showing visualization from the data
Elasticsearch – Fast, Flexible, but Needs Care
What do you like best about the product?
I’ve been using Elasticsearch for a while now, and the first thing that consistently impresses me is its speed. No matter if I’m searching through logs, text, or analytics data, it delivers results incredibly quickly once it’s properly configured. I also like how well it scales; adding more nodes allows it to handle larger and larger workloads smoothly.
I also appreciate its flexibility. Elasticsearch supports everything from simple keyword searches to more advanced aggregations, autocomplete, and even fuzzy matching.
I also appreciate its flexibility. Elasticsearch supports everything from simple keyword searches to more advanced aggregations, autocomplete, and even fuzzy matching.
What do you dislike about the product?
Elasticsearch is not particularly plug-and-play. There is a noticeable learning curve, especially when it comes to configuring clusters, tuning shards and replicas, and maintaining stable performance as your data volume increases. If you don't size your setup correctly, it can also become quite resource-intensive.
What problems is the product solving and how is that benefiting you?
I mainly use Elasticsearch as an enterprise search tool. It’s where we send a ton of data — logs, records, documents — so people can quickly find what they’re looking for. Instead of digging through raw databases, we can just search and get results instantly.
Before Elasticsearch, searching across big datasets was slow and frustrating. Now it’s basically instant. It handles millions of records without breaking a sweat, and the results are super accurate.
The biggest win for us is speed and scale — things that used to take forever now take seconds. That means faster troubleshooting, better insights, and less wasted time for the team. It just makes working with large amounts of data way more practical.
Before Elasticsearch, searching across big datasets was slow and frustrating. Now it’s basically instant. It handles millions of records without breaking a sweat, and the results are super accurate.
The biggest win for us is speed and scale — things that used to take forever now take seconds. That means faster troubleshooting, better insights, and less wasted time for the team. It just makes working with large amounts of data way more practical.
Amazing solution for introducing AI search with great company support
What do you like best about the product?
The tool is comprehensive yet still approachable and well documented for all configuration needs.
What do you dislike about the product?
Creating a support case does not always lead to quickly talking to a domain expert and it's often better to go through the sales engineer for help.
What problems is the product solving and how is that benefiting you?
Enterprise data search and monitoring/logs
Elasticsearch Review
What do you like best about the product?
- Reliable at scale: sharding and replication deliver solid HA; rolling restarts and node recovery are predictable when procedures are followed.
- Great for observability: fast searches/aggregations and the Elastic stack make log/metrics/APM pipelines effective for troubleshooting.
- Good ops surface: rich APIs and CAT endpoints make it scriptable, monitorable, and easy to automate runbooks.
- Great for observability: fast searches/aggregations and the Elastic stack make log/metrics/APM pipelines effective for troubleshooting.
- Good ops surface: rich APIs and CAT endpoints make it scriptable, monitorable, and easy to automate runbooks.
What do you dislike about the product?
- Finicky to run well: JVM/heap sizing, shard counts, and segment merges need care—or they bite during peak.
- Changes can be risky: upgrades, reindexing, and rebalances can cause latency spikes without tight change control.
- Costly footprint: hot nodes are CPU/IO heavy; replicas and long retention drive storage costs; licensing/features add complexity.
- Changes can be risky: upgrades, reindexing, and rebalances can cause latency spikes without tight change control.
- Costly footprint: hot nodes are CPU/IO heavy; replicas and long retention drive storage costs; licensing/features add complexity.
What problems is the product solving and how is that benefiting you?
Elasticsearch lets us deliver fast, relevant search and discovery across our articles. New articles become searchable within seconds, and aggregations power features like “most read,” topic pages, and related-article widgets. Flexible analyzers handle titles, body text, tags, and authors without rigid schemas, while replicas keep article search available during node failures. Net result: lower query latency, higher reader engagement, and a simpler path from article publish to discovery.
Elasticsearch at big belgian bank
What do you like best about the product?
API, dev console, schema-less indexing, documentation
What do you dislike about the product?
changing java sdk, hiding some previously available features behind a subscription
What problems is the product solving and how is that benefiting you?
excellent vector search, good indexing/search performance, support complex searches
The solution is modern and feature rich with extensive customization possibilities
What do you like best about the product?
The amount features present and you can do many custom things with it if something is not present out of the box, we really like the security monitoring features it provides
What do you dislike about the product?
Maintaining self managed deployments can be difficult, mapping conflicts and slow downs when ingesting many log sources can take a lot of time.
What problems is the product solving and how is that benefiting you?
Log collection and threat monitoring
Evaluation of Elasticsearch Efficiency Across Use Cases
What do you like best about the product?
The best thing I like about Elasticsearch is that its not limited to 1 or 2 features. I have been using ELK for implementing different use cases like the diverse search options like advanced relevance ranking, fuzzy search, autocomplete, and complex aggregations, analytics, monitoring.
The horizontal scaling feature eases the upgrade as data grows and query demands increase. Data ingestion, search queries, and cluster management can all be done via simple JSON-based API calls. Creating dashboards in Kibana can be quickly learnt and offers great insights on the metrics. It also much easier to connect using different languages with the official or community client libraries available.
We are also using Elasticsearch for real-time querying of logs and metrics for which ingestion is happening 24/7 and the dashboards are being monitored.
With the new AI features I see the use cases will continue to grow.
The horizontal scaling feature eases the upgrade as data grows and query demands increase. Data ingestion, search queries, and cluster management can all be done via simple JSON-based API calls. Creating dashboards in Kibana can be quickly learnt and offers great insights on the metrics. It also much easier to connect using different languages with the official or community client libraries available.
We are also using Elasticsearch for real-time querying of logs and metrics for which ingestion is happening 24/7 and the dashboards are being monitored.
With the new AI features I see the use cases will continue to grow.
What do you dislike about the product?
The one thing I dislike is sometimes the data is inconsistent and finding the reason for that is real pain because at one point it works perfectly fine and then shows incorrect data. One more thing I find confusing is the errors that are displayed when something goes wrong. The errors are not that insightful in some cases which leads to more time correcting them.
What problems is the product solving and how is that benefiting you?
We are storing Cloud based customer support data in Elasticsearch which is really huge and we have implemented real-time monitoring on top of it. It includes multiple complex dashboards and search options available to help the business person in monitoring and growing the business.
Fast Search Engine with a Learning Curve
What do you like best about the product?
Elasticsearch fast search performance. ability to perform full-text search, aggregations, and real-time analytics integrates with tools like Kibana, Logstash, and Beats and etc
What do you dislike about the product?
CCR is complex concept and considerable effort is needed for it
What problems is the product solving and how is that benefiting you?
logs analysis and reporting
Review of Elastic
What do you like best about the product?
APM feature, I like the APM feature in Elastic which helps to identify the endpoints failing or services which were not healthy at any point of time. The way it shows the failure transaction, latency throughput and mapping with services is useful in my daily works. The dependencies feature is great addon to identify what other services are being affected due to the issue.
What do you dislike about the product?
Searching for aged logs. In one of our clusters, it is hard for us to get the aged logs when we search with any pattern. Don't think this is fully due to Elastic it has more to do with our logs and tier configuration too. Also getting the logs and metrics of database server is something I feel hard.
What problems is the product solving and how is that benefiting you?
Solving unexpected Major outages. Elastic helped us to identify the outages before customer is impacted with APM metrics, error alerts, Machine learning jobs. With the alerts and monitoring, we are able to notice the behavior early and fix the issues. Due to fill log ingestion in elastic, it is helpful in even single customer issue analysis. The tracing of the logs is beneficial.
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