Indexing millions of daily records has been streamlined and search performance meets our needs
What is our primary use case?
Elastic Search use cases for us involve maintaining a huge amount of data per day, around millions of transactions for each record. We are maintaining all this data with Elastic, and Elastic is doing a fantastic job by doing the indexing. The algorithm is very good, enabling us to process the data very fast.
We are conducting searches with Elastic Search because the data volume is too high. With a couple of indexing configurations, we are able to achieve our goal.
What is most valuable?
A good feature of Elastic Search is that they have something called policies, which we can make hot and cold, all related to data retention, and that is what I appreciate the most.
What needs improvement?
From the UI point of view, we are using most probably Kibana, and I think they can do much better than that. That is something they can fine-tune a little bit, and then it will definitely be a good product.
Maintenance in terms of Elastic is that they can improve the UI and UX, and if they fine-tune it a little bit, then it will be much better.
For how long have I used the solution?
I have used Elastic Search for the last two years in my career.
What do I think about the stability of the solution?
So far I haven't noticed any lagging, crashing, or downtime with Elastic Search.
What do I think about the scalability of the solution?
The scalability of Elastic Search is good, and I am satisfied with that as of now, and the performance is good.
How are customer service and support?
I don't think I have ever had to contact technical support.
How would you rate customer service and support?
How was the initial setup?
I find the initial deployment of Elastic Search easy; it is quite straightforward.
Approximately, I am able to deploy Elastic Search within two to three hours for the first time.
What about the implementation team?
To deploy, one or two people will be enough because you need Logstash to be configured to bring the data to Elastic Search for indexing.
Which other solutions did I evaluate?
We tried to implement big data pipelines and all, and we tried to use Spark as well for analytics and data cleaning, but I think Elastic is better in that field. I didn't find anything better than that.
Fast, Scalable Elasticsearch for Quick Log Analysis
What do you like best about the product?
From our use, Elasticsearch is fast, scalable and provides quick results for querying which makes it very useful for any log analysis
What do you dislike about the product?
Operational cost is increasing
Shard allocation and indexing can be made easier to configure
What problems is the product solving and how is that benefiting you?
We use ELK for log parsing, and with it its ability to respond quickly to queries helps us identify issues and get clues about what’s going wrong much faster.
Powerful and Scalable Search Solution
What do you like best about the product?
What I like most about Elasticsearch is its speed and flexibility. It handles large amounts of data efficiently and makes searching very fast. It is also versatile enough to be used for both search and analytics use cases.
What do you dislike about the product?
One thing I dislike about Elasticsearch is that it can become complex to manage as it grows. It requires careful planning and monitoring to avoid performance and stability issues. Licensing and pricing changes over time have also created some uncertainty for users.
What problems is the product solving and how is that benefiting you?
Elasticsearch helps us quickly search and analyze large amounts of data in one place. It makes it easier to find relevant information, monitor systems, and generate insights from logs or application data. This improves visibility and allows us to respond to issues faster and make better decisions.
Powerful Log Database with Helpful Integrations for Easy Parsing
What do you like best about the product?
You can use it as a database and classify all type of logs. The integrations they have helps you to parse them
What do you dislike about the product?
Sometimes correlations can be difficult between different technologies
What problems is the product solving and how is that benefiting you?
Handling logs
Efficient Log Management & Search with Excellent Support
What do you like best about the product?
Very efficient product to manage our logs and search
The support is easy to interact with and the quality of the answers are perfect
What do you dislike about the product?
When it is self managed, a bit tedious to update
What problems is the product solving and how is that benefiting you?
Centralizing our documentation and making it available in quick search is really great
Best No-SQL Databases with vector search and AI use cases
What do you like best about the product?
It’s one of the best NoSQL databases on the market. It makes it easier to collect logs from many different sources and to define integrations for them. It provides many features within one tool like vector search, machine learning, alerting and a lot
What do you dislike about the product?
I don’t like the breaking changes that come with version upgrades, because they have a big impact when multiple teams depend on the deployment.
What problems is the product solving and how is that benefiting you?
We collect telecom metrics from around 1,000 servers, which helps us search for and debug errors, create KPIs, and set up rules and alerting based on that data. As a result, it reduces manual effort and is easy to integrate with other systems. The best part is elasticsearch can be used for varied use cases. Its a single point of monitoring for our whole telecom stack.
Real-Time Bet Monitoring That Helps Us Improve Before It Happens
What do you like best about the product?
It helps us monitor bets in real time, and we can even see where we need to improve before it happens.
What do you dislike about the product?
It gives us a real-time view of our infrastructure logs. The downside is that shards sometimes get corrupted, and we need to restore them, but we don’t have clear visibility into that process.
What problems is the product solving and how is that benefiting you?
It provides operators with real-time logs and supports the compliance team in meeting regulatory requirements.
Powerful Search Platform for Enterprise-Scale Operations
What do you like best about the product?
What I like best about Elasticsearch is its powerful search and aggregation capabilities combined with high performance at scale. We support over 100 customers who use it daily in their operations, and Elasticsearch consistently handles large data volumes with fast response times.
From a support perspective, features like detailed query capabilities, clear APIs, and strong integration within the Elastic Stack significantly improve our workflow. Kibana dashboards help us quickly analyze customer issues, review logs, and identify performance bottlenecks without needing custom tools. This often reduces troubleshooting time from hours to minutes.
An unexpected benefit has been how flexible and scalable the platform is across different customer environments. It allows us to support diverse use cases while maintaining a relatively standardized architecture.
What do you dislike about the product?
One of the main challenges with Elasticsearch is the complexity of configuration and tuning, especially in larger or high-availability clusters. For customers without deep expertise, settings around JVM tuning, shard allocation, and performance optimization can be difficult to manage. This often increases the support workload and extends troubleshooting time.
Version upgrades can also be demanding. Breaking changes between major versions and strict compatibility requirements sometimes require careful planning and additional testing, which impacts customer environments and maintenance windows.
Customers often ask about the possibility of reverting to the previous version, but this is not possible.
In such cases, we have to come up with our own workarounds.
Improved backward compatibility, clearer upgrade paths, and more built-in automated diagnostics for cluster health and performance tuning would significantly reduce operational overhead for both customers and support teams.
What problems is the product solving and how is that benefiting you?
Many of our customers struggled with slow database searches, limited reporting capabilities, and fragmented log storage. Troubleshooting incidents often required manually checking multiple systems, which was time-consuming and inefficient.
With Elasticsearch, they can centralize logs and operational data, perform fast full-text searches, and build real-time dashboards. As a result, tasks that previously took hours - such as identifying the root cause of an issue - can now often be completed in minutes.
For us as a support team, this has significantly reduced resolution times and improved SLA compliance. In many cases, incident investigation time has decreased by 50% or more, which directly benefits both our customers and our internal operations.
Fast, Flexible, and Innovative—Elasticsearch at Its Best
What do you like best about the product?
I appreciate its speed, flexibility, and innovation.
What do you dislike about the product?
There isn’t much to dislike about Elastic Search.
What problems is the product solving and how is that benefiting you?
It’s helping us improve our search platform and making it better overall.
Elasticsearch: The Best Engine for Fast Data Search and Analysis
What do you like best about the product?
Elasticsearch is the best platform/engine to analyze and search your data. With the AI capabilities Elastic is developing, it becomes even more powerful. Besides the company offers an excellent support.
I cannot imagine the current internet and technological world without Elasticsearch.
What do you dislike about the product?
Documentation is sometimes hard to follow and navigating it feels confusing.
What problems is the product solving and how is that benefiting you?
You just put your data in Elasticsearch, and it can produce value. No matter if the data comes from old databases, files, logs, etc. Once it´s in Elasticsearch you extract all the value and knowledge from it.
