Elasticsearch Reviews & Product Details

Everything from handling ingestion to observability + ML + SIEM +XDR + reporting Review collected by and hosted on G2.com.

it is good and bad in the same time , it is hard to follow all new features at time.

plus if more concret application is added o doc this would be great for better understanding of functialities Review collected by and hosted on G2.com.

I like best how Elasticsearch handles large amounts of data in a scalable and efficient manner, making it easier to manage and scale as my data grows. Additionally, the extensive community support and integration with other tools make it a valuable addition to my data processing workflow. Review collected by and hosted on G2.com.

While I appreciate the many benefits Elasticsearch provides, some drawbacks include the steep learning curve, potential complexity in management and maintenance, and the risk of performance overhead. Additionally, ensuring security configurations and managing data consistency issues are crucial to avoiding potential data loss or corruption. Review collected by and hosted on G2.com.

Best and scalable am using at central cluster which pipes the metrics and logs from several other clusters Review collected by and hosted on G2.com.

shards /documents runs out of limit more often Review collected by and hosted on G2.com.

Elasticsearch is awesome for fast and flexible search. It’s great at handling huge amounts of data and giving near-instant results. You can search, filter, and analyze text, numbers, logs pretty much anything. It’s super helpful for building search engines, monitoring systems, and real-time dashboards. Speed, scalability, and powerful full-text search. Review collected by and hosted on G2.com.

Elasticsearch is powerful but not always easy. It can throw errors that are hard to trace, especially with complex queries. Setup and scaling take effort, it uses a lot of resources, and security features are limited unless you pay. Review collected by and hosted on G2.com.

What stands out to me is how easy it is to integrate, along with its impressive capabilities for text search. Additionally, I appreciate the flexibility it offers when it comes to working with the schema. Review collected by and hosted on G2.com.

This isn't always the primary database, so running two databases in production can be a hassle, especially when it comes to keeping them in sync. Review collected by and hosted on G2.com.

1. Near real-time search

2. Hugh Scalability

3. In our scenario, it helps us to centralize logs and metrics from different systems into one searchable platform, helping our IT ops and security teams troubleshoot issues quickly.

4. It supports full-text search, filters, geospatial queries, and many more, all in the same engine. Review collected by and hosted on G2.com.

1. High resource usage - It is high CPU and memory hungry product.

2. It is quite expensive and complex to manage at scale Review collected by and hosted on G2.com.

As a Lead Solutions Architect, I've worked extensively with Elastic over the past few years, and it has become a cornerstone of our infrastructure. From log aggregation to real-time analytics and observability, Elastic consistently delivers high performance and flexibility.

We use Elasticsearch to power dashboards that process large volumes of data from various sources, including MySQL and Elastic Search itself. The ability to create custom indexes, mappings, and use REST APIs like Bulk and Multi Get has made our data ingestion and retrieval seamless. The platform’s support for metrics and aggregations has helped us build meaningful visualizations and improve operational decision-making.

Elastic’s integration with cloud platforms like Azure and AWS has been smooth. We've deployed Elastic Stack in production environments and leveraged its capabilities for distributed search, logging via Logstash, and visualization through Kibana. The training materials and internal documentation have been instrumental in onboarding new team members and scaling our usage.

What stands out most is Elastic’s commitment to innovation. Their recent push into Search AI and generative AI-powered applications, as highlighted in Elastic{ON} events , shows they’re not just keeping up—they’re leading.

Pros:

Powerful search capabilities with support for vector and semantic search

Scalable architecture for large datasets

Seamless integration with cloud and container platforms

Excellent visualization tools via Kibana

Strong community and documentation

Cons:

Initial setup and tuning can be complex for new users

Licensing and pricing models could be more transparent Review collected by and hosted on G2.com.

Cons:

Initial setup and tuning can be complex for new users

Licensing and pricing models could be more transparent Review collected by and hosted on G2.com.

Elastic has a great community and support that can be talked to and used in order to create and implement solutions. There are a plethora of prebuilt features in the platform such as the security solution that you can leverage and integrate with other platforms in order to create the solution that you need. I am in Elastic every day and am able to create and monitor the solutions I need easily in order to perform my job. Review collected by and hosted on G2.com.

With Elastic there are many features and some of which start to feel the same but with a different spin. Due to the sheer amount of features, sometimes it appears that something isn't possible, but it is; you just used the wrong method at the start and now have to go back and change some items around in ingest as an example in order to make it possible. There's no one way of doing things which sometimes makes it complicated as you know it may be able to be done but you just didn't pick the correct method. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

The platform is very easy to use and very easy to integrate with GCP. We were able to get it to work directly in our tool with 0 issues. Review collected by and hosted on G2.com.

Expensive to scale. We have a lot of data we use to search and elastic just costs a lot so we need to set up lifecycle management Review collected by and hosted on G2.com.