Elastic Cloud (Elasticsearch Service)

We use Elastic Search  for a research application based on paper study, and the primary usage is for indexing the data and then functioning in a similar way to an e-commerce search bar.

For us, what I can notice is the ability of adding weights to each field of the data, which is very useful because sometimes the user searches the data not just by the title, but by specific keywords, and being able to add weight to the fields in order to show that information to the final user is very useful. Also, the panel for showing graphs about the data and how the users are interacting with it is pretty useful.

The difference in performance of Elastic Search  is outstanding; if we compare a traditional database or service for search and index products or, in this case, papers, the difference is outstanding. That is the case when you want to filter the data; the primary advantage will be performance for sure.

Again, the primary improvement will be performance, and the interactivity we can have with the data is very flexible; it adapts to the needs of the user very easily.

I cannot see any issues at this point; the panel is great. The way to customize and configure the panel and the search is great; it is really visual. Documentation is great as well.

The initial configuration could be easier; at first, the learning curve is a little high, and over time, it becomes easier. For me, the initial configuration might be improved.

I have around three years of experience.

Stability has not been an issue; it is working perfectly in that aspect.

Scalability has not been an issue for now.

In the case scenario when we need to face support, support was really useful, and they answered the questions in a good period of time.

Cassandra  was one we were evaluating, but we preferred Elastic Search because the documentation was way better and the community was bigger. It is easier to find answers when we face a problem, and that is why we chose Elastic Search.

At first, we faced several issues related to some versioning and allowing indexing the database because part of our information is in a traditional SQL database, and we were using the IDs from the index for the records in Elastic Search. We created a little ETL for that, and handling that process was tricky and harder at first. That was the biggest challenge we faced when starting to set up Elastic Search.

I would say that first, contact support for the initial setup; I think it will make the process easier. Then start, for example, with how to send and retrieve the data in the documentation; I think that is the best thing they can do.

For that one, my field, the PO and the technical leader is the one that handles the bills about Elastic Search.

I am on the side of implementing it, so in terms of cost-efficient or the price of using it in the cloud, that is not something I am really involved with; I am more on the dev-ops side.

It was great; the developer experience is great when integrating either the frontend or the backend side. Nothing so complex could not come.

For implementing Elastic Search, I would say good documentation, and it is really easy to use. We have an example of almost every functionality that is inside of Elastic Search framework, so that is helpful. I would provide a rating of ten for this product, and I say a ten; it is really good.

My main use cases for Elastic Search  involve search capability. For instance, I built a banking product application, the PFM  personal information system, requiring search capability and fuzzy search using Elastic Search . Additionally, I use third-party API data to build a super app in the insurance domain, where I collect requests and responses from APIs and store the logs in Elastic Search for debugging purposes, analyzing the data using the Kibana dashboard.

I previously used Space Cloud to build similar functionality; however, it does not support fuzzy search, which is why I switched to Elastic Search for those requirements.

One of Elastic Search's best features is its search capability due to the index-based data management and lifecycle of unstructured data, primarily in the form of JSON, allowing for historical data storage and multiple indexes.

When using traditional keyword and full-text search capabilities, my experience with Elastic Search's performance indicates that the results are obtained much quicker compared to traditional SQL queries, demonstrating superior efficiency.

Elastic Search fulfills my use case requirements effectively, both for my current and previous needs, which is why I rely on it.

Elastic Search positively impacts my company with many benefits across multiple use cases; for example, it enables quick dashboard setups for client reviews and presents data efficiently, ensuring good user experience.

I think Elastic Search could be improved by introducing more AI features, particularly for complex queries and aggregator functions to enhance usability and readability.

Over the last four years, I have been using Elastic Search, including both the open-source version and the open search provided by AWS .

Elastic Search is stable in my experience.

Regarding scalability, Elastic Search provides horizontal scalability options on AWS , allowing me to scale according to my requirements and traffic.

Technical support for Elastic Search is satisfactory, with quick solutions provided by support teams and active open forums available. I rate customer service and technical support as an eight out of ten.

Before choosing Elastic Search, I evaluated other products like Space Cloud and three to four different banking applications, ultimately finding Elastic Search to be the most capable option.

The initial setup process of Elastic Search is straightforward, with comprehensive documentation available for installation guidelines that make it easy for beginners.

Pricing for Elastic Search setups is dependent on requirements and use cases, but I find the enterprise license to be reasonable in comparison to other products.

I am currently using Elastic Cloud Serverless .

My application is hosted on AWS cloud, utilizing managed services including the open search, which is a component of Elastic Search.

I use the ELK stack for log ingestion and visualization of application logs via Kibana.

I find that the ability to parse and structure raw logs without agents requires different approaches for each use case.

I am using the Attack Discovery feature.

The discovery feature helps me correlate alerts by writing custom queries to retrieve logs based on specific criteria.

I utilize generative AI models like Claude AI and Anthropic within the discovery context for better log analysis.

From a technical point of view, integrating AI capabilities within Elastic Search enhances its value, showcasing the potential for using models and RAG in my systems.

I recommend Elastic Search for companies with substantial data needs or searching requirements, considering it the best search engine. I have provided an overall review rating of nine out of ten.