Elastic Search Reviews

We are using Elastic Search for free text search. We scan cache files and convert them into OCR. This allows our end users to search for any judgment given in the 1980s or 1990s based on their criteria. 

Elastic Search is very quick when handling a large volume of data. The facet search is particularly valuable. It is scalable. Elastic Search makes handling large data volumes efficient and supports complex search operations.

I use the solution to store historical data and logs to find anomalies within the logs. That is about it. I don't create dashboards from it.

I find the solution to be fast. Aggregation is faster than querying directly from a database, like Postgres or Vertica. It's much faster if I want to do aggregation. These features allow me to store logs and find anomalies effectively.

It is basically for the banking and non-banking sectors. We use it for the APM perspective and application performance monitoring, but not in a holistic way; it is just layer seven, layer five, and six that are there.

In analytics, people use it for search patterns. I've also used Elasticsearch for indexing, where we can have content and do these things. But from an analytics perspective, I have never used Elasticsearch. I have used it in one project

It's a good tool because if you compare it with MongoDB, MongoDB is better. It has a very good data warehouse and search pattern. Elasticsearch cannot be made into a data warehouse. You can use it for smaller-scale analytics, but if you are looking at anything over 30-40 TB, it's not a data lake or big data solution. 

It's a normal database, and any Oracle database or enterprise DB like MSSQL or PostgreSQL can do these things. I've never used it for unstructured data. I have used MongoDB, but not for this.  

I can describe a project where we use Elasticsearch, Logstash, and Kibana (ELK stack) for our archiving objectives. I work in the security department of a Fintech company in the payment industry. We use the ELK stack to connect our internal systems with the bank's systems and we used Beats for data collection. We then store and forward this data to Elasticsearch for indexing and analysis, visualize and create alerts using Kibana based on categorized access logs, identifying and blocking malicious traffic or payloads.

Logsign provides us with the capability to execute multiple queries according to our requirements. The indexing is very high, making it effective for storing and retrieving logs. The real-time analytics with Elastic benefits us due to the huge traffic volume in our organization, which reaches up to 60,000 requests per second. With logs of approximately 25 GB per day, manually analyzing traffic behavior, payloads, headers, user agents, and other details is impractical.

The full text search capabilities in Elastic Search have proven to be extremely valuable for our operations.

Regarding AI integration, we have not yet implemented any AI-driven projects or initiatives using Elastic Search.

We use Elastic Enterprise Search to develop robust and competitive projects. 

We provide clients with a chance to upload a file. They have all the accounts and transactions that they want to do and we have a limit of 10,000. In two minutes, they can have the 10,000 transactions in their accounts.