Elasticsearch Reviews

In a cloud universe where we have hundreds or even thousand of servers to manage, is is a huge challenge to figure out the root cause of issues, it is totally unacceptable keep this sort of environment without a reliable logging and analysis system. Being part of the ELK stack, Elasticsearch give us what is necessary to handle this huge amount of data. I can't imagine our environments without Elasticsearch nowadays.

Elasticsearch is used on our B2B and B2C eCommerce websites to provide fast and powerful search capabilities for products. Search by title, artist, or various facets like genre, price-range and availability-date results in a list of products that the user can then drill down or continue searching within the result list. Within the organization, Elasticsearch is used by the programmers in the IT department.

Elasticsearch is being used for multiple purposes in multiple projects: centralized log management, APM, Metrics Collection as a TSDB, and as a replacement for traditional OLAP databases. It provides a high-performance indexing and search engine, which has become an invaluable tool addressing hard problems that would otherwise be very difficult to solve.

Elasticsearch has been a big help for us. We used to work with Application Performance Management Tools that need another layer of visualization and data treatment, and with Elasticsearch we have delivered better insights for our customers.
We use Elasticsearch at our Technology & Services Department to address these issues for our customers:
- Customized Dashboards.
- Anomaly Detection.
- Metrics Predictability.

The most crucial piece of infrastructure behind my company's whole product line is Elasticsearch. Our company's big selling point is an extremely flexible data model for our customers who send us their data. We want them to be able to send us data in almost whatever shape or form they want (as long as it's valid JSON we'll take it) and yet, make it still searchable. And you know how we store that nearly-unrestricted free-form data? Elasticsearch!

Elasticsearch is used as a full-text search solution in most of my use cases. We have another analytics us -case which uses Elasticsearch for both text search and aggregation use-cases.

Elasticsearch has been phenomenal in upgrading the log management capabilities at my organization.
Elasticsearch, along with Kibana, has provided a wide range of capabilities for our IT teams to investigate issues and create live monitoring environments.
This is being utilized as a central tool for all of our apps organization-wide.
Before Elasticsearch, our teams were finding a hard time investigating issues, tracking the root cause, and resolving them. Elasticsearch have greatly reduced the investigation time for us.

We currently use it to log the details of our RPA processes as they run through their production and development environments. They log back checkpoints, statues and error messages back to the Kibana database we use in conjunction with Elasticsearch.

ElasticSearch is used to store all searchable data indices from our product. We use ElasticSearch because it is extremely fast, highly available, and able to meet the demand of our product. We were using a different index-based search technology before, and it failed terribly. We migrated to ElasticSearch and have been very happy with the results.

Elasticsearch (ES) is being used to measure the performance metrics of our web crawlers for our web metrics department. They employ a series of crawlers: setting up data feeds to an ELK stack to measure and monitor organic messages related to our marketing campaigns. It primarily allows us to bring advanced analytics in-house.

It provides a distributed, multitenant-capable, full-text search engine with an HTTP web interface and schema-free JSON
documents. We use this in our IT department, but also resell it as part of a predictive AIOps platform that offers automation for many of the tedious tasks that data center staff struggle with every day.

We use Elasticsearch for our online (realtime) search engine. We've indexed over 2 billion documents, including every physician, hospital, and clinic in the United States. We started using ES from the beginning since I had a bunch of great experiences with the technology from my last job. We load data into Elasticsearch from multiple locations, including Postgres and BigQuery. On top of Elasticsearch, we've built a number of analytics tools that let us not only search but also deliver analytics for our stored data -- like top physicians performing a specific service and geography-based analyses. Overall we're super happy with Elasticsearch.

Elasticsearch is being used for multiple purposes at Iconscout. Starting from a search engine to viewing detailed analytics. We're even using it for logging of the server. It helps us to query through the millions of data easily and efficiently.

It is used in our custom software application for advanced searching and filtering capabilities for our users.

We use Elasticsearch to power a web search engine that allows users of our web platform to search for products, content, and more. With Elasticsearch we were able to quickly and effectively develop and deploy a search solution that is fast, scalable, and was a breeze for our developers to implement.

We use Elasticsearch to efficiently search large pools of data. Elasticsearch gives us the ability to have blazing fast searches even when doing partial text matches on multiple fields.

We decided to start looking into Elasticsearch after we had good success with using lucene (the full-text search indexer that Elastic uses). We had some queries in Oracle that were running EXTREMELY slow and knew we had to do something for the customer to make their experience better. We had a few thoughts on what we could use and Elasticsearch fit what we really wanted.

We use Elasticsearch to store data for quick querying of our various data sets via our APIs. It has allowed us to write APIs that perform much faster compared to their older versions that had complex relational queries.

We also use Elasticsearch to store log data for fast querying via Kibana.

We utilize Elasticsearch (with Kibana and Logstash) to provide log management services internally and as an offering to our IT clients. This helps clients meet compliance regulations requiring log review and SIEM implementation without paying the premium at other high-end products. In essence, Elasticsearch allows us and our clients on the platform to gain greater visibility into their applications and endpoints.

We use Elasticsearch as the storage/search component of our logging infrastructure (ElasticStack). Once we have broken apart the individual variable components of each log as their own variable type using Logstash, we store those records in to Elasticsearch. Kibana queries Elasticsearch to display the resulting data. We also utilize Elasticsearch to display the cluster status for each of our markets across our entire web cluster using an internal reporting tool we wrote.

Elasticsearch helped us to provide comprehensive reports, and frequent queries on our data (millions of rows), provided us a performance that we could not achieve before (though we have only 40 concurrent users at most) We also consolidate data from many sources within our company, and elasticsearch made it easy for us to do data analyzing, to have many useful insights of our data; things that we could never do (so easily) in the past.

Elasticsearch is used across the whole org. It's used mainly for storing and searching application logs. We have many elastic clusters set up differently. Sometimes it's one cluster per app; sometimes it's one cluster for many apps; depending upon the volume of data being generated. Elasticsearch is used mainly for debugging purposes rather than metrics, but sometimess it's used along with Kibana to visualize metrics also.

Elasticsearch is currently in our organization for multiple use cases. With the data volume growing huge and rapidly, we push the data into an Elasticsearch cluster setup. We collect logs from multiple systems and push into E C using logstash and few other message brokers system. We collect telemetry from multiple systems and run algorithms to analyze the data.

The first use case is log aggregation. We have a number of micro-services running, some of them in Docker, and we use the ELK to ensure we have easy access to our most recent logs. This proves invaluable for fault detection and diagnosis and is used primarily by engineers. Another use case in a customer-centric search index. Each of our customers is described by a set of data points that come from various sources and are indexed in Elasticsearch. The index is later used by marketing, customer service, and other departments to get quick insights on our customer base.

Elasticsearch its a critical piece of our platform. We rely on it not only for searching of our documents (that is 80% of our business goal and most used feature) but for tracking logs and analytics with Kibana. Elasticsearch allows us to build this amazing search component that gets the user the refinement they want so they can find easily and quickly the results they are looking for. Monitoring our logs is almost that important; we track incidents and code quality among others.