Elasticsearch

We use Elasticsearch (Elastic for short, but that includes Kibana & LogStash so the full ELK kit) for 3 major purposes:

• product data persistence - as JSON objects.
• as log storage - different components produce log files in different formats + logs from other systems like the OSes and even some networking appliances.
• as test automation results storage & reporting platform - this is an implementation we glimpsed from an old Trivago blog post.

Different forms of Elastic are being used across the company - the vanilla one, OpenDistro and OpenSearch. Licensing limbo + long-term support make people here jump from one implementation to another.

Elasticsearch enables an operational capacity to quickly adopt this technology and boost observability on the different platform's components (infrastructure, integration, application, and services). Elasticsearch distributed architecture to index and search data make it a robust platform to scale on the go and support operational needs.

We use Elasticsearch to Index and make available for Search and Navigation our proprietary data on the M&A landscape. It drives dashboards and alerts to allow users to monitor trends and the latest events that occur in our dataset. It aligns our research group with our bankers. We marry it to Couchbase and MS SQL-Server.

We are using this in conjunction with other applications such as Atlassian stack. So this is being used throughout the whole organization but is an extension to another application. This allows us to search for words/topics very quickly in projects and commits. We currently use it in a single server instance.

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

In my organization, Elasticsearch is used as a fast and simple solution for providing search capability to text-based data and to easily perform analytics for our dashboard. Being a JSON-based response system, our APIs become simple and support multiple behaviors by translating to Elasticsearch queries. Not only does Elasticsearch act as our analytics platform, but also it serves as secondary database storage.

Elasticsearch is currently our log aggregator and SIEM. It is collecting Windows Event Logs, Syslog, DNS logs and HIDS logs. We use it in the IT department, but its reach is far and wide and collects data from every domain machine we have. The problems it solves are numerous! We have dashboards set up for authentication activity, firewall event and VPN activity. With a single glance, it's easy to understand the data and move on to other tasks. In the event of an incident, the detail that is able to be gleaned is incredible. The SIEM app has a working Timeline feature that allows you to simply drag and drop events when investigating an issue. Host intrusion is done by a third-party app but is able to ship the data right to Elasticsearch for easy processing, storage, and display.

Our organisation is currently using Elasticsearch for the Elasticstack functionality. Elasticstack gives us functionality to collect, aggregate, search and alert on logging. Kibana, which runs within the Elasticstack, gives us the functionality to create neat dashboards which we use within every layer of our organisation. This addresses the need for various levels of insight across the organisation.

We're using Elasticsearch for indexing most of our data, allowing for blazing-fast searches. We store massive time-series data volumes from thousands of IoT sensors that Elasticsearch handles brilliantly, making metrics available in realtime. We're also running dashboards and canvas in Kibana, fed from Elasticsearch, which gets updated in realtime.

The way we set it up usually for our customers, Elasticsearch improves developer velocity by allowing to quickly search through millions of log messages. It is usually used by the development and operations team.

Elasticsearch is being used to store and search architecture standards, guidance, and other documents pertaining to software architectures. When used with the Spring Java Framework, it is extremely easy to set up custom queries.

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 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.

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 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 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.

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.

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.

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 (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 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 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.