Elasticsearch Reviews and Ratings

Use Cases and Deployment Scope

Elasticsearch is an important service that we use frequently in the organization. We use Elasticsearch as a logging service for our system logs, Once we have the logs in Elasticsearch, we connect to Kibana and start building dashboards and charts that help us track our system stability and availability in terms of System metrics. On the other hand, we use it to track new bugs and errors. The other usage for Elasticsearch in our system is as a search engine. Elasticsearch is a very fast and amazing search engine, where we store some fields and call Elasticsearch APIs to fetch these fields when needed.

Likelihood to Recommend

Well suited as : 1. Search Engine with Autocomplete and fuzzy match 2. Data analysis 3. Log management Not Good for A. Data Storage

Use Cases and Deployment Scope

We use ECE platform and Elasticsearch for the delivery data to track delivery. And also use kibana for visualization of business analysis and KPI. We also ingest the log from different API and investigate when there is a trouble. We also use transform and machine learning feature to detect anomalies.

Likelihood to Recommend

It is good for delivery tracking. Customer can search for the shipment ID to get the detail of the shipment. The business analysis with excel data is not as good as PowerBI.

Use Cases and Deployment Scope

We use Elasticsearch to analyze and visualize logs from various Engineering workflows. We have clusters defined for providing Application Performance Monitoring for a variety of Engineering applications, utilizing Beats and other processes to populate the data required for monitoring and analysis. We also capture metrics (for both servers and applications).

Likelihood to Recommend

As stated before, it does a good job of providing analysis and visualization on data coming into the system, but troubleshooting could be better (when issues arise). Performance, scalability, and overall speed are good, but the trade-off is it can be resource-intensive. Overall a good tool, it just takes a bit to learn (it's not always as "intuitive" as it should be).

Use Cases and Deployment Scope

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

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

Likelihood to Recommend

Elasticsearch is a really scalable solution that can fit a lot of needs, but the bigger and/or those needs become, the more understanding &amp; infrastructure you will need for your instance to be running correctly.

Elasticsearch is not problem-free - you can get yourself in a lot of trouble if you are not following good practices and/or if are not managing the cluster correctly.

Licensing is a big decision point here as Elasticsearch is a middleware component - be sure to read the licensing agreement of the version you want to try before you commit to it.

Same goes for long-term support - be sure to keep yourself in the know for this aspect you may end up stuck with an unpatched version for years.

Use Cases and Deployment Scope

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.

Likelihood to Recommend

Elasticseach platform allows implementing a robust operational stuck for unified observability handling a huge volume of data with high performance and capacity to scale fast. Logstash, Beats, and APM products provide a structured framework to collect events and data being easy to deploy and configure.

Use Cases and Deployment Scope

We use Elasticsearch to Index and make available for Search and Navigation our proprietary data on the M&amp;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.

Likelihood to Recommend

Elasticsearch is really well suited for searching text (Natural Language Processing) and you can fine tune the searches and scoring very well. I like the ability to find Significant Terms in the Index, where you can find aggregations that are really relevant to a specific search. It also allows for queries to lead to new queries via aggregations which is great for navigating your data. It is less suited to doing more complex aggregations where slices of data are required to be processing using guassian normalizations. And doing searches which join different documents is very very hard, and requires serious thought on how to denormalize data.

Use Cases and Deployment Scope

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.

Likelihood to Recommend

Elasticsearch is used very well in the log management space. In conjunction with Logstash, Kibana, and Graylog Elasticsearch makes leveraging these products wonderful. The ease of deploying it. Securing it very quickly. Fast and scalable searching options. It can also be a distributed data warehouse for immutable documents. However, it is not a fully functional database system.

Use Cases and Deployment Scope

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.

Likelihood to Recommend

Elasticsearch is best suited for search, analytics, aggregation, and consumption from single tabular structured data. It works best if you sync your data at regular intervals either with Logstash or any other custom sync process. <div>

</div><div>However, Elasticsearch still does not support relational queries out of the box. You could denormalize your data before every sync, but that has the potential for complicating the sync process very fast.</div>

Use Cases and Deployment Scope

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.

Likelihood to Recommend

Easiest recommendation of my career. The capability and speed are out of this world, and pricing compared to enterprise logging solutions is a fraction of the cost. That'd come with a caveat, that you must be ready to devote some time to it to learn it and get it working. It's not turnkey, but it's one of the best all-around.

Use Cases and Deployment Scope

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.

Likelihood to Recommend

Elasticsearch is very well suited within an IT architecture where a lot of open-source software is already being used and where the developers strongly appreciate open-source software. Elasticsearch might be less appropriate in an organisation where there is less space to master the tool. The tool is quite difficult to learn once you start working on the CLI-level search queries.