Great search, aggregation and visualization products.
We use ECE platform and Elasticsearch for the delivery data to track delivery. And also use kibana for visualization of business analysis …
Starting at $16 per month
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What is Elasticsearch?
Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
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Pricing
Standard
$16.00
Cloud
per month
Gold
$19.00
Cloud
per month
Platinum
$22.00
Cloud
per month
Entry-level set up fee?
- No setup fee
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Product Demos
How to create data views and gain insights on Elastic
YouTube
Setting Up a Search Box to Your Website or Application with Elasticsearch
YouTube
ChatGPT and Elasticsearch: OpenAI meets private data setup walkthrough
YouTube
Product Details
- About
- Tech Details
- FAQs
What is Elasticsearch?
Elasticsearch is a distributed, RESTful search and analytics engine capable of addressing a growing number of use cases. As the heart of the Elastic Stack, it centrally stores data for fast search, fine‑tuned relevancy, and analytics that scale.
Elasticsearch now features generative AI search capabilities. Elasticsearch Relevance Engineâ„¢ (ESRE) powers generative AI solutions for private data sets with a vector database and machine learning models for semantic search that bring increased relevance to more search application developers.
Elasticsearch now features generative AI search capabilities. Elasticsearch Relevance Engineâ„¢ (ESRE) powers generative AI solutions for private data sets with a vector database and machine learning models for semantic search that bring increased relevance to more search application developers.
ESRE combines AI with Elastic’s text search to give developers a full suite of sophisticated retrieval algorithms and the ability to integrate with large language models (LLMs). It is accessed through a single, unified API.
The Elasticsearch Relevance Engine’s configurable capabilities can be used to help improve relevance by:
- Applying advanced relevance ranking features including BM25f, a critical component of hybrid search
- Creating, storing, and searching dense embeddings using Elastic’s vector database
- Processing text using a wide range of natural language processing (NLP) tasks and models
- Letting developers manage and use their own transformer models in Elastic for business specific context
- Integrating with third-party transformer models such as OpenAI’s GPT-3 and 4 via API to retrieve intuitive summarization of content based on the customer’s data stores consolidated within Elasticsearch deployments
- Enabling ML-powered search without training or maintaining a model using Elastic’s out-of-the-box Learned Sparse Encoder model to deliver highly relevant, semantic search across a variety of domains
- Combining sparse and dense retrieval using Reciprocal Rank Fusion (RRF), a hybrid ranking method that gives developers control to optimize their AI search engine to their unique mix of natural language and keyword query types
- Integrating with third-party tooling such as LangChain to help build sophisticated data pipelines and generative AI applications
Elasticsearch Video
What is Elasticsearch?
Elasticsearch Technical Details
| Deployment Types | Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
Frequently Asked Questions
Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
Reviewers rate Support Rating highest, with a score of 7.8.
The most common users of Elasticsearch are from Mid-sized Companies (51-1,000 employees).
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Community Insights
TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
- Business Problems Solved
- Pros
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Elasticsearch has become an essential tool for users across various industries and domains. Its distributed architecture enables efficient searching of large datasets, even with partial text matches and across multiple fields. This capability makes it invaluable for tasks such as logging and analysis in cloud environments, where managing hundreds or thousands of servers is a necessity. Elasticsearch's fast and powerful search capabilities find application in B2B and B2C eCommerce websites, allowing users to search by various criteria like title, artist, genre, price range, and availability date. It serves as a reliable solution for tracking logs, incidents, analytics, and code quality. Additionally, Elasticsearch's ability to index and search large sets of data facilitates the creation of reporting dashboards. The product's built-in data replication features ensure data availability and easy retrieval while its scalability supports operational needs. It also enables tokenized free text search in audio transcripts as well as indexing and analyzing HTTP Request Response messages to detect security threats. With its wide range of use cases spanning from web search engines to scientific journals and complex data indexing, Elasticsearch proves to be an indispensable tool for organizations seeking efficient data storage solutions.
Highly Scalable Solution: Elasticsearch has been consistently praised by users for its highly scalable nature. It is able to handle storing and retrieving large numbers of documents, offering redundancy and distributed storage across multiple hosts with minimal configuration required.
Extensive Search Capabilities: Users highly praise Elasticsearch for its extensive search capabilities, especially in terms of full-text search. They find it easy to search and filter through millions of documents efficiently, even on large datasets, thanks to its fast search speeds.
Valuable Aggregations and Facets: Elasticsearch's support for aggregations and facets is highlighted as a valuable feature by users. They appreciate the ability to progressively add search criteria to refine their searches and uncover trends in their data.
Configuration Process: Users have encountered difficulties when implementing custom functions and have found the configuration process to be lacking. Some reviewers have mentioned challenges in integrating different elements of the program, incomplete documentation, and misleading forums.
Query Editor Limitations: Users have experienced issues with the query editor and noted that certain queries are not supported in the IntelliSense feature. Several users expressed frustration with inadequate documentation, hard-to-debug problems, and the complexities involved in tuning for ingress performance.
Learning Curve: Users have found the learning curve to be challenging, particularly for those with a background in SQL. Many reviewers mentioned a steep learning curve, extensive documentation requirements, and complexities related to mapping and data type conversion.
Attribute Ratings
Reviews
(1-7 of 7)Companies can't remove reviews or game the system. Here's why
March 31, 2024
Great search, aggregation and visualization products.
Score 9 out of 10
Vetted Review
Verified User
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.
- Full text search
- aggregation
- anomaly detection
- dashboard
- canvas
- SIEM
- Ingest API
- The performance for a large cluster
- business analysis
August 18, 2021
Elasticsearch Observability Enables an Outstanding Capacity To Transform IT Operations
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.
- Observability features
- Machine learning for anomaly detection
- Index and search high volume of data
- Basic alerting features
March 06, 2020
Elasticsearch: Open-source, Fast, Excellent!
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.
- Log storage efficiency - We have millions of events a day and are able to keep 90 days worth for under 1TB of on disk space.
- Dashboards - Technically through Kibana(but I consider the entire stack as part of Elasticsearch.) Dashboards are easy to manipulate and create from scratch. Many shippers have premade dashboards ready for day one, too.
- Speed - Have you ever searched an indexed database of 200 million events and found an answer in a matter of seconds? You could with Elasticsearch.
- Free/self-hosted can be a nightmarish amount of work. When you break it, it's easy to lose data.
- Documentation is thorough at times, but there still seems to be holes in some components. For instance, PacketBeat doesn't explicitly tell you best practices for DNS logging, and I had to use a different resource to get an answer.
- Pricing - The free tier is excellent, but it's a significant jump up to get the machine learning modules, endpoint security and more.
October 09, 2019
Very useful for eCommerce
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.
- Search results are provided very quickly.
- The search results are accurate.
- Search results contain details on the accuracy of each hit.
- There is a steep learning curve for this product so what is most useful for developers is good documentation including examples and sample applications.
Score 7 out of 10
Vetted Review
Verified User
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.
- Free of SQL: ES does not have the overhead of relying on SQL. In fact, you can use most (if not all) DBMs out there.
- Java: Normally, this is not a strength: Java is slow and cumbersome. I believe in this case, it's truly a feature: by utilizing a language with universal support, it makes ES VERY DevOps friendly, simply by being able to focus on Problem-oriented vs Solutions-based thinking.
- Although ES has been known to consume RAM, it's very flexible, and I have implemented on a number of distinct hardware configuration with success.
- Linux: It's not locked down to an OS (which is the way of the future), and as a result-running it on Linux means you get the power of Linux, in a data science package.
- Elastic Search IS a resource hog: most of the time, I will run ES on a dedicated VM (often a dedicated blade, too!) and allow the other components of the stack to run on separate blades/VMs.
- Works great for small projects, but is NOT industrial strength: When you are performing a data architecture project, where you are capturing and mining datasets, ES is fine, until you start getting into much denser data sources (orders to TBs), such that ES will violate Data integrity.
- It only supports JSON output: Which is very friendly to a lot of DevOps/Data Architecture projects but may become a hassle when your endpoints require CVS, XML, etc.
April 13, 2018
Find more faster with Elasticsearch
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.
- The best solution we've found for blazing fast searches, especially text-based.
- Easy to add nodes for data redundancy.
- Good documentation makes getting up and running easy.
- I found the learning curve fairly difficult having a SQL background.
March 01, 2018
Enthusiastic for Elasticsearch
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.
- Searching, it does it well and searches are fast...real fast.
- Ease of use, we were able to get an Elasticsearch cluster up and running in a half hour and doing basic searches after that was very easy with simple requests
- Redundancy built in and stability. We haven't had any of our Elastic clusters go down intentionally, but testing out redundancy by removing nodes Elasticsearch has gone flawlessly.
- Only breaking changes between versions when they are absolutely necessary.
- Works well with .Net libraries that are supported and coded by Elastic.
- A bit more of a learning curve for complex searches, indexing more complex things.
- Some of our updates between versions haven't gone as smoothly as we would like, but in more recent versions Elastic has done a much better job at trying to allow for full uptime upgrades.
- Configuration needs to be set up to do larger searches, or more complex searches and at times while starting it wasn't obvious what configuration needed to be changed.