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
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- Tech Details
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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 Enterprises (1,001+ 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!
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- 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
(1-4 of 4)Companies can't remove reviews or game the system. Here's why
November 09, 2021
Elasticsearch is a tricky, but great data platform
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.
- Data persistence & retriveval
- Data indexing
- Metrics & reporting over data thanks to its query language & Kibana visualization
- Flexibility of data sources - a lot of existing "beats" + ability to push custom data easily
- Very scalable - although a minimum of 3 nodes is advised, even a 1-node installation can work great for some use cases.
- Licensing - this is big issue with a lot of companies that try to embed Elasticsearch as a part of their products and not have to expose that explicitly or deal with licensing complications.
- Security - this is not a feature enabled by default so installations can go and be unsecure & thus exploited without anyone noticing.
- Having security turned off can be beneficial for some performance optimizations though.
- Cluster restructuring/upgrading - if you need to do a rolling cluster upgrade, node roles and data replication is handled in a complicated & tricky way so you need to have knowledge & experience to survive such an operation with your data & cluster to be operational after it.
- Data persistence, indexing and querying at high speed
- Scalability
- Building reporting over data thanks to Kibana
- Greatly reduced data-in-transit and at-rest overheads
- Provided us with a truly scalable solution for our data
- Kibana offers a reporting platform based on our custom queries. Extremely useful for reports from automated test executions.
- Apache Solr and MongoDB
Elasticsearch is the most well-known and supported free data platform that we identified. We are taking advantage of community knowledge and practices.
In terms of flexibility and breadth of use cases no other competitor came close to Elasticsearch.
We've tried Solr in the past be we encountered issues which were deal-breaking for us.
MongoDB - it just did not pass our evaluation parameters as a main data platform. We still use it for smaller purposes, though.
In terms of flexibility and breadth of use cases no other competitor came close to Elasticsearch.
We've tried Solr in the past be we encountered issues which were deal-breaking for us.
MongoDB - it just did not pass our evaluation parameters as a main data platform. We still use it for smaller purposes, though.
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
- Observability platform
- Anomaly detection
- Search performance
- Data driven enabler
- Capacity to scale
- Unify IT operations
Elasticsearch brings the capacity to grow data ingest and provides 24/7 visibility into critical services across IT and Business teams.
With Elasticsarch, specialized support teams can easily view all the relevant information by using real-time dashboards, and can immediately start the initial analysis to isolate and mitigate issues.
With Elasticsarch, specialized support teams can easily view all the relevant information by using real-time dashboards, and can immediately start the initial analysis to isolate and mitigate issues.
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.
- Indexing text data
- Aggregations allow users to progressively add search criteria to refine their searches
- Find trends in our data with Aggregations
- Integrate text Search our taxonomy Search
- Joining data requires duplicate de-normalized documents that make parent child relationships. It is hard and requires a lot of synchronizations
- Tracking errors in the data in the logs can be hard, and sometimes recurring errors blow up the error logs
- Schema changes require complete reindexing of an index
- Text Search (Natural Language Processing)
- Integration with Couchbase
- Integration with multiple platforms including dot net and nodejs
- Aggregations and search done together
- Most of our investment is in programming hours which is expensive
- Easy to set up nodes
- Free version has a lot of the great basic features
Elasticsearch and Solr are both based on Lucene, but the user community for Elasticsearch is much stronger, and setting up a cluster is easier. Splunk is very well suited for Log indexing and searching but is not nearly as flexible as Elasticsearch. Couchbase is a great NoSQL database and is super fast as key value store, but it's indexing abilities are much weaker than Elasticsearch and can not do aggregates and listings in a single query
May 17, 2021
Elasticsearch OSS Review
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.
- Database
- Scalability
- Deployment
- Backup
- Rest API browser
- Remote management using utilities
- Scalability
- Optimization
- Rapid deployment
- Finding talent.
- Using open source versus paid.
- Management if it gets too big.