Elasticsearch Reviews & Insights

Score8.7 out of 10

208 Reviews and Ratings

Community insights

TrustRadius Insights for Elasticsearch are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Pros

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.

Reviews

48 Reviews

Elasticsearch is your way to go!

Rating: 9 out of 10

Incentivized

June 18, 2024

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.

Pros

Log management
Search Engine
Autocomplete service
Storing Data
Caching layer in some cases
ML and data analysis

Cons

Elasticsearch is kind of hard to maintain as a cluster on k8s when self-managed.
Good to support AI that will help buidling complex queries
Documentation for Java library of Elasticsearch and Elasticsearch client is not that great compared to the APIs documentation

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

Verified User

Engineer in Information Technology (51-200 employees)

Vetted Review

7 years of experience

Great search, aggregation and visualization products.

Rating: 9 out of 10

March 31, 2024

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.

Pros

Full text search
aggregation
anomaly detection
dashboard
canvas

Cons

SIEM
Ingest API
The performance for a large cluster
business analysis

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.

Julie Zhong

Data analytics in Engineering at Netnordic (201-500 employees)

Vetted Review

6 years of experience

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Elasticsearch Overall Review

Rating: 9 out of 10

Incentivized

December 29, 2023

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).

Pros

Log and data capture, via Beats
Visualization of data
Application monitoring

Cons

Some of the cluster management functions could be more intuitive.
It would be nice if it could be used for large data sets (streaming data)
Troubleshooting could be easier.

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).

John Anderson

Lead Application Engineer IV in Information Technology at Cox Communications (10,001+ employees)

Vetted Review

3 years of experience

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Elasticsearch is a tricky, but great data platform

Rating: 8 out of 10

Incentivized

November 9, 2021

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:

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.

Pros

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.

Cons

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.

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 & 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.

Borislav Traykov

DevOps Team Leader in Engineering at Cognyte (1001-5000 employees)

Vetted Review

2 years of experience

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Elasticsearch Observability Enables an Outstanding Capacity To Transform IT Operations

Rating: 10 out of 10

Incentivized

August 17, 2021

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.

Pros

Observability features
Machine learning for anomaly detection
Index and search high volume of data

Cons

Basic alerting features

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.

Oscar Narváez Del Rio

Tools & Analytic Monitoring Leader in Information Technology at Entel (5001-10,000 employees)

Vetted Review

4 years of experience

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Search begets Search - Navigating your data progressively

Rating: 10 out of 10

Incentivized

June 10, 2021

Use Cases and Deployment Scope

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.

Pros

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

Cons

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

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.

Keith Lubell

Chief Technology Officer in Information Technology at Berkery, Noyes & Co., LLC (51-200 employees)

Vetted Review

5 years of experience

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Elasticsearch OSS Review

Rating: 9 out of 10

Incentivized

May 17, 2021

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.

Pros

Database
Scalability
Deployment

Cons

Backup
Rest API browser
Remote management using utilities

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.

Andrew Meyer

Sr Linux Engineer in Information Technology at Panera Bread (501-1000 employees)

Vetted Review

2 years of experience

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Elasticsearch: for searches, you know!

Rating: 8 out of 10

Incentivized

January 13, 2021

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.

Pros

Text-based searches on data
Daily, weekly, monthly analytics on data
Super easy scripting with painless scripting language

Cons

Relational data query
Sync data from SQL on table change (with hash maybe)
Provide better tutorials for beginners

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.

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.

Swastik Nath

Data Scientist in Information Technology at Intelligent Retail Pvt. Ltd. (51-200 employees)

Vetted Review

1 year of experience

Elasticsearch: Open-source, Fast, Excellent!

Rating: 10 out of 10

Incentivized

March 6, 2020

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.

Pros

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.

Cons

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.

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.

Verified User

Analyst in Information Technology (201-500 employees)

Vetted Review

2 years of experience

Elasticsearch helps you find the information you need!

Rating: 7 out of 10

Incentivized

February 14, 2020

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.

Pros

Complete package.
Open-source.

Cons

Complex query mechanism.
Complex architecture to set up and optimize.

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.

Verified User

Consultant in Information Technology (201-500 employees)

Vetted Review

2 years of experience

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Elasticsearch is a tricky, but great data platform

Rating: 8 out of 10

Incentivized

November 9, 2021

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:

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.

Pros

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.

Cons

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.

Likelihood to Recommend

Borislav Traykov

DevOps Team Leader in Engineering at Cognyte (1001-5000 employees)

Vetted Review

2 years of experience

View profile