Elasticsearch Reviews

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Score 8.9 out of 100

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Reviews (1-25 of 36)

Maria Sousa profile photo
Score 9 out of 10
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Elasticsearch is very well packed in a broad set of features, ranging from customization capabilities to security and add-ons, and also comes with a great visualization tool named Kibana. Most of the competitors are strong in some of these areas, but I know of no other that's so well balanced as Elasticsearch is.
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Erlon Sousa Pinheiro profile photo
Score 8 out of 10
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From my perspective, there is nothing currently on the marker better than Datadog, but unfortunately, that's a pricey product, Elasticsearch deliver us part of Datadog functionalities being cheaper. Fluentd as a service (provided by the company behind Fluentd) looks like a medium service. I didn't find anything better than Elasticsearch, so, from my perspective, Elasticsearch is a product between Fluentd and Datadog.
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Score 9 out of 10
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Almost no one uses Solr anymore--most have migrated to Elasticsearch. I've never tried it myself but I heard Solr is much more difficult to configure and because it doesn't use a REST API, it locks you into Java and XML. XML--ick!
Lucene: Elasticsearch is built using Lucene instances for each index (the ES code essentially just glues together tons of Lucene instances), so it's not a fair comparison. But I suppose if you wanted the flexible data-model and you don't need the system to be distributed and highly available and parallel, Lucene would be a good choice.
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Jose Adan Ortiz profile photo
Score 10 out of 10
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With Elasticsearch you can integrate a lot of data sources. It can act as a small DataLake where you can put different kinds of data and extract important insights. With Splunk, additional to elevated costs of licensing and hardware, you need to have expert engineers to address business and platform requirements. If you have Elasticsearch, it can be easily deployed and scaled.
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Score 9 out of 10
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Elasticsearch is open source and therefore cost effective.
Search query language in Elasticsearch is easy to use and helps everyone to get hands-on basic training.
Elasticsearch integrates well with Kibana and Logstash which extends its capabilities of visualization and analytics.
Its extendible with multiple applications through REST api services.
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Score 9 out of 10
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All database systems have things they are good at, and things they aren't as good at. Riak/SOLR is great as a K/V store, but SOLR cannot handle requests as fast as ElasticSearch. In fact, SOLR is the reason we had to migrate to ElasticSearch.
Redis is great at SET operations on large sets of data and quick in-memory operations. We actually use Redis for a small subset of tasks in our product that wasn't appropriate to perform on ElasticSearch. In this case, it was much faster and cheaper to use Redis.
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Score 7 out of 10
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ES does not compete with the above packages but compliments them. By automating and mining logs, you are able to get a sense of the business process, marketing data or whatever else you need to capture and mine. The potential energy stored within Elasticsearch makes it a great tool to include in your DevOps toolbox.
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Anatoly Geyfman profile photo
Score 10 out of 10
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When we first evaluated Elasticsearch, we compared it with alternatives like traditional RDBMS products (Postgres, MySQL) as well as other noSQL solutions like Cassandra & MongoDB. For our use case, Elasticsearch delivered on two fronts. First, we got a world-class search engine out of it, that we custom-built for our specific domain (healthcare). We've got, easily, the most expressive (easy to use & powerful) healthcare search engine out there. Second, along with the search, we also received an analytics engine that could do most analytics jobs as quickly as it retrieved search results. Overall, it would be very difficult for us to find a single solution for these two different problems.
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Score 10 out of 10
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As far as we are concerned, Elasticsearch is the gold standard and we have barely evaluated any alternatives. You could consider it an alternative to a relational or NoSQL database, so in cases where those suffice, you don't need Elasticsearch. But if you want powerful text-based search capabilities across large data sets, Elasticsearch is the way to go.
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January 10, 2019

The Best Available

Score 9 out of 10
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Elasticsearch is the most powerful and easy to use platform in this market. It's open source which makes enhancements very possible and also makes customization something that is commonplace. We're able to create custom modules to pull data from both log and config files, which is a very unique ability.
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David Greenwell profile photo
Score 10 out of 10
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The only other competitor we researched was mongo as some of our table information is stored in an XML file, but as we were doing searching we gravitated towards Elasticsearch. We knew mongo had some of the qualifications for what we wanted, but went with Elasticsearch for specifically our searches and actually used Mongo for more DB storage.
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Colby Shores profile photo
Score 10 out of 10
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Apache Solr is the closest competitor to ElasticSearch from a search engine perspective. ElasticSearch is simple and streamlined in it's configuration. When taken as a whole, Apache Solr is more robust as a storage engine from a developer perspective, ElasticSearch has the entire ElasticStack at it's disposal which sets it apart. Our organization looked into Splunk, however I wasn't with the organization at that time to give a solid perspective on it.
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Kris Bandurski profile photo
Score 10 out of 10
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  • Solr
I have used Solr only briefly, but Elasticsearch wins when it comes to the ease of setting up and getting access to data stored in search indices (Kibana). It also comes with comprehensive and easy to read documentation that familiarises the reader with the concepts behind a distributed search engine.
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Yasmany Cubela Medina profile photo
Score 10 out of 10
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  • sphinx
Even when sphinx base code is on c++ and they obtain a great performance from it, even when they have a set of plugins that allow to integrate with common database systems like MySQL, Elasticsearch is on top of license and all their experience on search. It also provides a long set of plugins and data extractors that can be used with common databases like MongoDB but the decisive point for our company was their modern concepts and ways to integrate and interact with modern software.
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About Elasticsearch

Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
Categories:  Enterprise Search

Elasticsearch Technical Details

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