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Amazon Elasticsearch Service

Score6.5 out of 10

16 Reviews and Ratings

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What is Amazon Elasticsearch Service?

Amazon Elasticsearch Service is a fully managed service that enables users to search, analyze, and visualize your log data at petabyte-scale. As a fully managed service, Amazon Elasticsearch Service manages the setup, deployment, configuration, patching, and monitoring of Elasticsearch clusters, so users can spend less time managing clusters and more time building applications. With a few clicks in the AWS console, users create scalable, secure, and available Elasticsearch clusters. Amazon Elasticsearch Service offers open source Elasticsearch APIs, managed Kibana, integration with Logstash and other AWS services, and SQL querying.

Categories & Use Cases

Reviews

What users are saying about Amazon Elasticsearch Service.
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Elasticsearch is fantastic, with some caveats

Incentivized
Verified User
Director in Information Technology (501-1000 employees employees)

Use Cases and Deployment Scope

We use AWS Elastic to compile metadata options for products that have a lot of options on them, things like rugs with multiple sizes, colors, patterns, shapes, and so on. This helps us to create one source of truth that we can then call through the API to get the data back we need through a lightweight webservice call.

Pros

  • Search
  • Query language
  • light-weight

Cons

  • onboarding
  • technical skills
  • difficulty in learning language

Return on Investment

  • Observability
  • Troubleshooting
  • Trending

Usability

Alternatives Considered

New Relic, Grafana and Splunk Cloud

Other Software Used

Grafana, Splunk Cloud, New Relic

Elasticsearch is a great searching solution

Incentivized
Todd Burkholder
Software Development Manager in Information Technology at Our Sunday Visitor, Inc (201-500 employees employees)

Pros

  • Slice data by dates and times
  • Provide weighting of search results
  • Easy configuration and setup

Cons

  • Understanding how stale the data is and what data needs refreshed

Most Important Features

  • Reducing time spent optimizing queries
  • Offloading resources around searching from our application

Return on Investment

  • Elasticsearch has allowed us to spend more time on building features and less time on optimizing search queries

Other Software Used

Amazon DynamoDB, AWS Lambda, Microsoft SQL Server

A fast & efficient way to store, search and analyze our structured & unstructured data

Sunny HemnaniView profile
Senior Software Engineer in Engineering at Coditas (501-1000 employees employees)

Use Cases and Deployment Scope

1. With Amazon Elasticsearch Service, we analyze logs of our microservices-based application to fix bugs. 2. With Amazon Elasticsearch Service, we accumulate data across the microservices of our product for visualization & other business purposes. 3. The benefit of elastic search is that, since data is in JSON format, all our microservices can easily change the data structure.

Pros

  • Fast Index based search.
  • Scalable.
  • Best for structured and unstructured data.

Cons

  • It is not so simple to learn.
  • Documentation is often very confusing.

Most Important Features

  • Centralized logging.
  • Handle tons of data.

Return on Investment

  • Poor documentation.
  • Hard to debug issues.

Amazon container is good for enterprise and pay less than local infrastructure

Incentivized
Verified User
Manager in Information Technology (501-1000 employees employees)

Pros

  • Stable, fast provisioning the VM, and also easy backup snapshot and restore.
  • Pay less than local server infrastructure, easy for collaboration work.

Cons

  • Really satisfied with this service from Amazon.

Most Important Features

  • Fast services and can scale very fast without affecting operation.

Return on Investment

  • The cost is a bit expensive if compared with other clouds but [the] performance is very good.

Amazon Elasticsearch offers excellent data ingestion and querying

Incentivized
Verified User
Manager in Engineering (11-50 employees employees)

Pros

  • Quick ingestion
  • Efficient data visualization thru Kibana

Cons

  • Managing production issues with shards is difficult

Most Important Features

  • Ability to ingest large volume of data

Return on Investment

  • Gave us the ability to produce some efficient reporting for our customers