Elasticsearch聽vs聽Rekognition API

Get Advice Icon

Need advice about which tool to choose?Ask the StackShare community!

Elasticsearch
Elasticsearch

8.9K
5.9K
+ 1
1.6K
Rekognition API
Rekognition API

2
7
+ 1
0
Add tool

Elasticsearch vs Rekognition API: What are the differences?

Developers describe Elasticsearch as "Open Source, Distributed, RESTful Search Engine". Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack). On the other hand, Rekognition API is detailed as "Integrated visual recognition API". ReKognition API offers services for detecting, recognizing, tagging and searching faces and concepts as well as categorizing scenes in any photo, through a RESTFUL API. We process and analyze photos from anywhere, so you can mix and match photo sources with user IDs, which can enable you to, say, recognize objects in Facebook and Flickr photos.

Elasticsearch and Rekognition API are primarily classified as "Search as a Service" and "Facial Recognition" tools respectively.

Elasticsearch is an open source tool with 42.4K GitHub stars and 14.2K GitHub forks. Here's a link to Elasticsearch's open source repository on GitHub.

- No public GitHub repository available -

What is Elasticsearch?

Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).

What is Rekognition API?

ReKognition API offers services for detecting, recognizing, tagging and searching faces and concepts as well as categorizing scenes in any photo, through a RESTFUL API. We process and analyze photos from anywhere, so you can mix and match photo sources with user IDs, which can enable you to, say, recognize objects in Facebook and Flickr photos.
Get Advice Icon

Need advice about which tool to choose?Ask the StackShare community!

Why do developers choose Elasticsearch?
Why do developers choose Rekognition API?
    Be the first to leave a pro

    Sign up to add, upvote and see more prosMake informed product decisions

      Be the first to leave a con
      Jobs that mention Elasticsearch and Rekognition API as a desired skillset
      What companies use Elasticsearch?
      What companies use Rekognition API?
        No companies found

        Sign up to get full access to all the companiesMake informed product decisions

        What tools integrate with Elasticsearch?
        What tools integrate with Rekognition API?
          No integrations found

          Sign up to get full access to all the tool integrationsMake informed product decisions

          What are some alternatives to Elasticsearch and Rekognition API?
          Solr
          Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.
          Lucene
          Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.
          MongoDB
          MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
          Algolia
          Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.
          Splunk
          Splunk Inc. provides the leading platform for Operational Intelligence. Customers use Splunk to search, monitor, analyze and visualize machine data.
          See all alternatives
          Decisions about Elasticsearch and Rekognition API
          Tim Specht
          Tim Specht
          鈥嶤o-Founder and CTO at Dubsmash | 16 upvotes 54.3K views
          atDubsmashDubsmash
          Memcached
          Memcached
          Algolia
          Algolia
          Elasticsearch
          Elasticsearch
          #SearchAsAService

          Although we were using Elasticsearch in the beginning to power our in-app search, we moved this part of our processing over to Algolia a couple of months ago; this has proven to be a fantastic choice, letting us build search-related features with more confidence and speed.

          Elasticsearch is only used for searching in internal tooling nowadays; hosting and running it reliably has been a task that took up too much time for us in the past and fine-tuning the results to reach a great user-experience was also never an easy task for us. With Algolia we can flexibly change ranking methods on the fly and can instead focus our time on fine-tuning the experience within our app.

          Memcached is used in front of most of the API endpoints to cache responses in order to speed up response times and reduce server-costs on our side.

          #SearchAsAService

          See more
          Julien DeFrance
          Julien DeFrance
          Full Stack Engineering Manager at ValiMail | 16 upvotes 281.1K views
          atSmartZipSmartZip
          Amazon DynamoDB
          Amazon DynamoDB
          Ruby
          Ruby
          Node.js
          Node.js
          AWS Lambda
          AWS Lambda
          New Relic
          New Relic
          Amazon Elasticsearch Service
          Amazon Elasticsearch Service
          Elasticsearch
          Elasticsearch
          Superset
          Superset
          Amazon Quicksight
          Amazon Quicksight
          Amazon Redshift
          Amazon Redshift
          Zapier
          Zapier
          Segment
          Segment
          Amazon CloudFront
          Amazon CloudFront
          Memcached
          Memcached
          Amazon ElastiCache
          Amazon ElastiCache
          Amazon RDS for Aurora
          Amazon RDS for Aurora
          MySQL
          MySQL
          Amazon RDS
          Amazon RDS
          Amazon S3
          Amazon S3
          Docker
          Docker
          Capistrano
          Capistrano
          AWS Elastic Beanstalk
          AWS Elastic Beanstalk
          Rails API
          Rails API
          Rails
          Rails
          Algolia
          Algolia

          Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

          I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

          For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

          Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

          Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

          Future improvements / technology decisions included:

          Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

          As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

          One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

          See more
          Interest over time
          Reviews of Elasticsearch and Rekognition API
          No reviews found
          How developers use Elasticsearch and Rekognition API
          Avatar of imgur
          imgur uses ElasticsearchElasticsearch

          Elasticsearch is the engine that powers search on the site. From a high level perspective, it鈥檚 a Lucene wrapper that exposes Lucene鈥檚 features via a RESTful API. It handles the distribution of data and simplifies scaling, among other things.

          Given that we are on AWS, we use an AWS cloud plugin for Elasticsearch that makes it easy to work in the cloud. It allows us to add nodes without much hassle. It will take care of figuring out if a new node has joined the cluster, and, if so, Elasticsearch will proceed to move data to that new node. It works the same way when a node goes down. It will remove that node based on the AWS cluster configuration.

          Avatar of Instacart
          Instacart uses ElasticsearchElasticsearch

          The very first version of the search was just a Postgres database query. It wasn鈥檛 terribly efficient, and then at some point, we moved over to ElasticSearch, and then since then, Andrew just did a lot of work with it, so ElasticSearch is amazing, but out of the box, it doesn鈥檛 come configured with all the nice things that are there, but you spend a lot of time figuring out how to put it all together to add stemming, auto suggestions, all kinds of different things, like even spelling adjustments and tomato/tomatoes, that would return different results, so Andrew did a ton of work to make it really, really nice and build a very simple Ruby gem called SearchKick.

          Avatar of AngeloR
          AngeloR uses ElasticsearchElasticsearch

          We use ElasticSearch for

          • Session Logs
          • Analytics
          • Leaderboards

          We originally self managed the ElasticSearch clusters, but due to our small ops team size we opt to move things to managed AWS services where possible.

          The managed servers, however, do not allow us to manage our own backups and a restore actually requires us to open a support ticket with them. We ended up setting up our own nightly backup since we do per day indexes for the logs/analytics.

          Avatar of Brandon Adams
          Brandon Adams uses ElasticsearchElasticsearch

          Elasticsearch has good tooling and supports a large api that makes it ideal for denormalizing data. It has a simple to use aggregations api that tends to encompass most of what I need a BI tool to do, especially in the early going (when paired with Kibana). It's also handy when you just want to search some text.

          Avatar of Ana Phi Sancho
          Ana Phi Sancho uses ElasticsearchElasticsearch

          Self taught : acquired knowledge or skill on one's own initiative. Open Source Search & Analytics. -time search and analytics engine. Search engine based on Lucene. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.

          How much does Elasticsearch cost?
          How much does Rekognition API cost?
          News about Rekognition API
          More news