CARTO聽vs聽Elasticsearch

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CARTO
CARTO

22
27
+ 1
3
Elasticsearch
Elasticsearch

8.9K
5.9K
+ 1
1.6K
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CARTO vs Elasticsearch: What are the differences?

What is CARTO? The CARTO platform empowers business analysts, data scientists and more, to turn location data into business outcomes. The CARTO platform empowers everyone, from business analysts to data scientists, to turn location data into business outcomes. We accelerate innovation, power new use cases and disrupt business models through Location Intelligence.

What is Elasticsearch? 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).

CARTO and Elasticsearch are primarily classified as "Mapping APIs" and "Search as a Service" tools respectively.

Some of the features offered by CARTO are:

  • Drag and drop data import allows you to create visualizations in seconds
  • Make sense of your location data and power your business.
  • Create beautiful visualizations with our easy to use design and styling tools.

On the other hand, Elasticsearch provides the following key features:

  • Distributed and Highly Available Search Engine.
  • Multi Tenant with Multi Types.
  • Various set of APIs including RESTful

CARTO and Elasticsearch are both open source tools. Elasticsearch with 41.9K GitHub stars and 14K forks on GitHub appears to be more popular than CARTO with 2.18K GitHub stars and 601 GitHub forks.

What is CARTO?

The CARTO platform empowers everyone, from business analysts to data scientists, to turn location data into business outcomes. We accelerate innovation, power new use cases and disrupt business models through Location Intelligence.

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).
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    Jobs that mention CARTO and Elasticsearch as a desired skillset
    PinterestPinterest
    San Francisco, CA; Palo Alto, CA
    PinterestPinterest
    San Francisco, CA; Palo Alto, CA
    PinterestPinterest
    San Francisco, CA; Palo Alto, CA
    PinterestPinterest
    San Francisco, CA; Palo Alto, CA
    What companies use CARTO?
    What companies use Elasticsearch?

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    What tools integrate with CARTO?
    What tools integrate with Elasticsearch?
      No integrations found

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      What are some alternatives to CARTO and Elasticsearch?
      Mapbox
      We make it possible to pin travel spots on Pinterest, find restaurants on Foursquare, and visualize data on GitHub.
      Google Maps
      Create rich applications and stunning visualisations of your data, leveraging the comprehensiveness, accuracy, and usability of Google Maps and a modern web platform that scales as you grow.
      Leaflet
      Leaflet is an open source JavaScript library for mobile-friendly interactive maps. It is developed by Vladimir Agafonkin of MapBox with a team of dedicated contributors. Weighing just about 30 KB of gzipped JS code, it has all the features most developers ever need for online maps.
      OpenStreetMap
      OpenStreetMap is built by a community of mappers that contribute and maintain data about roads, trails, caf茅s, railway stations, and much more, all over the world.
      OpenLayers
      An opensource javascript library to load, display and render maps from multiple sources on web pages.
      See all alternatives
      Decisions about CARTO and Elasticsearch
      Tim Specht
      Tim Specht
      鈥嶤o-Founder and CTO at Dubsmash | 16 upvotes 55K 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 285.7K 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.

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      Interest over time
      Reviews of CARTO and Elasticsearch
      Review ofCARTOCARTO

      Carto (formerly CartoDB) had strong open-source roots, and an early and active community of open-source contributors, which it nicely balanced with its business model. But about two years ago the company rebranded and essentially abandoned its open-source origins, disallowing API access, preventing map exports in non-proprietary formats, and not providing backward compatibility.

      If you're a developer, stay away from CARTO.

      How developers use CARTO and Elasticsearch
      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 CARTO cost?
      How much does Elasticsearch cost?