CARTO vs Elasticsearch

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CARTO

39
75
+ 1
3
Elasticsearch

31.1K
23.8K
+ 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.

Advice on CARTO and Elasticsearch
André Ribeiro
at Federal University of Rio de Janeiro · | 4 upvotes · 33K views

Hi, community, I'm planning to build a web service that will perform a text search in a data set off less than 3k well-structured JSON objects containing config data. I'm expecting no more than 20 MB of data. The general traits I need for this search are: - Typo tolerant (fuzzy query), so it has to match the entries even though the query does not match 100% with a word on that JSON - Allow a strict match mode - Perform the search through all the JSON values (it can reach 6 nesting levels) - Ignore all Keys of the JSON; I'm interested only in the values.

The only thing I'm researching at the moment is Elasticsearch, and since the rest of the stack is on AWS the Amazon ElasticSearch is my favorite candidate so far. Although, the only knowledge I have on it was fetched from some articles and Q&A that I read here and there. Is ElasticSearch a good path for this project? I'm also considering Amazon DynamoDB (which I also don't know of), but it does not look to cover the requirements of fuzzy-search and ignore the JSON properties. Thank you in advance for your precious advice!

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Replies (3)
Roel van den Brand
Lead Developer at Di-Vision Consultion · | 3 upvotes · 28.1K views
Recommends
Amazon AthenaAmazon Athena

Maybe you can do it with storing on S3, and query via Amazon Athena en AWS Glue. Don't know about the performance though. Fuzzy search could otherwise be done with storing a soundex value of the fields you want to search on in a MongoDB. In DynamoDB you would need indexes on every searchable field if you want it to be efficient.

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Ted Elliott

I think elasticsearch should be a great fit for that use case. Using the AWS version will make your life easier. With such a small dataset you may also be able to use an in process library for searching and possibly remove the overhead of using a database. I don’t if it fits the bill, but you may also want to look into lucene.

I can tell you that Dynamo DB is definitely not a good fit for your use case. There is no fuzzy matching feature and you would need to have an index for each field you want to search or convert your data into a more searchable format for storing in Dynamo, which is something a full text search tool like elasticsearch is going to do for you.

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Julien DeFrance
Principal Software Engineer at Tophatter · | 3 upvotes · 26.9K views

The Amazon Elastic Search service will certainly help you do most of the heavy lifting and you won't have to maintain any of the underlying infrastructure. However, elastic search isn't trivial in nature. Typically, this will mean several days worth of work.

Over time and projects, I've over the years leveraged another solution called Algolia Search. Algolia is a fully managed, search as a service solution, which also has SDKs available for most common languages, will answer your fuzzy search requirements, and also cut down implementation and maintenance costs significantly. You should be able to get a solution up and running within a couple of minutes to an hour.

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Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 276.1K views
Needs advice
on
AlgoliaAlgoliaElasticsearchElasticsearch
and
FirebaseFirebase

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

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Replies (2)
Josh Dzielak
Co-Founder & CTO at Orbit · | 8 upvotes · 201.3K views
Recommends
AlgoliaAlgolia

Hi Rana, good question! From my Firebase experience, 3 million records is not too big at all, as long as the cost is within reason for you. With Firebase you will be able to access the data from anywhere, including an android app, and implement fine-grained security with JSON rules. The real-time-ness works perfectly. As a fully managed database, Firebase really takes care of everything. The only thing to watch out for is if you need complex query patterns - Firestore (also in the Firebase family) can be a better fit there.

To answer question 2: the right answer will depend on what's most important to you. Algolia is like Firebase is that it is fully-managed, very easy to set up, and has great SDKs for Android. Algolia is really a full-stack search solution in this case, and it is easy to connect with your Firebase data. Bear in mind that Algolia does cost money, so you'll want to make sure the cost is okay for you, but you will save a lot of engineering time and never have to worry about scale. The search-as-you-type performance with Algolia is flawless, as that is a primary aspect of its design. Elasticsearch can store tons of data and has all the flexibility, is hosted for cheap by many cloud services, and has many users. If you haven't done a lot with search before, the learning curve is higher than Algolia for getting the results ranked properly, and there is another learning curve if you want to do the DevOps part yourself. Both are very good platforms for search, Algolia shines when buliding your app is the most important and you don't want to spend many engineering hours, Elasticsearch shines when you have a lot of data and don't mind learning how to run and optimize it.

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Mike Endale
Recommends
Cloud FirestoreCloud Firestore

Rana - we use Cloud Firestore at our startup. It handles many million records without any issues. It provides you the same set of features that the Firebase Realtime Database provides on top of the indexing and security trims. The only thing to watch out for is to make sure your Cloud Functions have proper exception handling and there are no infinite loop in the code. This will be too costly if not caught quickly.

For search; Algolia is a great option, but cost is a real consideration. Indexing large number of records can be cost prohibitive for most projects. Elasticsearch is a solid alternative, but requires a little additional work to configure and maintain if you want to self-host.

Hope this helps.

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Pros of CARTO
Pros of Elasticsearch
  • 1
    Crisp UI
  • 1
    Great customer service
  • 1
    Comprehensive platform
  • 323
    Powerful api
  • 314
    Great search engine
  • 230
    Open source
  • 214
    Restful
  • 199
    Near real-time search
  • 96
    Free
  • 83
    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
  • 5
    More than a search engine
  • 3
    Easy to scale
  • 3
    Awesome, great tool
  • 3
    Great docs
  • 2
    Potato
  • 2
    Document Store
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Reliable
  • 2
    Nosql DB
  • 2
    Fast
  • 2
    Easy setup
  • 2
    Highly Available
  • 2
    Great piece of software
  • 1
    Ecosystem
  • 1
    Scalability
  • 1
    Not stable
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Easy to get hot data
  • 1
    Open
  • 0
    Community

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Cons of CARTO
Cons of Elasticsearch
    Be the first to leave a con
    • 7
      Resource hungry
    • 6
      Diffecult to get started
    • 5
      Expensive
    • 4
      Hard to keep stable at large scale

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    - No public GitHub repository available -

    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).

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

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    What tools integrate with CARTO?
    What tools integrate with Elasticsearch?
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      Blog Posts

      May 21 2019 at 12:20AM

      Elastic

      ElasticsearchKibanaLogstash+4
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      GitHubPythonReact+42
      48
      40061
      GitHubPythonNode.js+47
      53
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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.
      ArcGIS
      It is a geographic information system for working with maps and geographic information. It is used for creating and using maps, compiling geographic data, analyzing mapped information, sharing and much more.
      Tableau
      Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
      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.
      See all alternatives