Airtable vs Elasticsearch

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Airtable

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Elasticsearch

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Airtable vs Elasticsearch: What are the differences?

## Introduction

Key differences between Airtable and Elasticsearch are outlined below:

1. **Data Structure**: Airtable uses a structured format with tables, fields, and records for organizing data, while Elasticsearch employs a JSON-based document format for data storage, making it more flexible for unstructured data.

2. **Query Language**: Airtable's query language is relatively simple and primarily meant for basic filtering and sorting, whereas Elasticsearch's query DSL (Domain Specific Language) is powerful and supports complex search queries, aggregations, and filters.

3. **Indexing and Search**: Airtable focuses on ease of use and quick data entry, while Elasticsearch is designed for high-speed indexing and searching of large volumes of data, suitable for applications with heavy search requirements.

4. **Scalability and Performance**: Elasticsearch is built for distributed computing and can scale horizontally, offering better performance for large-scale data operations compared to Airtable, which may experience limitations with increasing data size and user load.

5. **Real-time Data Updates**: Elasticsearch provides real-time indexing and search capabilities, making it suitable for applications requiring instant updates and search results, whereas Airtable may have latency in updating and reflecting changes in the data.

6. **Data Analysis and Visualization**: Airtable offers built-in features for data analysis and visualization, while Elasticsearch is more focused on data retrieval and search functionalities rather than in-depth data analysis tools.


In Summary, Airtable and Elasticsearch differ in terms of data structure, query language, indexing and search capabilities, scalability, real-time data updates, and data analysis and visualization features.
Advice on Airtable and Elasticsearch
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 365.5K 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 · 270.8K views
Recommends
on
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
on
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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Needs advice
on
AirtableAirtable
and
SheetsuSheetsu

I'm trying to set up an ideally "no- code" way to have a backend of 3 different tables and be able to find a value in table #3 (contains businesses & cities) by first finding a record in table #1 (7,000+ zip codes) that corresponds to a city (table #2 has the unique cities), and then finding which businesses are located in these cities ( in this specific, original zipcode lookup). And return the business and a description via an API to a front-end results page, which happens to be a WordPress page - but doesn't need to be. I've tried Airtable's API, AirPress (a finicky WordPress plugin for Airtable's API), and I've looked at Sheetsu and a similar spreadsheet as backend and a simple API. I run into the issue where they work fine when you just need to query 1 table, but when you need to use the result from that query in another query to a different table. I'm back in SQL land - where sure it could be done with SQLite - needing to probably create an intersection table or a JOIN and build an API off of that. Is there a way to accomplish what I want without going back to SQL queries and some API?

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Replies (2)
Max Musing
Founder & CEO at BaseDash · | 5 upvotes · 55K views
Recommends
on
BaseDashBaseDash

You're right that there isn't a great way to join tables with Airtable's API. The closest you can get is to use a linked record field, which acts as a pointer to another record. You still end up with the problem you mentioned of having to run another query on the second table separately.

Your best bet is to stick with an actual SQL database. Using an ORM should make your life significantly easier so you don't actually have to write raw SQL. If you still want a graphical interface to your data, BaseDash lets you view and edit SQL databases just like Airtable. A full API with join support is coming soon, so that could be your perfect solution to this problem.

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vikas vimal
Recommends
on
IntegromatIntegromat

Let me introduce you to integromat. It connects these services without you having to work any code. And it even has a decent database built inside it.

It makes is an easy process to develop multistep workflows with multiple services and it’s free tier is surprisingly functional.

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Pros of Airtable
Pros of Elasticsearch
  • 19
    Powerful and easy to use
  • 8
    Robust and dynamic
  • 6
    Quick UI Layer
  • 4
    Practical built in views
  • 3
    Robust API documentation
  • 0
    Great flexibility
  • 326
    Powerful api
  • 315
    Great search engine
  • 230
    Open source
  • 214
    Restful
  • 199
    Near real-time search
  • 97
    Free
  • 84
    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
  • 5
    More than a search engine
  • 3
    Highly Available
  • 3
    Awesome, great tool
  • 3
    Great docs
  • 3
    Easy to scale
  • 2
    Fast
  • 2
    Easy setup
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Great piece of software
  • 2
    Reliable
  • 2
    Potato
  • 2
    Nosql DB
  • 2
    Document Store
  • 1
    Not stable
  • 1
    Scalability
  • 1
    Open
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Ecosystem
  • 1
    Easy to get hot data
  • 0
    Community

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Cons of Airtable
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

    Sign up to add or upvote consMake informed product decisions

    What is Airtable?

    Working with Airtable is as fast and easy as editing a spreadsheet. But only Airtable is backed by the power of a full database, giving you rich features far beyond what a spreadsheet can offer.

    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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    Blog Posts

    Sep 29 2020 at 7:36PM

    WorkOS

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    May 21 2019 at 12:20AM

    Elastic

    ElasticsearchKibanaLogstash+4
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    GitHubPythonReact+42
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    GitHubPythonNode.js+47
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