Cassandra vs MongoDB vs PostgreSQL

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Cassandra
Cassandra

2.7K
2.3K
+ 1
454
MongoDB
MongoDB

26.1K
22.7K
+ 1
3.9K
PostgreSQL
PostgreSQL

26.2K
21.8K
+ 1
3.4K

What is Cassandra?

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

What is 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.

What is PostgreSQL?

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.
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    What are some alternatives to Cassandra, MongoDB, and PostgreSQL?
    HBase
    Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
    Hadoop
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    Redis
    Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets.
    Couchbase
    Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands.
    MySQL
    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
    See all alternatives
    Decisions about Cassandra, MongoDB, and PostgreSQL
    Rails
    Rails
    Sidekiq
    Sidekiq
    PostgreSQL
    PostgreSQL
    Redis
    Redis
    MongoDB
    MongoDB
    Vue.js
    Vue.js
    vuex
    vuex
    jQuery
    jQuery
    React
    React
    Redux
    Redux
    Yarn
    Yarn
    #Bulma.io
    #Font-awesome

    I'm building a new process management tool. I decided to build with Rails as my backend, using Sidekiq for background jobs. I chose to work with these tools because I've worked with them before and know that they're able to get the job done. They may not be the sexiest tools, but they work and are reliable, which is what I was optimizing for. For data stores, I opted for PostgreSQL and Redis. Because I'm planning on offering dashboards, I wanted a SQL database instead of something like MongoDB that might work early on, but be difficult to use as soon as I want to facilitate aggregate queries.

    On the front-end I'm using Vue.js and vuex in combination with #Turbolinks. In effect, I want to render most pages on the server side without key interactions being managed by Vue.js . This is the first project I'm working on where I've explicitly decided not to include jQuery . I have found React and Redux.js more confusing to setup. I appreciate the opinionated approach from the Vue.js community and that things just work together the way that I'd expect. To manage my javascript dependencies, I'm using Yarn .

    For CSS frameworks, I'm using #Bulma.io. I really appreciate it's minimal nature and that there are no hard javascript dependencies. And to add a little spice, I'm using #font-awesome.

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    Gregory Koberger
    Gregory Koberger
    Founder · | 13 upvotes · 125.1K views
    atReadMe.ioReadMe.io
    MongoDB
    MongoDB
    MySQL
    MySQL
    PostgreSQL
    PostgreSQL
    MongoDB Atlas
    MongoDB Atlas
    MongoLab
    MongoLab
    Compose
    Compose

    We went with MongoDB , almost by mistake. I had never used it before, but I knew I wanted the *EAN part of the MEAN stack, so why not go all in. I come from a background of SQL (first MySQL , then PostgreSQL ), so I definitely abused Mongo at first... by trying to turn it into something more relational than it should be. But hey, data is supposed to be relational, so there wasn't really any way to get around that.

    There's a lot I love about MongoDB, and a lot I hate. I still don't know if we made the right decision. We've been able to build much quicker, but we also have had some growing pains. We host our databases on MongoDB Atlas , and I can't say enough good things about it. We had tried MongoLab and Compose before it, and with MongoDB Atlas I finally feel like things are in a good place. I don't know if I'd use it for a one-off small project, but for a large product Atlas has given us a ton more control, stability and trust.

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    Anton Sidelnikov
    Anton Sidelnikov
    Backend Developer at Beamery · | 8 upvotes · 9.9K views
    PostgreSQL
    PostgreSQL
    MongoDB
    MongoDB

    In my opinion PostgreSQL is totally over MongoDB - not only works with structured data & SQL & strict types, but also has excellent support for unstructured data as separate data type (you can store arbitrary JSONs - and they may be also queryable, depending on one of format's you may choose). Both writes & reads are much faster, then in Mongo. So you can get best on Document NoSQL & SQL in single database..

    Formal downside of PostgreSQL is clustering scalability. There's not simple way to build distributed a cluster. However, two points:

    1) You will need much more time before you need to actually scale due to PG's efficiency. And if you follow database-per-service pattern, maybe you won't need ever, cause dealing few billion records on single machine is an option for PG.

    2) When you need to - you do it in a way you need, including as a part of app's logic (e.g. sharding by key, or PG-based clustering solution with strict model), scalability will be very transparent, much more obvious than Mongo's "cluster just works (but then fails)" replication.

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    Zach Coffin
    Zach Coffin
    Software Developer · | 4 upvotes · 8.3K views
    PostgreSQL
    PostgreSQL
    MongoDB
    MongoDB

    I started using PostgreSQL because I started a job at a company that was already using it as well as MongoDB. The main difference between the two from my perspective is that postgres columns are a chore to add/remove/modify whereas you can throw whatever you want into a mongo collection. And personally I prefer the query language for postgres over that of mongo, but they both have their merits. Maybe someday I'll be a DBA and have more insight to share but for now there's my 2 cents.

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    Jeyabalaji Subramanian
    Jeyabalaji Subramanian
    CTO at FundsCorner · | 25 upvotes · 907.2K views
    atFundsCornerFundsCorner
    MongoDB
    MongoDB
    PostgreSQL
    PostgreSQL
    MongoDB Stitch
    MongoDB Stitch
    Node.js
    Node.js
    Amazon SQS
    Amazon SQS
    Python
    Python
    SQLAlchemy
    SQLAlchemy
    AWS Lambda
    AWS Lambda
    Zappa
    Zappa

    Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

    We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

    Based on the above criteria, we selected the following tools to perform the end to end data replication:

    We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

    We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

    In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

    Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

    In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

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    Jeyabalaji Subramanian
    Jeyabalaji Subramanian
    CTO at FundsCorner · | 12 upvotes · 79.5K views
    atFundsCornerFundsCorner
    PostgreSQL
    PostgreSQL
    MongoDB
    MongoDB
    MongoDB Atlas
    MongoDB Atlas

    Database is at the heart of any technology stack. It is no wonder we spend a lot of time choosing the right database before we dive deep into product building.

    When we were faced with the question of what database to choose, we set the following criteria: The database must (1) Have a very high transaction throughput. We wanted to err on the side of "reads" but not on the "writes". (2) be flexible. I.e. be adaptive enough to take - in data variations. Since we are an early-stage start-up, not everything is set in stone. (3) Fast & easy to work with (4) Cloud Native. We did not want to spend our time in "ANY" infrastructure management.

    Based on the above, we picked PostgreSQL and MongoDB for evaluation. We tried a few iterations on hardening the data model with PostgreSQL, but realised that we can move much faster by loosely defining the schema (with just a few fundamental principles intact).

    Thus we switched to MongoDB. Before diving in, we validated a few core principles such as: (1) Transaction guarantee. Until 3.6, MongoDB supports Transaction guarantee at Document level. From 4.0 onwards, you can achieve transaction guarantee across multiple documents.

    (2) Primary Keys & Indexing: Like any RDBMS, MongoDB supports unique keys & indexes to ensure data integrity & search ability

    (3) Ability to join data across data sets: MongoDB offers a super-rich aggregate framework that enables one to filter and group data

    (4) Concurrency handling: MongoDB offers specific operations (such as findOneAndUpdate), which when coupled with Optimistic Locking, can be used to achieve concurrency.

    Above all, MongoDB offers a complete no-frills Cloud Database as a service - MongoDB Atlas. This kind of sealed the deal for us.

    Looking back, choosing MongoDB with MongoDB Atlas was one of the best decisions we took and it is serving us well. My only gripe is that there must be a way to scale-up or scale-down the Atlas configuration at different parts of the day with minimal downtime.

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    Tim Nolet
    Tim Nolet
    Founder, Engineer & Dishwasher at Checkly · | 8 upvotes · 78.5K views
    atChecklyHQChecklyHQ
    PostgreSQL
    PostgreSQL
    Heroku
    Heroku
    Node.js
    Node.js
    MongoDB
    MongoDB
    Amazon DynamoDB
    Amazon DynamoDB

    PostgreSQL Heroku Node.js MongoDB Amazon DynamoDB

    When I started building Checkly, one of the first things on the agenda was how to actually structure our SaaS database model: think accounts, users, subscriptions etc. Weirdly, there is not a lot of information on this on the "blogopshere" (cringe...). After research and some false starts with MongoDB and Amazon DynamoDB we ended up with PostgreSQL and a schema consisting of just four tables that form the backbone of all generic "Saasy" stuff almost any B2B SaaS bumps into.

    In a nutshell:cPostgreSQL Heroku Node.js MongoDB Amazon DynamoDB

    When I started building Checkly, one of the first things on the agenda was how to actually structure our SaaS database model: think accounts, users, subscriptions etc. Weirdly, there is not a lot of information on this on the "blogopshere" (cringe...). After research and some false starts with MongoDB and Amazon DynamoDB we ended up with PostgreSQL and a schema consisting of just four tables that form the backbone of all generic "Saasy" stuff almost any B2B SaaS bumps into.

    In a nutshell:

    • We use Postgres on Heroku.
    • We use a "one database, on schema" approach for partitioning customer data.
    • We use an accounts, memberships and users table to create a many-to-many relation between users and accounts.
    • We completely decouple prices, payments and the exact ingredients for a customer's plan.

    All the details including a database schema diagram are in the linked blog post.

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    Łukasz Korecki
    Łukasz Korecki
    CTO & Co-founder at EnjoyHQ · | 12 upvotes · 113.7K views
    atEnjoyHQEnjoyHQ
    RethinkDB
    RethinkDB
    MongoDB
    MongoDB
    PostgreSQL
    PostgreSQL

    We initially chose RethinkDB because of the schema-less document store features, and better durability resilience/story than MongoDB In the end, it didn't work out quite as we expected: there's plenty of scalability issues, it's near impossible to run analytical workloads and small community makes working with Rethink a challenge. We're in process of migrating all our workloads to PostgreSQL and hopefully, we will be able to decommission our RethinkDB deployment soon.

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    Mauro Bennici
    Mauro Bennici
    CTO at You Are My GUide · | 7 upvotes · 29.3K views
    atYou Are My GUideYou Are My GUide
    PostgreSQL
    PostgreSQL
    TimescaleDB
    TimescaleDB
    MongoDB
    MongoDB

    PostgreSQL plus TimescaleDB allow us to concentrate the business effort on how to analyze valuable data instead of manage them on IT side. We are now able to ingest thousand of social shares "managed" data without compromise the scalability of the system or the time query. TimescaleDB is transparent to PostgreSQL , so we continue to use the same SQL syntax without any changes. At the same time, because we need to manage few document objects we dismissed the MongoDB cluster.

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    Robert Zuber
    Robert Zuber
    CTO at CircleCI · | 22 upvotes · 752.7K views
    atCircleCICircleCI
    MongoDB
    MongoDB
    PostgreSQL
    PostgreSQL
    Redis
    Redis
    GitHub
    GitHub
    Amazon S3
    Amazon S3

    We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

    As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

    When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

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    Martin Johannesson
    Martin Johannesson
    Senior Software Developer at IT Minds · | 11 upvotes · 39.2K views
    atIT MindsIT Minds
    TypeScript
    TypeScript
    Node.js
    Node.js
    TypeORM
    TypeORM
    PostgreSQL
    PostgreSQL
    Apollo
    Apollo
    GraphQL
    GraphQL
    Next.js
    Next.js
    MongoDB
    MongoDB
    React
    React
    PWA
    PWA
    AMP
    AMP
    #B2B
    #Backend
    #Serverless

    At IT Minds we create customized internal or #B2B web and mobile apps. I have a go to stack that I pitch to our customers consisting of 3 core areas. 1) A data core #backend . 2) A micro #serverless #backend. 3) A user client #frontend.

    For the Data Core I create a backend using TypeScript Node.js and with TypeORM connecting to a PostgreSQL Exposing an action based api with Apollo GraphQL

    For the micro serverless backend, which purpose is verification for authentication, autorization, logins and the likes. It is created with Next.js api pages. Using MongoDB to store essential information, caching etc.

    Finally the frontend is built with React using Next.js , TypeScript and @Apollo. We create the frontend as a PWA and have a AMP landing page by default.

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    Nicolas Apx
    Nicolas Apx
    CEO - FullStack Javascript at Apx Development Limited · | 14 upvotes · 33.4K views
    atAPX DevelopmentAPX Development
    Python
    Python
    Node.js
    Node.js
    MongoDB
    MongoDB
    PostgreSQL
    PostgreSQL

    I am planning on building a micro-service eCommerce back-end to be easy to reuse in any project as we need. I would like to use both Python and Node.js and MongoDB & PostgreSQL , in your opinion which one would best suited for the following services:

    • Users-service
    • Products-service
    • Auth-service
    • Inventory-service
    • Order-service
    • Payment-service
    • Sku-service
    • And more not yet defined....

    Thanks

    Nicolas

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    Bryam Rodriguez
    Bryam Rodriguez
    Ruby
    Ruby
    Rails
    Rails
    React
    React
    Redux
    Redux
    Create React App
    Create React App
    Jest
    Jest
    react-testing-library
    react-testing-library
    RSpec
    RSpec
    PostgreSQL
    PostgreSQL
    MongoDB
    MongoDB
    Redis
    Redis
    React Native
    React Native
    Next.js
    Next.js
    Python
    Python
    Bit
    Bit
    JavaScript
    JavaScript

    I'm working as one of the engineering leads in RunaHR. As our platform is a Saas, we thought It'd be good to have an API (We chose Ruby and Rails for this) and a SPA (built with React and Redux ) connected. We started the SPA with Create React App since It's pretty easy to start.

    We use Jest as the testing framework and react-testing-library to test React components. In Rails we make tests using RSpec.

    Our main database is PostgreSQL, but we also use MongoDB to store some type of data. We started to use Redis  for cache and other time sensitive operations.

    We have a couple of extra projects: One is an Employee app built with React Native and the other is an internal back office dashboard built with Next.js for the client and Python in the backend side.

    Since we have different frontend apps we have found useful to have Bit to document visual components and utils in JavaScript.

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    George Krachtopoulos
    George Krachtopoulos
    GraphQL
    GraphQL
    MongoDB
    MongoDB
    PostgreSQL
    PostgreSQL
    MySQL
    MySQL
    Node.js
    Node.js
    React
    React
    Django
    Django

    I would like to build a medium to large scale app, that has real-time operations and a good authentication system and a secure and fast API. Should I use Django with React only? Or maybe use Django for the API, Node.js for real-time operations and React for the frontend? Any suggestions? Which database should I use with those technologies? Should I use both MySQL / PostgreSQL and MongoDB together? Should I use only MongoDB or MySQL / PostgreSQL? Or is it better to go with both MySQL and PostgreSQL at the same time? Should I use also GraphQL?

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    George Krachtopoulos
    George Krachtopoulos
    GraphQL
    GraphQL
    React
    React
    Node.js
    Node.js
    MongoDB
    MongoDB
    Django
    Django
    Python
    Python
    PostgreSQL
    PostgreSQL

    Hello everyone,

    Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

    *The project is going to use React for the front-end and GraphQL is going to be used for the API.

    Thank you all. Any answer or advice would be really helpful!

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    Interest over time
    Reviews of Cassandra, MongoDB, and PostgreSQL
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    How developers use Cassandra, MongoDB, and PostgreSQL
    Avatar of AngeloR
    AngeloR uses PostgreSQLPostgreSQL

    We use postgresql for the merge between sql/nosql. A lot of our data is unstructured JSON, or JSON that is currently in flux due to some MVP/interation processes that are going on. PostgreSQL gives the capability to do this.

    At the moment PostgreSQL on amazon is only at 9.5 which is one minor version down from support for document fragment updates which is something that we are waiting for. However, that may be some ways away.

    Other than that, we are using PostgreSQL as our main SQL store as a replacement for all the MSSQL databases that we have. Not only does it have great support through RDS (small ops team), but it also has some great ways for us to migrate off RDS to managed EC2 instances down the line if we need to.

    Avatar of Cloudcraft
    Cloudcraft uses PostgreSQLPostgreSQL

    PostgreSQL combines the best aspects of traditional SQL databases such as reliability, consistent performance, transactions, querying power, etc. with the flexibility of schemaless noSQL systems that are all the rage these days. Through the powerful JSON column types and indexes, you can now have your cake and eat it too! PostgreSQL may seem a bit arcane and old fashioned at first, but the developers have clearly shown that they understand databases and the storage trends better than almost anyone else. It definitely deserves to be part of everyone's toolbox; when you find yourself needing rock solid performance, operational simplicity and reliability, reach for PostgresQL.

    Avatar of Soundcloud
    Soundcloud uses CassandraCassandra

    Stitch is a wrapper around a Cassandra database. It has a web application that provides read-access to the counts through an HTTP API. The counts are written to Cassandra in two distinct ways, and it's possible to use either or both of them:

    • Real-time: For real-time updates, Stitch has a processor application that handles a stream of events coming from a broker and increments the appropriate counts in Cassandra.

    • Batch: The batch part is a MapReduce job running on Hadoop that reads event logs, calculates the overall totals, and bulk loads this into Cassandra.

    Avatar of Brandon Adams
    Brandon Adams uses PostgreSQLPostgreSQL

    Relational data stores solve a lot of problems reasonably well. Postgres has some data types that are really handy such as spatial, json, and a plethora of useful dates and integers. It has good availability of indexing solutions, and is well-supported for both custom modifications as well as hosting options (I like Amazon's Postgres for RDS). I use HoneySQL for Clojure as a composable AST that translates reliably to SQL. I typically use JDBC on Clojure, usually via org.clojure/java.jdbc.

    Avatar of Tarun Singh
    Tarun Singh uses MongoDBMongoDB

    Used MongoDB as primary database. It holds trip data of NYC taxis for the year 2013. It is a huge dataset and it's primary feature is geo coordinates with pickup and drop off locations. Also used MongoDB's map reduce to process this large dataset for aggregation. This aggregated result was then used to show visualizations.

    Avatar of Trello
    Trello uses MongoDBMongoDB

    MongoDB fills our more traditional database needs. We knew we wanted Trello to be blisteringly fast. One of the coolest and most performance-obsessed teams we know is our next-door neighbor and sister company StackExchange. Talking to their dev lead David at lunch one day, I learned that even though they use SQL Server for data storage, they actually primarily store a lot of their data in a denormalized format for performance, and normalize only when they need to.

    Avatar of Foursquare
    Foursquare uses MongoDBMongoDB

    Nearly all of our backend storage is on MongoDB. This has also worked out pretty well. It's enabled us to scale up faster/easier than if we had rolled our own solution on top of PostgreSQL (which we were using previously). There have been a few roadbumps along the way, but the team at 10gen has been a big help with thing.

    Avatar of ReviewTrackers
    ReviewTrackers uses PostgreSQLPostgreSQL

    PostgreSQL is responsible for nearly all data storage, validation and integrity. We leverage constraints, functions and custom extensions to ensure we have only one source of truth for our data access rules and that those rules live as close to the data as possible. Call us crazy, but ORMs only lead to ruin and despair.

    Avatar of Jeff Flynn
    Jeff Flynn uses PostgreSQLPostgreSQL

    Tried MongoDB - early euphoria - later dread. Tried MySQL - not bad at all. Found PostgreSQL - will never go back. So much support for this it should be your first choice. Simple local (free) installation, and one-click setup in Heroku - lots of options in terms of pricing/performance combinations.

    Avatar of AngeloR
    AngeloR uses MongoDBMongoDB

    We are testing out MongoDB at the moment. Currently we are only using a small EC2 setup for a delayed job queue backed by agenda. If it works out well we might look to see where it could become a primary document storage engine for us.

    Avatar of Matt Welke
    Matt Welke uses MongoDBMongoDB

    Used for proofs of concept and personal projects with a document data model, especially with need for strong geographic queries. Often not chosen in long term apps due to chance data model can end up relational as needs develop.

    Avatar of Vital Labs, Inc.
    Vital Labs, Inc. uses CassandraCassandra

    Cassandra is our data management workhorse. It handles all our key-value services, supports time-series data storage and retrieval, securely stores all our audit trails, and backs our Datomic database.

    Avatar of SocialCops
    SocialCops uses CassandraCassandra

    While we experimented with Cassandra in the past, we are no longer using it. It is, however, open for consideration in future projects.

    Avatar of ShareThis
    ShareThis uses CassandraCassandra

    We are using Cassandra in a few of our apps. One of them is as a count service application to track the number of shares, clicks.. etc

    Avatar of Kaiko
    Kaiko uses CassandraCassandra
    How much does Cassandra cost?
    How much does MongoDB cost?
    How much does PostgreSQL cost?
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