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CloudBoost
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CloudBoost vs MongoDB: What are the differences?

CloudBoost: One complete database service that not only does data storage, but also search, real-time and more. CloudBoost.io is a database service for the “next web” - that not only does data-storage, but also search, real-time and a whole lot more which enables developers to build much richer apps with 50% less time saving them a ton of cost and helping them go to market much faster; MongoDB: The database for giant ideas. 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.

CloudBoost and MongoDB are primarily classified as "NoSQL Database as a Service" and "Databases" tools respectively.

"Good features, simple API, save a lot of time" is the primary reason why developers consider CloudBoost over the competitors, whereas "Document-oriented storage" was stated as the key factor in picking MongoDB.

MongoDB is an open source tool with 16.3K GitHub stars and 4.1K GitHub forks. Here's a link to MongoDB's open source repository on GitHub.

- No public GitHub repository available -

What is CloudBoost?

CloudBoost.io is a database service for the “next web” - that not only does data-storage, but also search, real-time and a whole lot more which enables developers to build much richer apps with 50% less time saving them a ton of cost and helping them go to market much faster.

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.
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        What are some alternatives to CloudBoost and MongoDB?
        Firebase
        Firebase is a cloud service designed to power real-time, collaborative applications. Simply add the Firebase library to your application to gain access to a shared data structure; any changes you make to that data are automatically synchronized with the Firebase cloud and with other clients within milliseconds.
        Parse
        With Parse, you can add a scalable and powerful backend in minutes and launch a full-featured app in record time without ever worrying about server management. We offer push notifications, social integration, data storage, and the ability to add rich custom logic to your app’s backend with Cloud Code.
        Amazon DynamoDB
        With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.
        Cloud Firestore
        Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale.
        Azure Cosmos DB
        Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.
        See all alternatives
        Decisions about CloudBoost and MongoDB
        MongoDB
        MongoDB

        I starting using MongoDB because it was much easier to implement in production then hosted SQL, and found that a lot of the limitation you think of from a document store vs a relational database were overcome by connecting the application to a graphql API, making retrieval seamless. Mongos latest upgrades as well as Stitch and Mongo mobile make it a perfect fit especially if your application will be cross platform web and mobile.

        See more
        Anton Sidelnikov
        Anton Sidelnikov
        Backend Developer at Beamery · | 6 upvotes · 9.1K 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 · | 3 upvotes · 7.5K 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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        Antonio Sanchez
        Antonio Sanchez
        CEO at Kokoen GmbH · | 11 upvotes · 101.9K views
        atKokoen GmbHKokoen GmbH
        PHP
        PHP
        Laravel
        Laravel
        MySQL
        MySQL
        Go
        Go
        MongoDB
        MongoDB
        JavaScript
        JavaScript
        Node.js
        Node.js
        ExpressJS
        ExpressJS

        Back at the start of 2017, we decided to create a web-based tool for the SEO OnPage analysis of our clients' websites. We had over 2.000 websites to analyze, so we had to perform thousands of requests to get every single page from those websites, process the information and save the big amounts of data somewhere.

        Very soon we realized that the initial chosen script language and database, PHP, Laravel and MySQL, was not going to be able to cope efficiently with such a task.

        By that time, we were doing some experiments for other projects with a language we had recently get to know, Go , so we decided to get a try and code the crawler using it. It was fantastic, we could process much more data with way less CPU power and in less time. By using the concurrency abilites that the language has to offers, we could also do more Http requests in less time.

        Unfortunately, I have no comparison numbers to show about the performance differences between Go and PHP since the difference was so clear from the beginning and that we didn't feel the need to do further comparison tests nor document it. We just switched fully to Go.

        There was still a problem: despite the big amount of Data we were generating, MySQL was performing very well, but as we were adding more and more features to the software and with those features more and more different type of data to save, it was a nightmare for the database architects to structure everything correctly on the database, so it was clear what we had to do next: switch to a NoSQL database. So we switched to MongoDB, and it was also fantastic: we were expending almost zero time in thinking how to structure the Database and the performance also seemed to be better, but again, I have no comparison numbers to show due to the lack of time.

        We also decided to switch the website from PHP and Laravel to JavaScript and Node.js and ExpressJS since working with the JSON Data that we were saving now in the Database would be easier.

        As of now, we don't only use the tool intern but we also opened it for everyone to use for free: https://tool-seo.com

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        Jeyabalaji Subramanian
        Jeyabalaji Subramanian
        CTO at FundsCorner · | 24 upvotes · 351.3K 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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        Khauth György
        Khauth György
        CTO at SalesAutopilot Kft. · | 12 upvotes · 113.5K views
        atSalesAutopilot Kft.SalesAutopilot Kft.
        Amazon CloudWatch
        Amazon CloudWatch
        Amazon SNS
        Amazon SNS
        Amazon CloudFront
        Amazon CloudFront
        Amazon Route 53
        Amazon Route 53
        MySQL
        MySQL
        MongoDB
        MongoDB
        Redis
        Redis
        jQuery UI
        jQuery UI
        Vue.js
        Vue.js
        Vuetify
        Vuetify
        vuex
        vuex
        Docker
        Docker
        Jenkins
        Jenkins
        AWS CodePipeline
        AWS CodePipeline
        GitHub
        GitHub

        I'm the CTO of a marketing automation SaaS. Because of the continuously increasing load we moved to the AWSCloud. We are using more and more features of AWS: Amazon CloudWatch, Amazon SNS, Amazon CloudFront, Amazon Route 53 and so on.

        Our main Database is MySQL but for the hundreds of GB document data we use MongoDB more and more. We started to use Redis for cache and other time sensitive operations.

        On the front-end we use jQuery UI + Smarty but now we refactor our app to use Vue.js with Vuetify. Because our app is relatively complex we need to use vuex as well.

        On the development side we use GitHub as our main repo, Docker for local and server environment and Jenkins and AWS CodePipeline for Continuous Integration.

        See more
        Jeyabalaji Subramanian
        Jeyabalaji Subramanian
        CTO at FundsCorner · | 12 upvotes · 23.6K 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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        Ajit Parthan
        Ajit Parthan
        CTO at Shaw Academy ·