Amazon RDS vs Amazon Redshift vs Amazon S3

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Amazon RDS
Amazon RDS

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Amazon Redshift
Amazon Redshift

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Amazon S3
Amazon S3

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What is Amazon RDS?

Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

What is Amazon Redshift?

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

What is Amazon S3?

Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web
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      What are some alternatives to Amazon RDS, Amazon Redshift, and Amazon S3?
      Apache Aurora
      Apache Aurora is a service scheduler that runs on top of Mesos, enabling you to run long-running services that take advantage of Mesos' scalability, fault-tolerance, and resource isolation.
      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.
      Oracle
      Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.
      Heroku Postgres
      Heroku Postgres provides a SQL database-as-a-service that lets you focus on building your application instead of messing around with database management.
      Google Cloud SQL
      MySQL databases deployed in the cloud without a fuss. Google Cloud Platform provides you with powerful databases that run fast, don’t run out of space and give your application the redundant, reliable storage it needs.
      See all alternatives
      Decisions about Amazon RDS, Amazon Redshift, and Amazon S3
      John-Daniel Trask
      John-Daniel Trask
      Co-founder & CEO at Raygun · | 19 upvotes · 68.2K views
      atRaygunRaygun
      AWS Elastic Load Balancing (ELB)
      AWS Elastic Load Balancing (ELB)
      Amazon EC2
      Amazon EC2
      nginx
      nginx
      Amazon RDS
      Amazon RDS
      Amazon S3
      Amazon S3
      #LoadBalancerReverseProxy
      #CloudStorage
      #WebServers
      #CloudHosting

      We chose AWS because, at the time, it was really the only cloud provider to choose from.

      We tend to use their basic building blocks (EC2, ELB, Amazon S3, Amazon RDS) rather than vendor specific components like databases and queuing. We deliberately decided to do this to ensure we could provide multi-cloud support or potentially move to another cloud provider if the offering was better for our customers.

      We’ve utilized c3.large nodes for both the Node.js deployment and then for the .NET Core deployment. Both sit as backends behind an nginx instance and are managed using scaling groups in Amazon EC2 sitting behind a standard AWS Elastic Load Balancing (ELB).

      While we’re satisfied with AWS, we do review our decision each year and have looked at Azure and Google Cloud offerings.

      #CloudHosting #WebServers #CloudStorage #LoadBalancerReverseProxy

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      Tim Specht
      Tim Specht
      ‎Co-Founder and CTO at Dubsmash · | 13 upvotes · 57.6K views
      atDubsmashDubsmash
      Amazon RDS for Aurora
      Amazon RDS for Aurora
      Redis
      Redis
      Amazon DynamoDB
      Amazon DynamoDB
      Amazon RDS
      Amazon RDS
      Heroku
      Heroku
      PostgreSQL
      PostgreSQL
      #PlatformAsAService
      #Databases
      #NosqlDatabaseAsAService
      #SqlDatabaseAsAService

      Over the years we have added a wide variety of different storages to our stack including PostgreSQL (some hosted by Heroku, some by Amazon RDS) for storing relational data, Amazon DynamoDB to store non-relational data like recommendations & user connections, or Redis to hold pre-aggregated data to speed up API endpoints.

      Since we started running Postgres ourselves on RDS instead of only using the managed offerings of Heroku, we've gained additional flexibility in scaling our application while reducing costs at the same time.

      We are also heavily testing Amazon RDS for Aurora in its Postgres-compatible version and will also give the new release of Aurora Serverless a try!

      #SqlDatabaseAsAService #NosqlDatabaseAsAService #Databases #PlatformAsAService

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      Jake Stein
      Jake Stein
      CEO at Stitch · | 13 upvotes · 95.2K views
      atStitchStitch
      Go
      Go
      Clojure
      Clojure
      JavaScript
      JavaScript
      Python
      Python
      Kubernetes
      Kubernetes
      AWS OpsWorks
      AWS OpsWorks
      Amazon EC2
      Amazon EC2
      Amazon Redshift
      Amazon Redshift
      Amazon S3
      Amazon S3
      Amazon RDS
      Amazon RDS

      Stitch is run entirely on AWS. All of our transactional databases are run with Amazon RDS, and we rely on Amazon S3 for data persistence in various stages of our pipeline. Our product integrates with Amazon Redshift as a data destination, and we also use Redshift as an internal data warehouse (powered by Stitch, of course).

      The majority of our services run on stateless Amazon EC2 instances that are managed by AWS OpsWorks. We recently introduced Kubernetes into our infrastructure to run the scheduled jobs that execute Singer code to extract data from various sources. Although we tend to be wary of shiny new toys, Kubernetes has proven to be a good fit for this problem, and its stability, strong community and helpful tooling have made it easy for us to incorporate into our operations.

      While we continue to be happy with Clojure for our internal services, we felt that its relatively narrow adoption could impede Singer's growth. We chose Python both because it is well suited to the task, and it seems to have reached critical mass among data engineers. All that being said, the Singer spec is language agnostic, and integrations and libraries have been developed in JavaScript, Go, and Clojure.

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      Ankit Sobti
      Ankit Sobti
      CTO at Postman Inc · | 10 upvotes · 72.4K views
      atPostmanPostman
      dbt
      dbt
      Amazon Redshift
      Amazon Redshift
      Stitch
      Stitch
      Looker
      Looker

      Looker , Stitch , Amazon Redshift , dbt

      We recently moved our Data Analytics and Business Intelligence tooling to Looker . It's already helping us create a solid process for reusable SQL-based data modeling, with consistent definitions across the entire organizations. Looker allows us to collaboratively build these version-controlled models and push the limits of what we've traditionally been able to accomplish with analytics with a lean team.

      For Data Engineering, we're in the process of moving from maintaining our own ETL pipelines on AWS to a managed ELT system on Stitch. We're also evaluating the command line tool, dbt to manage data transformations. Our hope is that Stitch + dbt will streamline the ELT bit, allowing us to focus our energies on analyzing data, rather than managing it.

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      Julien DeFrance
      Julien DeFrance
      Principal Software Engineer at Tophatter · | 16 upvotes · 385.7K 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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      Sebastian Gębski
      Sebastian Gębski
      CTO at Shedul/Fresha · | 6 upvotes · 48.9K views
      atFresha EngineeringFresha Engineering
      Amazon RDS
      Amazon RDS
      Amazon S3
      Amazon S3
      Amazon EKS
      Amazon EKS
      Amazon EC2
      Amazon EC2
      Ansible
      Ansible
      Terraform
      Terraform
      Kubernetes
      Kubernetes
      Docker Compose
      Docker Compose
      Docker
      Docker

      Heroku was a decent choice to start a business, but at some point our platform was too big, too complex & too heterogenic, so Heroku started to be a constraint, not a benefit. First, we've started containerizing our apps with Docker to eliminate "works in my machine" syndrome & uniformize the environment setup. The first orchestration was composed with Docker Compose , but at some point it made sense to move it to Kubernetes. Fortunately, we've made a very good technical decision when starting our work with containers - all the container configuration & provisions HAD (since the beginning) to be done in code (Infrastructure as Code) - we've used Terraform & Ansible for that (correspondingly). This general trend of containerisation was accompanied by another, parallel & equally big project: migrating environments from Heroku to AWS: using Amazon EC2 , Amazon EKS, Amazon S3 & Amazon RDS.

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      Ganesa Vijayakumar
      Ganesa Vijayakumar
      Full Stack Coder | Module Lead · | 15 upvotes · 393K views
      SonarQube
      SonarQube
      Codacy
      Codacy
      Docker
      Docker
      Git
      Git
      Apache Maven
      Apache Maven
      Amazon EC2 Container Service
      Amazon EC2 Container Service
      Microsoft Azure
      Microsoft Azure
      Amazon Route 53
      Amazon Route 53
      Elasticsearch
      Elasticsearch
      Solr
      Solr
      Amazon RDS
      Amazon RDS
      Amazon S3
      Amazon S3
      Heroku
      Heroku
      Hibernate
      Hibernate
      MySQL
      MySQL
      Node.js
      Node.js
      Java
      Java
      Bootstrap
      Bootstrap
      jQuery Mobile
      jQuery Mobile
      jQuery UI
      jQuery UI
      jQuery
      jQuery
      JavaScript
      JavaScript
      React Native
      React Native
      React Router
      React Router
      React
      React

      I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.

      I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).

      As per my work experience and knowledge, I have chosen the followings stacks to this mission.

      UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.

      Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.

      Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.

      Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.

      Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.

      Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.

      Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.

      Happy Coding! Suggestions are welcome! :)

      Thanks, Ganesa

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      Praveen Mooli
      Praveen Mooli
      Technical Leader at Taylor and Francis · | 11 upvotes · 162.6K views
      MongoDB Atlas
      MongoDB Atlas
      Amazon S3
      Amazon S3
      Amazon DynamoDB
      Amazon DynamoDB
      Amazon RDS
      Amazon RDS
      Serverless
      Serverless
      Docker
      Docker
      Terraform
      Terraform
      Travis CI
      Travis CI
      GitHub
      GitHub
      RxJS
      RxJS
      Angular 2
      Angular 2
      AWS Lambda
      AWS Lambda
      Amazon SQS
      Amazon SQS
      Amazon SNS
      Amazon SNS
      Amazon Kinesis Firehose
      Amazon Kinesis Firehose
      Amazon Kinesis
      Amazon Kinesis
      Flask
      Flask
      Python
      Python
      ExpressJS
      ExpressJS
      Node.js
      Node.js
      Spring Boot
      Spring Boot
      Java
      Java
      #Data
      #Devops
      #Webapps
      #Eventsourcingframework
      #Microservices
      #Backend

      We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

      To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

      To build #Webapps we decided to use Angular 2 with RxJS

      #Devops - GitHub , Travis CI , Terraform , Docker , Serverless

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      Interest over time
      Reviews of Amazon RDS, Amazon Redshift, and Amazon S3
      Review ofAmazon S3Amazon S3

      Insanely low prices, quite easy to use, and they're fast. Plus they provide great support. And they're integrated with other AWS services, like CloudFront.

      Seriously, this is the best service of it's kind out there.

      How developers use Amazon RDS, Amazon Redshift, and Amazon S3
      Avatar of Pathwright
      Pathwright uses Amazon RDSAmazon RDS

      While we initially started off running our own Postgres cluster, we evaluated RDS and found it to be an excellent fit for us.

      The failovers, manual scaling, replication, Postgres upgrades, and pretty much everything else has been super smooth and reliable.

      We'll probably need something a little more complex in the future, but RDS performs admirably for now.

      Avatar of AngeloR
      AngeloR uses Amazon RDSAmazon RDS

      We are using RDS for managing PostgreSQL and legacy MSSQL databases.

      Unfortunately while RDS works great for managing the PostgreSQL systems, MSSQL is very much a second class citizen and they don't offer very much capability. Infact, in order to upgrade instance storage for MSSQL we actually have to spin up a new cluster and migrate the data over.

      Avatar of CloudRepo
      CloudRepo uses Amazon S3Amazon S3

      We store the software components that CloudRepo stores for its customers here for the following reasons:

      • Data is Encrypted at Rest
      • Data is stored across multiple physical locations
      • Pricing is competitive
      • Reliability is industry leading and our customers need to be able to access their data at all times list text here
      Avatar of Yelp
      Yelp uses Amazon S3Amazon S3

      In October 2008 we moved to using scribe (now a custom branch), which has served us very well over the past 5+ years that we’ve been using it. We take the logs scribe aggregates and move them into Amazon S3 for storage, which makes using EMR on AWS seamless.

      Avatar of cloak.ly
      cloak.ly uses Amazon S3Amazon S3

      S3 serves as zero-knowledge temporary storage. Files are encrypted in the browser before being uploaded in chunks to S3. When the target recipient downloads them the chunks are reassembled and decrypted in the browser. Files expire after a week and the encrypted chunks are permanently deleted from S3.

      Avatar of CloudRepo
      CloudRepo uses Amazon S3Amazon S3

      Since we generate a static website for our website, AWS S3 provides hosting for us so that we don't have to run our own servers just to serve up static content.

      The pricing is great as you only pay for what you use.

      Avatar of Tana
      Tana uses Amazon S3Amazon S3

      This object storage is always evolving and getting harder to explain. We use it for 1) hosting every static websites, 2) datalake to store every transaction and 3) query with Athena / S3 Select.

      Avatar of Olo
      Olo uses Amazon RedshiftAmazon Redshift

      Aggressive archiving of historical data to keep the production database as small as possible. Using our in-house soon-to-be-open-sourced ETL library, SharpShifter.

      Avatar of Wirkn Inc.
      Wirkn Inc. uses Amazon RDSAmazon RDS

      Our PostgreSQL servers, where we keep the bulk of Wirkn data, are hosted on the fantastically easy and reliable AWS RDS platform.

      Avatar of Digital2Go
      Digital2Go uses Amazon RDSAmazon RDS

      We use Aurora for our OLTP database, it provides significant speed increases on top of MySQL without the need to manage it

      Avatar of fadingdust
      fadingdust uses Amazon RDSAmazon RDS

      RDS allows us to replicate the development databases locally as well as making it available to CircleCI.

      Avatar of Christian Moeller
      Christian Moeller uses Amazon RedshiftAmazon Redshift

      Connected to BI (Pentaho)

      Avatar of Kovid Rathee
      Kovid Rathee uses Amazon RedshiftAmazon Redshift

      OLAP and BI

      How much does Amazon RDS cost?
      How much does Amazon Redshift cost?
      How much does Amazon S3 cost?