Amazon RDS vs Amazon Redshift vs Amazon S3

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Amazon RDS

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Amazon Redshift

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Amazon S3

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Amazon RDS vs Amazon Redshift vs Amazon S3: What are the differences?

Introduction

In this Markdown code, we will outline the key differences between Amazon RDS, Amazon Redshift, and Amazon S3 in a website-friendly format.

  1. Deployment and Use Case: Amazon RDS is a relational database service suitable for OLTP workloads, while Amazon Redshift is a data warehousing service optimized for OLAP workloads. On the other hand, Amazon S3 is an object storage service ideal for storing and retrieving large amounts of unstructured data.

  2. Scalability: Amazon RDS allows you to scale vertically by upgrading to a larger instance, while Amazon Redshift and Amazon S3 support horizontal scaling for handling increased workloads and large amounts of data.

  3. Data Structure and Querying: Amazon RDS supports relational database structures and SQL querying, making it suitable for transactional applications. Amazon Redshift is designed for analyzing large datasets using SQL queries optimized for data warehousing. Amazon S3, as an object storage service, does not have built-in querying capabilities, requiring the use of additional tools or services for data analysis.

  4. Performance: Amazon Redshift offers high-performance analytics with massively parallel processing (MPP), columnar storage, and data compression techniques. Amazon RDS can provide good performance for OLTP workloads but may not be optimized for data warehousing tasks. Amazon S3 is designed for high scalability and availability but may not offer the same level of performance as specialized data warehouse solutions like Amazon Redshift.

  5. Data Durability and Cost: Amazon S3 offers a durable storage solution with data redundancy across multiple facilities and resilient data protection mechanisms. Amazon RDS and Amazon Redshift also provide data durability but may incur higher costs for storage and data processing, especially for large-scale data warehousing operations.

  6. Management and Maintenance: Amazon RDS manages routine database tasks such as patching, backups, and monitoring, easing the burden of database administration. Amazon Redshift requires more in-depth management for data warehousing tasks, including data distribution and optimization for query performance. Amazon S3 simplifies data storage and retrieval but may involve more manual management for data organization and access control.

In Summary, the key differences between Amazon RDS, Amazon Redshift, and Amazon S3 lie in their deployment, scalability, data structures, performance, cost, and management approaches for different types of workloads and data processing tasks.

Advice on Amazon RDS, Amazon Redshift, and Amazon S3

Hello! I have a mobile app with nearly 100k MAU, and I want to add a cloud file storage service to my app.

My app will allow users to store their image, video, and audio files and retrieve them to their device when necessary.

I have already decided to use PHP & Laravel as my backend, and I use Contabo VPS. Now, I need an object storage service for my app, and my options are:

  • Amazon S3 : It sounds to me like the best option but the most expensive. Closest to my users (MENA Region) for other services, I will have to go to Europe. Not sure how important this is?

  • DigitalOcean Spaces : Seems like my best option for price/service, but I am still not sure

  • Wasabi: the best price (6 USD/MONTH/TB) and free bandwidth, but I am not sure if it fits my needs as I want to allow my users to preview audio and video files. They don't recommend their service for streaming videos.

  • Backblaze B2 Cloud Storage: Good price but not sure about them.

  • There is also the self-hosted s3 compatible option, but I am not sure about that.

Any thoughts will be helpful. Also, if you think I should post in a different sub, please tell me.

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Replies (2)
Michira Griffins
Software Developer at Codeshares Ltd · | 1 upvotes · 111.9K views

If pricing is the issue i'd suggest you use digital ocean, but if its not use amazon was digital oceans API is s3 compatible

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Recommends
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Cloudways Cloudways

Hello Mohammad, I am using : Cloudways >> AWS >> Bahrain for last 2 years. This is best I consider out of my 10 year research on Laravel hosting.

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We need to perform ETL from several databases into a data warehouse or data lake. We want to

  • keep raw and transformed data available to users to draft their own queries efficiently
  • give users the ability to give custom permissions and SSO
  • move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

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Replies (3)
John Nguyen
Recommends
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AirflowAirflowAWS LambdaAWS Lambda

You could also use AWS Lambda and use Cloudwatch event schedule if you know when the function should be triggered. The benefit is that you could use any language and use the respective database client.

But if you orchestrate ETLs then it makes sense to use Apache Airflow. This requires Python knowledge.

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AirflowAirflow

Though we have always built something custom, Apache airflow (https://airflow.apache.org/) stood out as a key contender/alternative when it comes to open sources. On the commercial offering, Amazon Redshift combined with Amazon Kinesis (for complex manipulations) is great for BI, though Redshift as such is expensive.

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Recommends

You may want to look into a Data Virtualization product called Conduit. It connects to disparate data sources in AWS, on prem, Azure, GCP, and exposes them as a single unified Spark SQL view to PowerBI (direct query) or Tableau. Allows auto query and caching policies to enhance query speeds and experience. Has a GPU query engine and optimized Spark for fallback. Can be deployed on your AWS VM or on prem, scales up and out. Sounds like the ideal solution to your needs.

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Decisions about Amazon RDS, Amazon Redshift, and Amazon S3

Minio is a free and open source object storage system. It can be self-hosted and is S3 compatible. During the early stage it would save cost and allow us to move to a different object storage when we scale up. It is also fast and easy to set up. This is very useful during development since it can be run on localhost.

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Julien Lafont

Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.

Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.

BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.

BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.

Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.

BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.

We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution

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Gabriel Pa

We offer our customer HIPAA compliant storage. After analyzing the market, we decided to go with Google Storage. The Nodejs API is ok, still not ES6 and can be very confusing to use. For each new customer, we created a different bucket so they can have individual data and not have to worry about data loss. After 1000+ customers we started seeing many problems with the creation of new buckets, with saving or retrieving a new file. Many false positive: the Promise returned ok, but in reality, it failed.

That's why we switched to S3 that just works.

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Pros of Amazon RDS
Pros of Amazon Redshift
Pros of Amazon S3
  • 165
    Reliable failovers
  • 156
    Automated backups
  • 130
    Backed by amazon
  • 92
    Db snapshots
  • 87
    Multi-availability
  • 30
    Control iops, fast restore to point of time
  • 28
    Security
  • 24
    Elastic
  • 20
    Push-button scaling
  • 20
    Automatic software patching
  • 4
    Replication
  • 3
    Reliable
  • 2
    Isolation
  • 41
    Data Warehousing
  • 27
    Scalable
  • 17
    SQL
  • 14
    Backed by Amazon
  • 5
    Encryption
  • 1
    Cheap and reliable
  • 1
    Isolation
  • 1
    Best Cloud DW Performance
  • 1
    Fast columnar storage
  • 590
    Reliable
  • 492
    Scalable
  • 456
    Cheap
  • 329
    Simple & easy
  • 83
    Many sdks
  • 30
    Logical
  • 13
    Easy Setup
  • 11
    REST API
  • 11
    1000+ POPs
  • 6
    Secure
  • 4
    Plug and play
  • 4
    Easy
  • 3
    Web UI for uploading files
  • 2
    Faster on response
  • 2
    Flexible
  • 2
    GDPR ready
  • 1
    Easy to use
  • 1
    Plug-gable
  • 1
    Easy integration with CloudFront

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Cons of Amazon RDS
Cons of Amazon Redshift
Cons of Amazon S3
    Be the first to leave a con
      Be the first to leave a con
      • 7
        Permissions take some time to get right
      • 6
        Requires a credit card
      • 6
        Takes time/work to organize buckets & folders properly
      • 3
        Complex to set up

      Sign up to add or upvote consMake informed product decisions

      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

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

      What companies use Amazon RDS?
      What companies use Amazon Redshift?
      What companies use Amazon S3?

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      What tools integrate with Amazon RDS?
      What tools integrate with Amazon Redshift?
      What tools integrate with Amazon S3?

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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
      Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.
      See all alternatives