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Amazon Redshift

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Amazon Redshift vs Amazon S3: What are the differences?

Developers describe Amazon Redshift as "Fast, fully managed, petabyte-scale data warehouse service". Redshift makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. It is optimized for datasets 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. On the other hand, Amazon S3 is detailed as "Store and retrieve any amount of data, at any time, from anywhere on the web". 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.

Amazon Redshift belongs to "Big Data as a Service" category of the tech stack, while Amazon S3 can be primarily classified under "Cloud Storage".

Some of the features offered by Amazon Redshift are:

  • Optimized for Data Warehousing- It uses columnar storage, data compression, and zone maps to reduce the amount of IO needed to perform queries. Redshift has a massively parallel processing (MPP) architecture, parallelizing and distributing SQL operations to take advantage of all available resources.
  • Scalable- With a few clicks of the AWS Management Console or a simple API call, you can easily scale the number of nodes in your data warehouse up or down as your performance or capacity needs change.
  • No Up-Front Costs- You pay only for the resources you provision. You can choose On-Demand pricing with no up-front costs or long-term commitments, or obtain significantly discounted rates with Reserved Instance pricing.

On the other hand, Amazon S3 provides the following key features:

  • Write, read, and delete objects containing from 1 byte to 5 terabytes of data each. The number of objects you can store is unlimited.
  • Each object is stored in a bucket and retrieved via a unique, developer-assigned key.
  • A bucket can be stored in one of several Regions. You can choose a Region to optimize for latency, minimize costs, or address regulatory requirements. Amazon S3 is currently available in the US Standard, US West (Oregon), US West (Northern California), EU (Ireland), Asia Pacific (Singapore), Asia Pacific (Tokyo), Asia Pacific (Sydney), South America (Sao Paulo), and GovCloud (US) Regions. The US Standard Region automatically routes requests to facilities in Northern Virginia or the Pacific Northwest using network maps.

"Data Warehousing" is the primary reason why developers consider Amazon Redshift over the competitors, whereas "Reliable" was stated as the key factor in picking Amazon S3.

According to the StackShare community, Amazon S3 has a broader approval, being mentioned in 3235 company stacks & 1616 developers stacks; compared to Amazon Redshift, which is listed in 270 company stacks and 68 developer stacks.

Advice on Amazon Redshift and Amazon S3

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)

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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Recommends
Airflow

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 Redshift and Amazon S3
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 Redshift
Pros of Amazon S3
  • 37
    Data Warehousing
  • 27
    Scalable
  • 16
    SQL
  • 14
    Backed by Amazon
  • 5
    Encryption
  • 1
    Cheap and reliable
  • 1
    Isolation
  • 1
    Best Cloud DW Performance
  • 1
    Fast columnar storage
  • 589
    Reliable
  • 492
    Scalable
  • 456
    Cheap
  • 328
    Simple & easy
  • 83
    Many sdks
  • 29
    Logical
  • 12
    Easy Setup
  • 11
    1000+ POPs
  • 9
    REST API
  • 5
    Secure
  • 3
    Easy
  • 2
    Web UI for uploading files
  • 2
    Plug and play
  • 1
    GDPR ready
  • 1
    Flexible
  • 1
    Faster on response
  • 1
    Plug-gable
  • 1
    Easy to use
  • 1
    Easy integration with CloudFront

Sign up to add or upvote prosMake informed product decisions

Cons of Amazon Redshift
Cons of Amazon S3
    Be the first to leave a con
    • 7
      Permissions take some time to get right
    • 6
      Takes time/work to organize buckets & folders properly
    • 5
      Requires a credit card
    • 3
      Complex to set up

    Sign up to add or upvote consMake informed product decisions

    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 Redshift?
    What companies use Amazon S3?
    See which teams inside your own company are using Amazon Redshift or Amazon S3.
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    What tools integrate with Amazon Redshift?
    What tools integrate with Amazon S3?

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    What are some alternatives to Amazon Redshift and Amazon S3?
    Google BigQuery
    Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.
    Amazon Athena
    Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
    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.
    Amazon Redshift Spectrum
    With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” -- without having to load or transform any data.
    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.
    See all alternatives