Redshift vs S3: which is best?

Redshift vs S3: which is best?

April 11, 2023
April 11, 2023
Redshift vs S3: which is best?
In this article, we’ll compare Amazon Redshift vs. S3 and cover their key features. We’ll also explain why you need a data pipeline tool like Fivetran that can effortlessly work with both. 

An organization’s choice of data storage solution impacts the performance of its data pipelines. It can slow down data query processing and leave users hanging when they need answers.

Amazon Redshift and Amazon Simple Storage Solutions (S3) are common data storage choices for individuals and businesses. Some choose one over the other, while others use both in tandem.

In this article, we’ll compare Amazon Redshift vs. S3 and cover their key features. We’ll also explain why you need a data pipeline tool like Fivetran that can effortlessly work with both. 

What is Amazon Redshift? 

Amazon Redshift is a fully managed cloud data warehouse where users can store and analyze their data. Users can collect structured and semi-structured data from data streams, transactional databases, application logs and more for analysis.

The platform uses a cluster-based architecture consisting of a set of nodes, each with CPU, storage and RAM. A lead node ingests and delegates queries to compute nodes, which then process the results. Since Redshift is a fully managed service, users don’t need to worry about backend monitoring and maintenance. Instead, organizations can scale the number of clusters they use.

Here are a few of Amazon Redshift’s key features:

  • Redshift allows analysts to process massive datasets via parallel processing and distributed design strategy.
  • Redshift enables business analysis using in-built machine learning services and other Amazon tools like QuickSight.
  • Redshift integrates with Apache Spark, enabling data teams to run more analysis applications on their data warehouse.
  • The platform supports a range of advanced analytics processing, including spatial data processing, HyperLogLog sketches and semi-structured data processing.

When it comes to data integration storage options, Amazon Redshift is comparable to a cloud data warehouse. The data within it must be structured in a predefined format. While Redshift is a fully-managed platform, organizations must still monitor and scale the infrastructure when needed.

To address this issue, Amazon recently launched Redshift Serverless, which automatically scales data warehouse capacity and aims to streamline real-time data analysis, collaborate on data and build reporting and dashboarding applications. Redshift integrates with data integration tools like Fivetran to speed up ingestion and loading. Fivetran connectors automatically add Redshift’s primary and foreign keys to enable quicker operations and analysis.

What is Amazon S3?

Amazon Simple Storage Solutions (S3) is a fast, inexpensive and scalable cloud data storage infrastructure. It stores data as objects within systems called “buckets.” Users can store all types of data on S3, including videos, pictures and log files, along with information from applications and anywhere else on the web.

Amazon S3 allows users to monitor data, enforce data access controls and run big data analytics. It has the following key features:

  • Amazon S3 can store unlimited objects. Each object can be up to 5 terabytes in size.
  • Amazon S3 has a range of features for storage management, including bucket names, prefixes and object tags. These functions allow users to perform batch operations and replication for improved organization and reporting.
  • Amazon S3 hosts flexible security options to prevent unauthorized data access.
  • Amazon S3 can also analyze stored data. The S3 Storage Lens, for example, provides insights on activity trends, storage use and recommendations for improved data security.

These features and more can be easily accessed and managed using their web interface.

In terms of storage types used for data integration, Amazon S3 is similar to a data lake — a centralized repository to store structured and unstructured data. The platform also offers APIs and can be integrated with Amazon Web Services (AWS) and other software processing tools to enable additional capabilities.

For example, you can sync files from your S3 bucket to a destination using dynamic data integration solutions like Fivetran. Like a data lake, you can use Amazon S3 to decouple storage from data processes and computing. This means organizations can prevent data duplication and data silos, save money and gain greater flexibility during scaling.

Redshift vs S3: Four key differences

Deciding whether your team needs Redshift or S3 (or both) can be a tough choice. But it all depends on your use cases and budget. Let’s compare S3 vs. Redshift to see which best fulfills your requirements.

Redshift and S3 differ in four key ways.


The first big difference is that Redshift is mainly used for structured data, while S3 can ingest structured, semi-structured and unstructured data. RedShift is comparable to a cloud data warehouse. It also has in-built tools to deliver real-time and predictive analysis. In contrast, S3 is primarily a storage platform that’s similar to a data lake. Businesses can use it as a destination at the end of their data pipeline. Data teams then use third-party artificial intelligence (AI), analytics and machine learning (ML) tools to analyze this data.

Data storage category

Redshift is a columnar database and data warehouse ideal for online analytical processing (OLAP). Columnar storage enables faster data aggregation as it groups all the values from a particular data field into a single block. Consequently, analysts can run complex queries at a quicker pace. S3 is an object storage solution. It’s ideal for storing data from all kinds of sources and then letting analysts run data transformations and perform analysis as needed. This storage type is typically used in Extract, Transform, Load (ELT) data pipelines.

Use cases

Since the data within Amazon Redshift is already structured, data teams can get faster access, rapid insights and fresh forecasts. They can directly feed data from Redshift into business intelligence tools. Amazon S3 is used by organizations to consolidate large volumes of data of different formats in one repository. The data team can then use analytic tools to gain insights. 

Companies using a modern data stack prefer to use a data lake over a warehouse for three key reasons:

  • Data lakes can handle unstructured data, while warehouses struggle with this. In the current market, many data types are unstructured, like videos, images and raw text. The ability to store and integrate this data is vital; a warehouse cannot be used for this purpose.
  • Data lakes are more flexible and affordable. Businesses that collect high volumes of images or text rely on this flexibility to create productive data pipelines. The increased flexibility enables more thorough big data analytics, where teams can identify hidden patterns and correlations in large data volumes.
  • Since lakes can easily store high volumes of data, analysts can use historical data to predict future trends and events. Lakes are also essential for accommodating data types that are vital for training/validation sets for AI/ML models.


Amazon Redshift uses an hourly payment model that starts at $0.25 per hour. Businesses can choose between three node types — RA3, Redshift Managed Storage (RMS) and DC2. The pricing depends on the type of node chosen and the number of nodes in your cluster.

Amazon S3 is an affordable storage option where users only pay for what they need. There’s no minimum charge. Many data lake providers follow this pricing model and are cheaper for companies with a high volume of diverse data. In most cases, data lakes can be cheaper for companies and are often used with data warehouses.

Why Fivetran is the perfect data pipeline for both data storage solutions

Whether you use Amazon Redshift or Amazon S3 for your data storage, Fivetran has the features to accommodate both platforms. It can help data analysts and developers create no-code pipelines for each of them in minutes.

Amazon Redshift and Fivetran

Users can link Redshift with Fivetran using an SSH tunnel or via PrivateLink. Once connected, data from your data pipeline will be transformed and loaded into your Redshift warehouse. It’s best to connect Fivetran as a Master user with CREATE permissions, but organizations can also connect as a limited user using a SQL client tool.

The ability to seamlessly integrate both types of storage into your data pipeline in minutes is a critical advantage that Fivetran offers. Using data connectors, Fivetran can significantly speed up your data integration, enabling faster, more accurate insights.

Amazon S3 and Fivetran

Users can link Fivetran with an S3 bucket using connectors to automatically load data and send it to their destination, like an analytics or business intelligence tool. Once Fivetran has access to your bucket, analysts can choose which subsets of folders and types of files to only sync the data they need. You can also set up multiple connectors with different configurations for the same bucket to enable better analysis.


Amazon Redshift is excellent for storing and analyzing high volumes of structured data, while Amazon S3 is the perfect storage solution for modern data stacks that ingest diverse data types. Both platforms serve their own purposes. Using Fivetran, organizations can effortlessly integrate one or both storage solutions into their data pipeline to enable rapid data ingestion, processing and analytics. Understand how our platform can elevate data integration for your company by starting a 14-day free trial today.

IDC reveals an average three-year ROI of 459% and $1.5 million in average annual benefits for Fivetran customers.
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