Amazon Redshift vs Amazon Redshift Spectrum

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

Amazon Redshift

+ 1
Amazon Redshift Spectrum

+ 1
Add tool

Amazon Redshift vs Amazon Redshift Spectrum: What are the differences?


Here, we will discuss the key differences between Amazon Redshift and Amazon Redshift Spectrum. Both services are offered by Amazon Web Services (AWS) and are designed to handle and analyze large datasets efficiently. However, there are distinct differences in their features and functionalities.

  1. Data Storage and Processing: In Amazon Redshift, data is stored and processed within the Redshift cluster itself. It offers a high-performance columnar data store optimized for online analytic processing (OLAP). On the other hand, Amazon Redshift Spectrum separates storage and processing. It allows users to directly query data stored in Amazon S3 using the same SQL syntax used for Redshift. This feature enables querying and analyzing data without first loading it into Redshift, offering greater flexibility and cost optimization.

  2. Cost Structure: When using Amazon Redshift, users incur costs based on the size of their cluster, regardless of the amount of data stored in it. This means that even if a cluster contains only small amounts of data, the cost is calculated based on the provisioned cluster size. In contrast, Amazon Redshift Spectrum follows a pay-per-use pricing model. Users are billed based on the amount of data scanned from S3 during query execution. This allows organizations to efficiently store and access large volumes of data without incurring unnecessary costs for idle clusters.

  3. Scaling: While both services provide scalability, they differ in their approach. With Amazon Redshift, scaling is achieved by adding more nodes to the Redshift cluster. This vertical scaling technique requires downtime during the scaling process and may cause temporary service disruptions. In contrast, Redshift Spectrum leverages the scalability of Amazon S3. As data is stored in S3, there are no capacity constraints. Users can parallelize queries across thousands of instances, providing seamless scaling without any impact on query performance.

  4. Query Performance: Amazon Redshift is optimized for high-performance OLAP workloads, with data stored on local disks of the cluster nodes. As a result, it offers faster query execution times compared to Redshift Spectrum, especially for frequently accessed and aggregated data. Redshift Spectrum, on the other hand, offloads query processing to Amazon S3, which introduces some latency due to network transfer. Although Redshift Spectrum supports the use of columnar data formats like Parquet and ORC that improve query performance, it may not match the performance of Redshift for real-time interactive queries.

  5. Complex Transformations: Amazon Redshift provides a variety of transformation capabilities, such as joins, aggregations, and complex SQL functions. Users can perform complex analytical operations directly on the data within the Redshift cluster. Redshift Spectrum, while supporting a subset of SQL functions, doesn't provide in-cluster transformations. It primarily focuses on querying the data stored in Amazon S3, which limits the complex transformations that can be performed. Complex transformation operations would require data to be loaded into Redshift for processing.

  6. Data Source: Amazon Redshift requires that the data being queried or analyzed be loaded into the Redshift cluster. It may require data loading using the COPY command or other ETL methods before it can be accessed and analyzed. In contrast, Redshift Spectrum allows querying data directly from Amazon S3. This means that data stored in different formats and sources can be queried without the need for loading or transforming it into the Redshift cluster.

In summary, Amazon Redshift is a fully managed data warehousing service optimized for high-performance OLAP workloads, providing faster query execution times. On the other hand, Amazon Redshift Spectrum offers the ability to directly query data stored in Amazon S3, providing cost optimization, flexibility, and scalability without the need for data loading into the Redshift cluster.

Advice on Amazon Redshift and Amazon Redshift Spectrum

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.

See more
Replies (3)
John Nguyen
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.

See more

Though we have always built something custom, Apache airflow ( 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.

See more

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.

See more
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Amazon Redshift
Pros of Amazon Redshift Spectrum
  • 41
    Data Warehousing
  • 27
  • 17
  • 14
    Backed by Amazon
  • 5
  • 1
    Cheap and reliable
  • 1
  • 1
    Best Cloud DW Performance
  • 1
    Fast columnar storage
  • 1
    Good Performance
  • 1
    Great Documentation
  • 1

Sign up to add or upvote prosMake 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 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.

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

Jobs that mention Amazon Redshift and Amazon Redshift Spectrum as a desired skillset
What companies use Amazon Redshift?
What companies use Amazon Redshift Spectrum?
See which teams inside your own company are using Amazon Redshift or Amazon Redshift Spectrum.
Sign up for StackShare EnterpriseLearn More

Sign up to get full access to all the companiesMake informed product decisions

What tools integrate with Amazon Redshift?
What tools integrate with Amazon Redshift Spectrum?

Sign up to get full access to all the tool integrationsMake informed product decisions

Blog Posts

Jul 9 2019 at 7:22PM

Blue Medora

DockerPostgreSQLNew Relic+8
What are some alternatives to Amazon Redshift and Amazon Redshift Spectrum?
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.
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.
Microsoft Azure
Azure is an open and flexible cloud platform that enables you to quickly build, deploy and manage applications across a global network of Microsoft-managed datacenters. You can build applications using any language, tool or framework. And you can integrate your public cloud applications with your existing IT environment.
See all alternatives