Concurrencylabs
concurrencylabs.com › blog › starburst-presto-vs-aws-redshift
Querying 8.66 Billion Records - a Performance and Cost Comparison between Starburst Presto and Redshift - Concurrency Labs
In my experience, launching a cluster for the first time is a bit easier in Redshift. After that, re-launching and resizing clusters is significantly easier using Starburst Presto on EC2. Since these clusters are expensive to run 24 / 7, re-launching and resizing will likely be a common task (more on that in the Cost Comparison section below) The TPC-H benchmark consists of a standard dataset and 22 queries that are executed sequentially against this dataset.
Medium
altinitydb.medium.com › clickhouse-vs-amazon-redshift-benchmark-e223429f4f95
ClickHouse® vs Amazon RedShift Benchmark | by AltinityDB | Medium
November 13, 2024 - The result showed that ClickHouse performing much faster with that optimization than it was before. The query time dropped down from 8 to 1.5 second. ... Our benchmarks show that dedicated servers are still significantly faster than Amazon instances for analytic DBMS workloads. ClickHouse is significantly faster than RedShift in most scenarios, in some cases ClickHouse queries require optimizations.
Redshift on new M4's?
I have been a Mac user for 20+ years and love working on Redshift. There’s a bit of false info in some of these comments. Apple Silicon does do GPU rendering, not CPU rendering. Apple’s CUDA equivalent is Metal, and Redshift now natively supports this. The M3 series saw a big improvement in Redshift benchmark scores, the M1 was terrible - so you may be pleasantly surprised. Nobody has posted M4 scores yet, so it’s hard to say how they compare - but the M3 Max was somewhere around a 3070-3080 ( Cinebench scores - scroll to GPU results). So it’s not the greatest, but it’s a laptop. Personally speaking, I love being able to get these sorts of results when working on a portable computer, but when I need serious power I’m still going to be sending to an NVidia rig. So what am I saying? Like others here - it sounds like you need a render rig, regardless of what computers you actually do the work on. I think you should sell it in to management as a separate server - especially given they seem weirdly hell bent on dictating what hardware you use day to day. Something like this might scare them , in terms of budget, but you could always build something yourself for less. More on reddit.com
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November 2, 2024Yet another benchmark report: We benchmarked 5 data warehouses and open-sourced it
Why doesn’t it show DDL for any of the tables which would wildly affect the outcome? Or am I just not seeing it More on reddit.com
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July 18, 2025Flawed Redshift Pricing Comparisons
Redshift is almost always cheaper than snowflake even with auto suspend. Redshift is so under rated these days. More on reddit.com
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May 4, 2024Databricks vs Redshift
TLDR; Redshift is an MPP database whereas Databricks is a unified Data Platform. Along with Redshift you need different AWS services to achieve the same what you can do in Databricks. Your ideal comparison should be Redshift vs Databricks SQL Warehouse. More on reddit.com
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July 7, 2023Videos
01:17
Powerful Redshift Render Farm | Redshift 3.0.50 Benchmark 2 x RTX3090 ...
01:21
Powerful Redshift Render Farm | Redshift Benchmark 8 x RTX 3090 ...
10:29
The GPU Benchmark Nobody's Talking About - Octane & Redshift - ...
10:11
Cinema 4D + RedShift Benchmark On M1 Max MacBook Pro 16" - YouTube
04:34
RTX 4090 & 3090 Multi-GPU Performance Test for C4D & Redshift ...
Test GPU Performance for Cinema 4D & Redshift on 1/2/4/6/8 ...
AWS re:Post
repost.aws › articles › ARrTNWDZAhSdukf4lmIX9aYg › amazon-redshift-advanced-benchmarking
Amazon Redshift Advanced Benchmarking | AWS re:Post
January 16, 2024 - AWS Amazon Redshift excels in executing the derived queries from the popular TPC-DS benchmark. AWS has the data, DDL and queries needed to perform this benchmark in GitHub so it is easy to test this for yourself.
AWS
aws.amazon.com › blogs › big-data › run-a-popular-benchmark-on-amazon-redshift-serverless-easily-with-aws-data-exchange
Run a popular benchmark on Amazon Redshift Serverless easily with AWS Data Exchange | Amazon Web Services
January 9, 2023 - This feature empowers customers to quickly query, analyze, and build applications with these third-party datasets. TPC-DS is a commonly used benchmark for measuring the query performance of data warehouse solutions such as Amazon Redshift.
Integrate.io
integrate.io › home › blog › big data › benchmarking the performance of amazon redshift ra3.16xlarge versus ds2.8xlarge instances
Benchmarking the Performance of Amazon Redshift ra3.16xlarge versus ds2.8xlarge instances | Integrate.io
February 2, 2026 - On paper, the ra3.16xlarge nodes are around 1.5 times larger than ds2.8xlarge nodes in terms of CPU and Memory, 2.5 times larger in terms of I/O performance, and 4 times larger in terms of storage capacity: On to the tests! A reported improvement for the RA3 instance type is a bigger pipe for moving data into and out of Redshift.
Satori
satoricyber.com › home › aws redshift benchmark: testing and optimizing query performance
AWS Redshift Benchmark: Testing and Optimizing Query Performance
April 22, 2023 - This article will briefly review Redshift’s high performance architecture, show you how to build a reliable benchmark test for your Redshift workloads, and provide a few techniques for tuning Redshift performance.
GitHub
github.com › aws-samples › redshift-benchmarks
GitHub - aws-samples/redshift-benchmarks · GitHub
load-tpc-ds - Test the performance of Redshift Serverless with the popular TPC-DS derived benchmark. This benchmark loads the data from an S3 bucket and run n number of concurrent queries using Amazon Redshift rsql.
Author aws-samples
AWS
aws.amazon.com › blogs › big-data › building-high-quality-benchmark-tests-for-amazon-redshift-using-sqlworkbench-and-psql
Building high-quality benchmark tests for Amazon Redshift using SQLWorkbench and psql | Amazon Web Services
December 14, 2020 - Data architects and engineers have observed the Amazon Redshift cluster’s average CPU utilization steadily increase, and now wish to scale up the cluster before onboarding additional data, ETL jobs, and users waiting in the project pipeline. To determine the optimal cluster size, we perform a few simple benchmark tests on different cluster configurations.
AWS
aws.amazon.com › blogs › big-data › amazon-redshift-out-of-the-box-performance-innovations-for-data-lake-queries
Amazon Redshift out-of-the-box performance innovations for data lake queries | Amazon Web Services
August 5, 2025 - With Amazon Redshift patch 190, the TPC-DS 3TB benchmark showed an overall 2x query performance improvement on Apache Iceberg tables without statistics, including TPC-DS Query #72, which improved by 125 times from 690 seconds to 5.5 seconds.
CGDirector
cgdirector.com › home › benchmarks › redshift benchmark results (updated)
Redshift Benchmark Results (Updated) - CG Director
April 11, 2024 - Extensive Redshift Benchmark Results List with all modern GPUs, Operating Systems, CPUs and Multi-GPU-Setups. Find the best performing GPU for Redshift.
Maxon
help.maxon.net › r3d › maya › en-us › Content › html › The+redshiftBenchmark+tool.html
The redshiftBenchmark tool
By default, the Redshift Benchmark uses a block size (aka "bucket size" : see System - Advanced) of 128x128 pixels. To use different bucket sizes, use the "blocksize" parameter. Below is an example using 256x256 pixel buckets.
AWS
docs.aws.amazon.com › amazon redshift › management guide › amazon redshift provisioned clusters › monitoring amazon redshift cluster performance › viewing performance data › viewing cluster performance data
Viewing cluster performance data - Amazon Redshift
You can benchmark the data on this chart to measure I/O performance per WLM queue and tune its most time-consuming queries if necessary. Query throughput per WLM queue – Shows the average number of completed queries per second. You can analyze data on this chart to measure database performance per WLM queue. Concurrency scaling activity – Shows the number of active concurrency scaling clusters. When concurrency scaling is enabled, Amazon Redshift ...
Jsaer
jsaer.com › download › vol-11-iss-11-2024 › JSAER2024-11-11-10-14.pdf pdf
Available online www.jsaer.com Journal of Scientific and Engineering Research
November 11, 2024 - Finally, complex analytical queries employing window functions, subqueries, and GROUP BY clauses were executed to evaluate each platform's capabilities in handling intricate ... For datasets up to 100 GB, both Redshift and BigQuery exhibited excellent performance for simple queries,
AWS
aws.amazon.com › blogs › big-data › amazon-redshift-lower-price-higher-performance
Amazon Redshift: Lower price, higher performance | Amazon Web Services
October 26, 2023 - All queries used in the benchmark are available in our GitHub repository and performance is measured by launching a data warehouse, enabling Concurrency Scaling on Amazon Redshift (or the corresponding auto scaling feature on other warehouses), loading the data out of the box (no manual tuning or database-specific setup), and then running a concurrent stream of queries at concurrencies from 1–200 in steps of 32 on each data warehouse.
AWS
aws.amazon.com › blogs › big-data › building-high-quality-benchmark-tests-for-redshift-using-open-source-tools-best-practices
Building high-quality benchmark tests for Redshift using open-source tools: Best practices | Amazon Web Services
October 6, 2020 - It’s strongly recommended that you conduct at least four iterations (one warm-up iteration and three subsequent iterations) for each test for statistical confidence in the benchmark results. The warm-up iteration is intended to prime the Amazon Redshift cluster just as a real-world cluster would be. For example, Amazon Redshift compiles all queries to machine code to achieve the fastest query performance.
Oracle
oracle.com › ai database › heatwave
HeatWave Performance Benchmark | Oracle
Discover how HeatWave MySQL on OCI outperforms Snowflake, Amazon Redshift, Amazon Aurora, and Amazon RDS in query processing and machine learning benchmarks.
Puget Systems
pugetsystems.com › home › hardware articles › redshift: nvidia geforce rtx 40 series performance
Redshift: NVIDIA GeForce RTX 40 Series Performance | Puget Systems
November 15, 2023 - The top performer, the RTX 4090 24GB, is 71% faster in Redshift compared to the previous generation’s RTX 3090. The RTX 4080 16GB has a smaller, but still welcomed 44% improvement in speed when compared to the similarly priced RTX 3080 Ti.
OpenBenchmarking.org
openbenchmarking.org › test › pts › redshift
RedShift Demo Benchmark - OpenBenchmarking.org
September 12, 2020 - pts/redshift-1.0.0 [View Source] Sat, 12 Sep 2020 12:05:24 GMT Initial commit of Maxon RedShift demo benchmark. ... OpenBenchmarking.org metrics for this test profile configuration based on 307 public results since 12 September 2020 with the latest data as of 20 September 2025. Below is an overview of the generalized performance for components where there is sufficient statistically significant data based upon user-uploaded results.
Reddit
reddit.com › r/redshiftrenderer › redshift on new m4's?
r/RedshiftRenderer on Reddit: Redshift on new M4's?
November 2, 2024 -
Long story short, at job we render on M1 Max 64GB MBP. It's slow and unsustainable for final rendering sequences and the turnaround time we need.
I've been pushing them to look into getting a Windows build with RTX4090's if they want to see a real, tangible difference in render times and get the most out of Redshift, since it's Cuda based and Apple Silicon isn't.
They were open to pricing one out until the new M4's were announced. Now higher ups just want to go with the new M4's because "Mac is what we've always used".
If we get them, we're stuck with them for a while.
Will the M4 be comparable to a typical Windows+NVIDIA RTX build for Redshift when rendering out final image sequences?
The M1 Max's have been awful in terms final frame render time, and ends up taking way too long to render sequences for the turnaround time we need in order to work efficiently.
I'm resistant to continue in the Mac ecosystem for rendering out of Redshift. Apple Silicon is great for AE, Editing, and Photoshop, but GPU rendering is it's kryptonite.
Will the M4's be trash compared to a proper Windows build? Or will they be better? If they are at least equivalent to a proper windows build, great. If not, seems like a waste of money/time.
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Keep pushing for the pc rig. Work on the scene on the macbook and jump in with parsec on the render rig. Argue about upgradability. You can easily add a second GPU in the future. You can also use your macbook longer since it does not need to be as powerful or big. Multiple artists can push renders to the pc from their mobile workstations. This is the way
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I have been a Mac user for 20+ years and love working on Redshift. There’s a bit of false info in some of these comments. Apple Silicon does do GPU rendering, not CPU rendering. Apple’s CUDA equivalent is Metal, and Redshift now natively supports this. The M3 series saw a big improvement in Redshift benchmark scores, the M1 was terrible - so you may be pleasantly surprised. Nobody has posted M4 scores yet, so it’s hard to say how they compare - but the M3 Max was somewhere around a 3070-3080 ( Cinebench scores - scroll to GPU results). So it’s not the greatest, but it’s a laptop. Personally speaking, I love being able to get these sorts of results when working on a portable computer, but when I need serious power I’m still going to be sending to an NVidia rig. So what am I saying? Like others here - it sounds like you need a render rig, regardless of what computers you actually do the work on. I think you should sell it in to management as a separate server - especially given they seem weirdly hell bent on dictating what hardware you use day to day. Something like this might scare them , in terms of budget, but you could always build something yourself for less.
Fivetran
fivetran.com › blog › warehouse-benchmark
Cloud Data Warehouse Benchmark | Blog | Fivetran
November 26, 2025 - Our newest benchmark compares price, performance and differentiated features for Redshift, Snowflake, BigQuery, Databricks and Synapse.