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GeezerVison is showing github.com — GitHub - fivetran/benchmark: Benchmark data warehouses under Fivetran-like conditions

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microsoft_sql
microsoft_sql
 
 
query
query
 
 
viz
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.gitignore
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001-LaunchDataproc.sh
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002-GenerateData.sh
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003-GenerateGs.sh
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004-CopyToS3.sh
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006-LaunchPresto.sh
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007-ConnectPresto.sh
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008-ConnectToEc2Instance.sh
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200-PopulateRedshift.sh
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201-BenchmarkRedshift.sh
201-BenchmarkRedshift.sh
 
 
202-RedshiftTiming.sh
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300-PopulateSnowflake.sh
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301-BenchmarkSnowflake.sh
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302-SnowflakeTiming.sql
302-SnowflakeTiming.sql
 
 
400-PopulateBigQuery.sh
400-PopulateBigQuery.sh
 
 
401-BenchmarkBigQuery.sh
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500-BenchmarkAzure.sh
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500-PopulateAzure.sql
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502-AzureTiming.sql
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600-GenPopulateDatabricks.js
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601-PopulateDatabricks.sql
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602-BenchmarkDatabricks.sh
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AzureQueryRunner.sh
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ForwardHttp.sh
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ForwardProfiler.sh
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MicrosoftTools.sh
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README.md
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Warmup.sql
Warmup.sql
 
 

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Results

https://fivetran.com/blog/warehouse-benchmark

Design

This is based on the TPC-DS benchmark, a standard data warehouse benchmark that uses lots of joins, aggregations and subqueries. The TPC-DS queries have been modified somewhat to improve portability across implementations, and eliminate the use of obscure SQL features like grouping-sets. We generated 1 TB of data, which contains about 4 billion rows in the largest fact table. We used the following warehouse configurations:

Configuration Cost / Hour
Redshift 5x ra3.4xlarge $16.30
Snowflake Large $16.00
Presto 4x n2-highmem-32 $8.02
BigQuery Flat-rate 500 slots $13.70

Usage

These scripts are intended to be manually copy-pasted into various terminals. You can skip steps 1-4 since gs://fivetran-benchmark and s3://fivetran-benchmark are already populated.

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Benchmark data warehouses under Fivetran-like conditions

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