Benchmark Snowflake, BigQuery, SingleStore , Databricks, Datamart and DuckDB using TPC-H-SF10

Edit 18 May 2022: Microsoft released Datamart which has excellent performance for this type of Workload.

Another blog on my favorite topic, interactive Live BI Workload with low latency and high concurrency, but this time, hopefully with numbers to compare.

I tested only the Databases that I am familiar with, BigQuery, Snowflake, Databricks , SingleStore , PowerBI Datamart and DuckDB


The most widely used Benchmark to test BI workload is TPC-DS and TPC-H produced by the independent Organization TPC, unfortunately most of the available benchmark are for big dataset starting from 1 TB, as I said before I more interested in smaller Workload for a simple reason, after nearly 5 years of doing Business intelligence for different companies, most of the data model are really small, ( my biggest was 70 Million rows with 4 small dimension tables).

Benchmarking is a very complex process, and I am not claiming that my results are correct, all I wanted to know as a user is an order of magnitude and a benchmark can give you a high level impression of a database performance.


I Like TPC-H as it has a simpler schema 8 tables and only 22 Queries compared to TPC-DS which require 99 Queries.


Some Considerations

  • Result Cache is not counted.
  • The results are using warm cache and at least one cold run, I run the 22 Queries multiple times.
  • Databricks by default provide a sample Database TPC-SF05, the main Table Lineitem is 30 Millions rows, I don’t know enough to import the data and apply the proper sorting etc , so I preferred to use the smaller dataset. I did create a local copy by using create table as select ( Loaded SF10 Data)
  • Snowflake and SingleStore have SF10 and other scale by default.
  • BigQuery, I imported the data from Snowflake , I sorted the tables for better performance, it is a bit odd that BigQuery don’t provide such an important public dataset by default
  • Microsoft Datamart no sorting or partitioned was applied , the data was imported from Biguery.

No Results Cache

Most DWH support results cache, basically if you run the same Query and the base tables did not change the Engine will return the same results very quickly, obviously in any benchmark, you need to filter out those queries.

  • In Snowflake you can use this statement to turn the results cache off
  • In Databrick
SET use_cached_result = false
  • BigQuery, just add an option in the UI
  • SingleStore and Datamart, does not have a result cache per se, the engine just keep a copy of the Query Plan, but it scan The Data every time.

Warm Cache

Snowflake, SingleStore and Databricks leverage the local SSD cache, when you run a Query the first time, it scan the data from the cloud storage which is a slow operation, then when you run it again the Query will try to use the data already copied in the local disk which is substantially faster, specially with Snowflake if you want to keep the local warm cache it make sense to keep your Cluster running a bit longer.

BigQuery is a different beast there is no VM, the data is read straight from the Google Cloud Storage, yes google Cloud Network is famous for being very fast, but I guess it can not compete with a local SSD Disk, anyway that’s why we have BI Engine which basically cache the data in RAM, but not all Queries are supported, actually only 6 are fully accelerated as of this writing. ( see Limitations )

Query History

Getting Query results is very straightforward using information_Schema, except for databricks, it seems it is only supported using an API, I just copied one warm run and paste it to excel and load it from there.

Engine Used

  • Snowflake : X-Small (Lowest tier)
  • Databricks : 2X-Small (Lowest tier)
  • Single Store : S-0
  • BigQuery : on Demand + 1 GB Reservation of BI Engine
  • Datamart : included with PowerBI Premium, official spec not disclosed.
  • DuckDB : my laptop, 16GB RAM 🙂


The 22 Queries are saved in this repo, I am using PowerBI to combine all the results

let’s start with

Snowflake VS BigQuery

Snowflake Vs SingleStore

Snowflakes VS Databricks

Notice Databricks is using the smaller Dataset SF05, 30 million rows and still Snowflake show better performance


Edit : due to feedback, I am adding the sum of all Queries, You can download the results here

Edit : 26-Jan-2022, I Updated the results for Databricks SF10, I Uploaded the same data used for BigQuery, then created Delta Table and applied optimize Z Order

Take away

  • Snowflake is very fast and has consistent results for all the 22 Queries, Except Query 13 is a bit odd

  • SingleStore is remarkable but Query 13 is not good at all and skew the overall performance.

  • BigQuery is fantastic when BI Engine works ( only 11 Queries are supported from the total of 22)

  • Databricks performance in TPC-H-SF05 is problematic, I just hope they release a proper TPC-H-SF10 dataset and information schema like other DWH

  • Datamart has the best user experience, the only Data Platform where you can load the data without writing any Code,The same as Singlestore; Query 13 has a very Big Cost on the overall performance.

  • DuckDB : Query 9 skew the overall performance and probably I need a new laptop 🙂

First Impression of Snowflake from a BigQuery user perspective.

TL;DR : Random observations after using Snowflake for a couple of hours, there is a lot to likes but mixed feeling about the cost.

For no obvious reason, I felt an urge to try Snowflake, The setup was trivial, you get 30 days trial with $400, no Credit card required.

Snowflake is multi cloud product, first I had to choose the cloud provider and the region, My Personal PowerBI instance is in Melbourne, unfortunately as of this writing it is Only Available in Azure Sydney Region, it make sense to choose the same region for two reasons

  • Latency, inter region Transfer take more time
  • Egress Cost, Cloud Provider charge for Inter Region Transfer

For the record my personal data is in GCP Tokyo , but Snowflake is not available there.

User interface

The User interface is very neat and simplistic ( Good thing), I did not need to check any documentation

Snowflake provide free sample data by default

and Obviously, you can browser all kind of data from the Marketplace , it is very well integrated and seems trivial to use, as you have noticed already, Snowflake like BigQuery has a total separation between Storage and Compute, so far so good.

Preview Data

I click on Data Preview, and I got this message

Yes Unlike BigQuery, Data Preview is a paid operation and require a Cluster running.

Create a new Cluster

This is the core feature of Snowflake, creating a new cluster is trivial, as a test, I create the smallest possible Cluster.

The Cluster was up and running in a couple of seconds, very Impressive, and the way it works is very simple.

if there is no Query Running, it will shut down after 1 minute ( or whatever you choose), When a new Query showed up for example from a BI tool, the Engine very quickly wake up !!!!

The Minimum Cluster, I could setup was X-Small and it cost 1 credit/Hour ( 2,75 $/Hour), but you pay per second with a 1 minute minimum , I am using Standard Edition, Enterprise edition cost more.

Note : As a BigQuery enthusiast, I hope Google release the auto flex slot

Notice here, Snowflake is not simply a cluster to Run some Queries, but it does have a Service Layer which do a lot of operations behind the scene, personally I am mainly interested in free Results cache, which is freaking fast , as low as a 50 ms !!!!

Query History Log

A nice Query history log, I really liked the Client Driver, you can easily tell, if the Queries are coming from an external BI tools or Query from the Console, one very very annoying thing, if you change that view and you came back, you lose the selection and you have to select the columns again, I wish Snowflake could save the customization of the columns.

Query Console

it was not very obvious, but to select columns name, you need first to click on a table which open a panel then Click on those three little dot, (it is obvious once you know it) , there is no multi tab support, new Query open a new Window, but honestly seeing the new BigQuery UI, maybe it is not a bad idea after all 🙂


My initial plan was to use a copy a SQL Script from BigQuery that uses Loops, but turn out Snowflake don’t support DO while ( it is coming for SQL ), there is a workaround using javascript which I may use later, but instead, and just to have a first impression, I used a PowerBI report in DirectQuery mode and see how it goes.


Snowflake Driver for PowerBI is amazing, I never saw a sub second Direct Query in PowerBI before, even when returning 1 row from cache, ( BigQuery Driver for PowerBI is not optimized, I don’t really knows who to blame, Google or Microsoft or probably both, turn out it is Google responsability)

I am using TPCH_SF10 dataset, the main fact Table contains 60 Million records

And here is the Snowflake data Model ( pun intended) in PowerBI using DirectQuery.

Some results a really intriguing, The ones that don’t have Bytes scanned are cached, but look at this Query that scanned 2.1 GB and returned in 770ms, that’s a BigQuery BI Engine territory right there !!!

The Reason, the Query is in the subsecond, is again because of another type of cache, Snwoflake cache the raw data in the local SSD drive of the cluster, hence it make sense for better performance to keep the Cluster running for a bit longer, if you suspend a Cluster, there is no guarantee that Snowflake will resume the same one.

Btw, the Query plan visual is very detailed and explain every step, very nice.


The Quick Auto suspend and resume of Clusters, Global Query result cache and the fact it is a Multi Cloud offering are the key strength of Snowflake.

I was really surprised by the experience of using Snowflake Direct Query with PowerBI, and the data marketplace was a very polished experience.

I will show my bias here , for 2.75 $/Hour I can reserved a BigQuery BI Engine instance with 50 GB in-memory RAM, it will be interesting to compare the performance and concurrency of Both Engine.

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