Platform Comparison Matrix

This matrix compares BenchBox platform families and links exact platform counts back to runtime registry metadata.

Platform Overview

BenchBox supports platforms across registry categories and editorial documentation groups. Exact counts below are generated from PlatformRegistry.get_platform_count_summary() and checked by unit tests; the later sections remain human-oriented groupings for choosing a platform.

Platform registry: 52 metadata entries; 48 SQL-capable; 18 DataFrame-capable; 14 dual-mode; support status counts: stable=5, beta=28, experimental=18, deprecated=1.

Registry category	Platforms	Notes
analytical	8	Local and engine-native analytical platforms
cloud	23	Managed warehouses, cloud Spark, and SaaS surfaces
dataframe	5	DataFrame-only metadata entries
distributed	6	Federated, cluster, and distributed SQL engines
embedded	1	Built-in embedded runtime
olap	4	PostgreSQL extension, embedded OLAP, and DuckLake lakehouse variants
relational	1	General relational database support
timeseries	3	Time-series engines with SQL/query support

Note

CLI Naming Convention: DataFrame platforms use the -df suffix (e.g., polars-df, pandas-df) to distinguish them from SQL mode platforms. Some platforms like DataFusion support both modes - see Hybrid Platforms for details.

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Quick Decision Guide

1. Local development or testing?
   └── Yes → DuckDB (default) or DataFusion (Arrow-native)
   └── No → Continue to #2

2. Need DataFrame API (not SQL)?
   └── Yes → Polars-df (single-node) or PySpark-df (distributed)
   └── No → Continue to #3

3. Already using Snowflake and want DataFrame API?
   └── Yes → snowpark-connect (PySpark-compatible)
   └── No → Continue to #4

4. Cost is primary concern?
   └── Yes → DuckDB, ClickHouse, or PostgreSQL (self-hosted)
   └── No → Continue to #5

5. Data > 100GB?
   └── No → DuckDB, ClickHouse, or DataFusion
   └── Yes → Continue to #6

6. Which cloud provider?
   └── AWS → Redshift, Athena, or EMR Serverless
   └── GCP → BigQuery or Dataproc Serverless
   └── Azure → Azure Synapse Analytics, Microsoft Fabric Warehouse, or Microsoft Fabric Spark
   └── Multi-cloud → Snowflake or Databricks

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Deployment Modes

BenchBox supports multiple deployment modes for platforms, enabling the same benchmark to run against local, self-hosted, and cloud-managed instances. Use the colon syntax to specify deployment modes: --platform platform:mode.

Platforms with Multiple Deployment Modes

Platform	Default Mode	Available Modes	Syntax Examples
ClickHouse	local	local, server	clickhouse-local, clickhouse-server
Firebolt	core	core, cloud	firebolt:core, firebolt:cloud
TimescaleDB	self-hosted	self-hosted, cloud	timescaledb, timescaledb:cloud
PySpark	local	local	pyspark
LakeSail	local	local, distributed	lakesail (+ lakesail-df for DataFrame mode)
Velox	local	local, remote	velox:local, velox:remote
DuckLake	local	local, postgres_catalog, cloud_storage	ducklake with --platform-option catalog=...

First-Class Cloud Platforms (Dialect Inheritance)

Platform	Inherits Dialect From	CLI Name	Notes
ClickHouse Cloud	ClickHouse	clickhouse-cloud	Managed ClickHouse service
MotherDuck	DuckDB	motherduck	Serverless DuckDB cloud
DuckLake	DuckDB	ducklake	Local DuckDB lakehouse (Parquet + SQL catalog)
Starburst	Trino	starburst	Starburst Galaxy managed Trino

Deployment Mode Characteristics

Mode	Credentials	Network	Cloud Storage	Examples
local	No	No	No	DuckDB, chDB, Firebolt Core
self-hosted	Yes	Yes	No	ClickHouse Server, Trino, TimescaleDB, SingleStore
managed	Yes	Yes	Sometimes	MotherDuck, ClickHouse Cloud, Starburst Galaxy

Note

For detailed deployment mode configuration, see Deployment Modes Guide.

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Local/Embedded Platforms

Zero-infrastructure platforms that run in-process. Ideal for development, testing, and medium-scale analytics.

Architecture Comparison

Feature	DuckDB	DataFusion	SQLite	Polars	ClickHouse
CLI Name	duckdb	datafusion	sqlite	polars-df	clickhouse-local
Architecture	Embedded Columnar	In-memory Columnar	Embedded Row-based	In-memory Columnar	Embedded/Server Columnar
Storage Format	Native/Parquet	Arrow/Parquet	B-tree Pages	Arrow/Parquet	MergeTree
Query Engine	Vectorized	Vectorized (Rust)	B-tree	Vectorized (Rust)	Vectorized
Concurrency	Read-heavy	In-memory Only	Limited Write	Read-heavy	High Concurrent
ACID Support	Limited	None	Full	None	Limited
SQL Support	Full	Full	Full	DataFrame only	Full

Note

ClickHouse Platforms: ClickHouse is available as three first-class platforms: clickhouse-local (chDB, zero-config), clickhouse-server (self-hosted), and clickhouse-cloud (managed). The legacy bare clickhouse selector has been removed and now raises an error naming these replacements. See Migration Guide.

Relative Performance Characteristics

Performance varies based on workload, query complexity, data size, and hardware. The following are general architectural characteristics:

Feature	DuckDB	DataFusion	SQLite	Polars	ClickHouse
Storage Type	Columnar	In-memory Arrow	Row	In-memory Arrow	Columnar
Parallelism	Multi-threaded	Multi-threaded	Single	Multi-threaded	Multi-threaded
Data Loading	CSV/Parquet	Parquet/CSV	CSV	Parquet	Multiple
Memory Model	Disk + RAM	In-memory	Disk	In-memory	Configurable
Max Recommended SF	100	50+	1	100	100+

Use Cases

Use Case	Recommended	Alternatives
Development/Testing	DuckDB	DataFusion, SQLite
CI/CD Validation	DuckDB, SQLite	DataFusion
PyArrow Workflows	DataFusion	DuckDB
Research/Academia	DuckDB	ClickHouse
Cost-Sensitive	DuckDB	ClickHouse
DataFrame API Preferred	Polars (-df)	PySpark, DataFusion

Installation

uv add duckdb

uv add datafusion

uv add polars

uv add benchbox --extra clickhouse-local

uv add benchbox --extra clickhouse-server

In order, these commands install:

• DuckDB, the default, which is included with BenchBox.
• DataFusion.
• Polars, for the DataFrame API only. Use the polars-df platform.
• ClickHouse Local (chDB), which needs no configuration.
• ClickHouse Server, which is self-hosted and requires a running instance.

SQLite is built into Python and needs no installation.

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Cloud Data Warehouses

Managed cloud platforms with enterprise features, auto-scaling, and petabyte-scale capacity.

Note

TimescaleDB also supports a managed cloud deployment mode via TigerData (timescaledb:cloud). It is shown separately below because it is a managed PostgreSQL time-series service rather than an MPP warehouse.

Architecture Comparison

Feature	BigQuery	Snowflake	Databricks	Redshift	Synapse	Fabric DW	Athena	Firebolt
CLI Name	bigquery	snowflake	databricks	redshift	synapse	fabric-dw	athena	firebolt
Cloud	GCP	Multi-cloud	Multi-cloud	AWS	Azure	Azure	AWS	Multi-cloud
Architecture	Columnar MPP	Columnar MPP	Columnar MPP (Spark)	Columnar MPP	Columnar MPP	Columnar MPP	Columnar MPP (Trino)	Columnar MPP (Trino)
Storage	Capacitor	Micro-partitions	Delta Lake	Columnar Blocks	Distributed	OneLake/Delta	S3/Parquet	Proprietary
Compute Model	Serverless	Virtual Warehouses	Clusters/Serverless	Provisioned/Serverless	Dedicated Pools	Capacity Units	Serverless	Engines

Managed PostgreSQL / Time-Series Service

Platform	CLI Name	Protocol	Authentication	SSL	Storage	Managed
TimescaleDB / TigerData	timescaledb:cloud	PostgreSQL	Password	Required	TigerData managed PostgreSQL storage	Yes

Cost Analysis

Pricing as of January 2026. Verify current rates with providers.

Platform	Compute Pricing	Storage Pricing	Data Transfer	Free Tier
BigQuery	$6.25/TiB queried	$20/TB/month	$0.12/GB	1TB/month queries
Snowflake	$2-40/credit/hour	Included in compute	$0.023/GB	30-day trial
Databricks	$0.15-0.65/DBU	Cloud storage	$0.087/GB	14-day trial
Redshift	$0.25-16/hour	$0.024/GB SSD	$0.02/GB	2-month trial
Synapse	$1.33+/DWU/hour	$0.023/GB	$0.05/GB	30-day trial
Fabric DW	Capacity Units	Included	Included	60-day trial
Athena	$5/TB scanned	S3 pricing	S3 pricing	None
Firebolt	Per-engine pricing	Included	Included	Free local mode
TigerData	Service tiers	Included	Included	Free trial tiers

Cost estimate context: Monthly costs assume SF=10 workload (~10GB), 100 queries/day, standard configurations. Actual costs vary significantly based on query complexity, concurrency, and data volume.

Security & Compliance

Feature	BigQuery	Snowflake	Databricks	Redshift	Synapse	Fabric DW	Athena	Firebolt
Encryption at Rest	Default	Default	Default	Default	Default	Default	Default	Default
Encryption in Transit	HTTPS	TLS	HTTPS	TLS	TLS	TLS	HTTPS	TLS
Authentication	IAM/OAuth	Users/SSO	Users/SSO	IAM/Users	Entra ID	Entra ID	IAM	Users/SSO
SOC 2	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
HIPAA	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Contact
FedRAMP	Yes	Yes	Yes	Yes	Yes	Pending	Yes	No

TigerData (timescaledb:cloud) security profile:

• Encryption at rest: Default
• Encryption in transit: TLS (SSL required by adapter)
• Authentication: Username/password (TIGERDATA_* primary, TIMESCALE_* fallback)

Installation

uv add google-cloud-bigquery google-cloud-storage

uv add snowflake-connector-python

uv add databricks-sql-connector

uv add redshift-connector boto3

uv add pyodbc azure-storage-blob azure-identity

uv add pyodbc azure-identity azure-storage-file-datalake

uv add pyathena boto3

uv add firebolt-sdk

In order, these commands install the drivers for BigQuery, Snowflake, Databricks (SQL mode), Redshift, Azure Synapse Analytics, Fabric Warehouse, Amazon Athena, and Firebolt.

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Distributed SQL Engines

Federated and distributed query engines for multi-source analytics.

Architecture Comparison

Feature	Trino	PrestoDB	Spark SQL	ClickHouse	SingleStore
CLI Name	trino	presto	spark	clickhouse-server	singlestore
Architecture	Distributed MPP	Distributed MPP	Distributed (Spark)	Distributed Columnar	Distributed Columnar
Query Engine	Vectorized	Vectorized	Spark Catalyst	Vectorized	Vectorized + Code Gen
Federation	Native	Native	Via connectors	Limited	Limited
Concurrency	High	High	Job-based	Very High	Very High
Execution Model	Interactive MPP	Interactive MPP	Batch-oriented	Interactive	Interactive

Architectural Characteristics

Feature	Trino	PrestoDB	Spark SQL	ClickHouse	SingleStore
Execution Model	Interactive MPP	Interactive MPP	Batch/micro-batch	Interactive columnar	Interactive HTAP
Optimization	Cost-based	Cost-based	Catalyst optimizer	Columnar vectorized	Cost-based + code gen
Max Scale Factor	1000+	1000+	10000+	1000+	1000+
Memory Model	Distributed	Distributed	Distributed	Distributed	Distributed
Typical Use Case	Ad-hoc federation	Ad-hoc federation	Large-scale batch ETL	Analytical queries	Real-time analytics/HTAP

Use Cases

Use Case	Recommended	Notes
Data Lake Analytics	Trino, Spark SQL	S3/GCS/ADLS support
Multi-source Federation	Trino, PrestoDB	Native connector ecosystem
Real-time Analytics	ClickHouse	SingleStore, Firebolt
HTAP (Analytics + OLTP)	SingleStore	TimescaleDB
Batch ETL	Spark SQL	Native Spark integration
Starburst Enterprise	Trino	Commercial Trino distribution

Installation

uv add trino

uv add presto-python-client

uv add pyspark

uv add benchbox --extra clickhouse-server

uv add singlestoredb

In order, these commands install the drivers for Trino, PrestoDB, Apache Spark SQL, ClickHouse (server mode, which requires a running instance), and SingleStore.

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Spark-Compatible Engines

Native-code engines that accept Spark SQL / DataFrame workloads without running Apache Spark’s JVM execution path. Both speak the Spark Connect protocol, so the standard pyspark client is the only client library required.

Architecture Comparison

Feature	LakeSail Sail	Apache Gluten + Velox
CLI Name	lakesail (+ lakesail-df)	velox
Engine	Rust (DataFusion core)	C++ (Velox) offloaded from Spark
Spark relationship	Drop-in replacement - no JVM	Plugin - retains JVM scheduler
Execution Mode	SQL and DataFrame	SQL (Spark SQL dialect)
Protocol	Spark Connect	Spark Connect (remote) / in-process SparkSession (local)
Platform Support	Linux, macOS	Linux only (Docker on macOS/Windows)
Fallback Behavior	Native-only (no JVM path)	Unsupported operators fall back to JVM
Headline Speedup	~4x faster than Spark, TPC-H SF100	~3.3x faster than Spark, TPC-H (Gluten community)

Deployment Options

Feature	LakeSail Sail	Apache Gluten + Velox
Local	Single-node multi-threaded server	In-process SparkSession with Gluten jar
Distributed	Multi-worker Rust cluster	Not applicable (single-host native)
Remote	Spark Connect endpoint	Pre-started Gluten-enabled Spark Connect server
Docker Image	Community images	benchbox-velox (linux/amd64 by default; custom arm64 jar required for native arm64)

Use Cases

Use Case	Recommended	Notes
Drop-in Spark replacement (no JVM)	LakeSail	Rust engine, zero-rewrite migration
Accelerate existing Spark deployments	Velox	Keep Spark scheduler; offload operators
Benchmark DataFrame + SQL on the same engine	LakeSail	lakesail (SQL) and lakesail-df (DF) share engine
Linux-only native tuning	Velox	Requires Gluten bundle jar
macOS / Windows development	LakeSail	Runs natively; Velox requires Docker

Installation

uv add benchbox --extra lakesail
uv add pysail
python -m pysail spark server --port 50051
benchbox run --platform lakesail --benchmark tpch --scale 1.0

uv add benchbox --extra velox
benchbox run --platform velox --benchmark tpch --scale 0.1 \
    --platform-option gluten_jar_path=/opt/gluten-velox-bundle-spark4.0_2.13-linux_amd64-1.6.0.jar

Run the pysail server in a separate terminal. LakeSail uses the standard PySpark client through Spark Connect. Velox pulls in pyspark[connect]>=3.5.0 and requires the Gluten bundle jar.

See LakeSail Platform Guide, Velox Platform Guide, and Velox Jar Setup for full setup instructions.

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Relational & Time-Series Databases

Traditional relational databases and specialized time-series engines.

Architecture Comparison

Feature	PostgreSQL	TimescaleDB	InfluxDB
CLI Name	postgresql	timescaledb	influxdb
Type	Relational RDBMS	Time-series (PG extension)	Time-series
Storage	Row-based (heap)	Hypertables + compression	Columnar (Parquet)
Query Language	PostgreSQL SQL	PostgreSQL SQL	SQL (FlightSQL)
ACID Support	Full	Full	Limited
Compression	Optional (TOAST)	Native	Native

Performance Characteristics

Feature	PostgreSQL	TimescaleDB	InfluxDB
Query Latency	Low	Low	Very Low
Write Throughput	Medium	High	Very High
Time-series Queries	Manual optimization	Native optimization	Native optimization
Max Recommended SF	10	100	100
Best For	OLTP workloads	Time-series OLAP	IoT/metrics

Use Cases

Use Case	Recommended	Notes
OLTP Benchmarking	PostgreSQL	Traditional workloads
Time-series Analytics	TimescaleDB	Continuous aggregates
IoT/Metrics	InfluxDB	High-cardinality support
Hybrid OLTP/OLAP	TimescaleDB	Best of both worlds

Installation

uv add benchbox --extra postgresql

uv add benchbox --extra influxdb

The postgresql extra provides the psycopg 3 driver; the influxdb extra provides influxdb3-python.

In order, these commands install the drivers for PostgreSQL, TimescaleDB (which uses the same driver as PostgreSQL), and InfluxDB.

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Managed Spark Services

Cloud-managed Apache Spark environments for distributed processing.

AWS Spark Services

Feature	AWS Glue	EMR Serverless	Athena for Spark
CLI Name	glue	emr-serverless	athena-spark
Type	Managed ETL	Serverless Spark	Interactive Spark
Startup Time	2-5 minutes	~30s (warm), 2-3min (cold)	~30s (warm), 1-2min (cold)
Pricing	$0.44/DPU-hour	vCPU + Memory	DPU-hour
Use Case	ETL pipelines	Batch processing	Interactive analysis
Data Catalog	Glue Catalog	Glue Catalog	Glue Catalog

GCP Spark Services

Feature	Dataproc	Dataproc Serverless
CLI Name	dataproc	dataproc-serverless
Type	Managed Clusters	Serverless Batches
Startup Time	1-2 minutes	30-60 seconds
Pricing	Per-second	Per-second
Use Case	Long-running clusters	Ad-hoc batches
Metastore	Hive Metastore	Hive Metastore

Azure Spark Services

Feature	Fabric Spark	Synapse Spark
CLI Name	fabric-spark	synapse-spark
Type	SaaS Spark	Enterprise Spark
Storage	OneLake	ADLS Gen2
Auth	Entra ID	Entra ID
Pricing	Capacity Units	vCore-hour
Use Case	Fabric ecosystem	Enterprise analytics

Cross-Cloud Comparison

Feature	Glue	EMR Serverless	Dataproc	Dataproc Serverless	Fabric Spark	Synapse Spark
Serverless	Yes	Yes	No	Yes	Yes	No
DataFrame Mode	Yes	Yes	Yes	Yes	Yes	Yes
SQL Mode	Yes	Yes	Yes	Yes	Yes	Yes
Auto-scaling	Yes	Yes	Manual	Yes	Yes	Manual
Spot/Preemptible	No	Yes	Yes	No	No	No

Multi-Cloud Spark Alternatives

Feature	Onehouse Quanton	Databricks
CLI Name	quanton	databricks
Type	Serverless Spark	Managed Spark
Table Formats	Hudi, Iceberg, Delta	Delta (native)
XTable Support	Yes	No
Pricing	Compute-hours	DBU-hours
Use Case	Multi-format lakes	Lakehouse
Cloud Support	AWS	AWS, Azure, GCP

Installation

uv add boto3

uv add google-cloud-dataproc google-cloud-storage

uv add azure-identity azure-storage-file-datalake requests

uv add requests boto3

In order, these commands install the dependencies for AWS Spark services (Glue, EMR Serverless, Athena for Spark), GCP Spark services (Dataproc, Dataproc Serverless), Azure Spark services (Fabric Spark, Synapse Spark), and Onehouse Quanton.

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DataFrame Platforms

Native DataFrame API libraries for programmatic data manipulation.

Important

Naming Convention: All DataFrame platforms use the -df suffix in CLI names to distinguish them from SQL-mode platforms. For example, use polars-df for Polars DataFrame API or datafusion-df for DataFusion DataFrame API.

Expression Family (Lazy Evaluation)

Feature	Polars	PySpark	DataFusion	Databricks	Snowpark Connect	LakeSail
CLI Name	polars-df	pyspark-df	datafusion-df	databricks-df	snowpark-connect	lakesail-df
Language	Rust + Python	Scala + Python	Rust + Python	Scala + Python	Python	Rust + Python
Execution	Lazy	Lazy	Lazy	Lazy	Lazy (pushdown)	Lazy
Parallelism	Multi-threaded	Distributed	Multi-threaded	Distributed	Snowflake compute	Multi-threaded / distributed
Memory Model	In-memory + streaming	Cluster-bound	In-memory	Cluster-bound	Snowflake-managed	In-memory + streaming
Arrow Native	Yes	Yes	Yes	Yes	No (Snowflake types)	Yes

Pandas Family (Eager/Lazy)

Feature	Pandas	Dask	cuDF
CLI Name	pandas-df	dask-df	cudf-df
Execution	Eager	Lazy	Eager
Parallelism	Single-threaded	Distributed	GPU
API Compatibility	Reference	Near drop-in	Near drop-in
Best For	Small datasets	Distributed	GPU acceleration

Performance Characteristics

Platform	Query Latency	Throughput	Memory Usage	Max SF
Polars	Very Low	Very High	Efficient	100
PySpark	Medium	Very High	Cluster-bound	1000+
DataFusion	Very Low	High	Efficient	50
Databricks	Medium	Very High	Cluster-bound	1000+
Snowpark Connect	Medium	High	Snowflake-managed	1000+
LakeSail	Low	Very High	Efficient	100+
Pandas	Low	Medium	High	10
Dask	Medium	High	Distributed	1000+
cuDF	Very Low	Very High	GPU VRAM	50

GPU Acceleration

Feature	cuDF	PySpark (RAPIDS)
Hardware	NVIDIA GPU	NVIDIA GPU
Platform	Linux only	Linux only
API	Pandas-like	Spark DataFrame
Memory	GPU VRAM	GPU + Host
Use Case	Single-node GPU	Distributed GPU

Installation

uv add polars
uv add pyspark
uv add datafusion

uv add databricks-connect

uv add "snowflake-snowpark-python[pandas]"

uv add pyspark pyarrow

uv add pandas
uv add dask[distributed]

pip install cudf-cu12 --extra-index-url=https://pypi.nvidia.com

The commands install, in order:

• The expression-family libraries: Polars, PySpark (requires Java 17 or 21), and DataFusion.
• Databricks DataFrame, which requires Databricks Connect.
• Snowpark Connect, which is PySpark-compatible on Snowflake.
• LakeSail DataFrame, which uses Spark Connect through the standard PySpark client.
• The Pandas family: Pandas and Dask.
• cuDF, which needs an NVIDIA GPU and Linux. It uses pip because of its CUDA dependencies.

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Hybrid Platforms (SQL + DataFrame)

These platforms support both SQL and native DataFrame execution modes. Use the appropriate CLI name based on your preferred execution paradigm.

Platform	SQL Mode	DataFrame Mode	Default Mode	Notes
DataFusion	datafusion	datafusion-df	SQL	Arrow-native both modes
PySpark	spark	pyspark-df	DataFrame	SparkSQL vs DataFrame API
Databricks	databricks	databricks-df	SQL	SQL Warehouse vs Databricks Connect
Snowpark Connect	N/A	snowpark-connect	DataFrame	PySpark-compatible API on Snowflake
LakeSail	lakesail	lakesail-df	SQL	Rust/DataFusion engine, Spark Connect

Note

Polars is DataFrame-only (polars-df). SQL mode was removed due to fundamental limitations in Polars’ SQL implementation (no implicit joins, limited subquery support) that make it incompatible with TPC benchmarks. For SQL benchmarks, use duckdb or another SQL-native platform.

Usage Example:

benchbox run --platform polars-df --benchmark tpch --scale 1

benchbox run --platform datafusion --benchmark tpch --scale 1
benchbox run --platform datafusion-df --benchmark tpch --scale 1

benchbox run --platform databricks --benchmark tpch --scale 10
benchbox run --platform databricks-df --benchmark tpch --scale 10

benchbox run --platform snowpark-connect --benchmark tpch --scale 10

Polars supports DataFrame mode only. DataFusion supports SQL mode (datafusion) and DataFrame mode (datafusion-df). Databricks runs against a SQL Warehouse (databricks) or through Databricks Connect (databricks-df). Snowpark Connect runs DataFrame workloads on Snowflake.

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Benchmark Support Matrix

TPC Benchmarks

Benchmark	Local	Cloud DW	Distributed	Time-Series	DataFrame
TPC-H	All	All	All	PostgreSQL, TimescaleDB	All
TPC-DS	DuckDB, ClickHouse	All	All	N/A	Polars, PySpark, Databricks
SSB	All	All	All	N/A	All

Analytics Benchmarks

Benchmark	Local	Cloud DW	Distributed	Time-Series	DataFrame
ClickBench	DuckDB, ClickHouse	All	ClickHouse	N/A	Polars
H2O.ai	DuckDB	All	Spark	N/A	All
AMPLab	DuckDB	All	Spark, Databricks	N/A	PySpark, Databricks

Time-Series Benchmarks

Benchmark	PostgreSQL	TimescaleDB	InfluxDB
TSBS DevOps	Limited	Full	Native
TSBS IoT	Limited	Full	Native

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Scale Factor Recommendations

Scale Factor	Dataset Size	Recommended Platforms
0.001-0.01	1-10MB	DuckDB, DataFusion, SQLite
0.1-1	100MB-1GB	DuckDB, DataFusion, ClickHouse, Polars
1-10	1-10GB	DuckDB, ClickHouse, BigQuery, All DataFrame
10-100	10-100GB	DuckDB, ClickHouse, All Cloud DW, PySpark
100-1000	100GB-1TB	BigQuery, Snowflake, Databricks, Redshift
1000+	1TB+	BigQuery, Snowflake, Databricks, Spark

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Cost Comparison (All Categories)

All cost estimates as of January 2026. Based on SF=10 workload (~10GB data), 100 queries/day.

Free/Open Source

Platform	Type	Cost
DuckDB	Local	Free
DataFusion	Local	Free
SQLite	Local	Free
Polars	Local/DataFrame	Free
ClickHouse	Local/Server	Free (self-hosted)
PostgreSQL	Relational	Free (self-hosted)
Trino/Presto	Distributed	Free (self-hosted)
Spark	Distributed	Free (self-hosted)

Cloud Pay-Per-Use

Platform	Typical Monthly Cost	Best For	Cost Basis
BigQuery	$50-5000	GCP workloads	$6.25/TiB queried
Athena	$50-2000	S3 data lake	$5/TB scanned
Firebolt	$100-3000	High-performance analytics	Per-engine

Cloud Provisioned

Platform	Typical Monthly Cost	Best For	Cost Basis
Snowflake	$100-10000	Multi-cloud enterprise	Credits/hour
Databricks	$200-5000	ML/Lakehouse	DBU/hour
Redshift	$100-5000	AWS enterprise	Node-hours
Synapse	$200-5000	Azure enterprise	DWU-hours
Fabric	Capacity-based	Microsoft ecosystem	Capacity Units

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Use Case Recommendations

By Team Size

Team Profile	Recommended	Alternatives
Individual Developer	DuckDB	DataFusion, Polars
Small Team (2-5)	DuckDB, ClickHouse	PostgreSQL
Medium Team (5-20)	BigQuery, Snowflake	Databricks
Enterprise	Snowflake, Databricks	Redshift, Synapse

By Workload

Workload	Recommended	Alternatives
Ad-hoc Analytics	DuckDB, BigQuery	Athena, Snowflake
Production Dashboards	Snowflake, BigQuery	Redshift, Databricks
ML/Data Science	Databricks, PySpark	Snowflake, BigQuery
Real-time Analytics	ClickHouse	Firebolt
Time-series IoT	TimescaleDB, InfluxDB	ClickHouse
Data Lake	Databricks, Athena	Trino, Spark

By Cloud Provider

Cloud	Data Warehouse	Spark Service	Serverless
AWS	Redshift	EMR Serverless	Athena
GCP	BigQuery	Dataproc Serverless	BigQuery
Azure	Synapse, Fabric DW	Fabric Spark	Fabric DW
Multi-cloud	Snowflake, Databricks	Databricks	Snowflake

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Migration Complexity

From \ To	DuckDB	BigQuery	Snowflake	Databricks
DuckDB	-	Medium	Medium	Hard
BigQuery	Medium	-	Easy	Medium
Snowflake	Medium	Easy	-	Medium
Databricks	Hard	Medium	Medium	-
Redshift	Medium	Medium	Easy	Medium

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Summary by Category

Local/Embedded

• DuckDB: Default choice for development, testing, and medium-scale analytics
• DataFusion: Arrow-native, ideal for PyArrow workflows
• SQLite: Lightweight testing and CI/CD
• Polars: High-performance DataFrame library (DataFrame API only, use polars-df)
• ClickHouse: High-throughput analytics (local or server mode)

Cloud Data Warehouses

• BigQuery: GCP serverless, pay-per-query
• Snowflake: Multi-cloud enterprise, elastic scaling
• Databricks: Lakehouse, ML/data science
• Redshift: AWS-native, provisioned or serverless
• Synapse: Azure enterprise, T-SQL compatible
• Fabric DW: Microsoft ecosystem, OneLake integration
• Athena: AWS serverless, S3 data lake
• Firebolt: High-performance vectorized analytics

Distributed SQL

• Trino: Federated SQL, data lake analytics
• PrestoDB: Meta’s fork, similar to Trino
• Spark SQL: Batch processing, Spark ecosystem
• ClickHouse: Real-time OLAP, high concurrency
• SingleStore: Distributed HTAP, columnstore analytics (MySQL protocol)

Spark-Compatible Engines

• LakeSail Sail: Rust drop-in Spark replacement (DataFusion core), SQL + DataFrame via Spark Connect
• Apache Gluten + Velox: Native C++ acceleration plugin for Spark SQL (Linux-only local mode; Docker on macOS/Windows)

Relational & Time-Series

• PostgreSQL: Traditional RDBMS baseline
• TimescaleDB: Time-series on PostgreSQL
• InfluxDB: IoT and metrics workloads

Managed Spark

• AWS: Glue, EMR Serverless, Athena for Spark
• GCP: Dataproc, Dataproc Serverless
• Azure: Fabric Spark, Synapse Spark

DataFrame

• Expression Family: Polars (single-node), PySpark (distributed), DataFusion (Arrow-native), Databricks (managed), Snowpark Connect (Snowflake-native), LakeSail (Rust/DataFusion via Spark Connect)
• Pandas Family: Pandas (reference), Dask (distributed), cuDF (GPU-accelerated)

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Resources

• Individual Platform Guides: See platform-specific docs in this section
• Platform Selection Guide: platform-selection-guide.md
• DataFrame Platforms: dataframe.md
• Getting Started: ../usage/getting-started.md