When should I use Greenplum instead of Snowflake?

MPP PostgreSQL-based data warehouse for large-scale analytical workloads with familiar SQL. Teams wanting a PostgreSQL SQL dialect with massively parallel execution for analytical queries. On-premises data warehouse workloads where cloud migration is not yet feasible or desired

When should I use Snowflake instead of Greenplum?

Cloud-native data warehouse with near-infinite scalability and separation of compute and storage. Multi-cloud environments (AWS, Azure, GCP) requiring a consistent SQL interface across providers. Secure data sharing and data marketplace for exchanging data between organizations

What are the main weaknesses of Greenplum?

On-premises-first design; cloud deployment is less seamless than Snowflake or BigQuery. Complex administration: table redistribution, vacuum, and resource queue tuning require expertise. VMware/Broadcom ownership raises concerns about the long-term open-source roadmap

What are the main weaknesses of Snowflake?

Per-credit compute pricing can surprise teams without active cost governance and query optimization. Not suitable for OLTP or high-concurrency transactional workloads. Vendor lock-in: Snowflake SQL has proprietary extensions and features not in ANSI standard SQL

Greenplum vs Snowflake: Key Differences for Python Data Engineering

Databases & Data Warehouses

Greenplum

Open Source Data Warehouse

★ 4.2

Apache-2.0

pip install psycopg2-binary

Snowflake

Cloud Data Platform

★ 4.8

Commercial (Snowflake)

pip install snowflake-connector-python

Side-by-Side Comparison

Greenplum

Snowflake

Greenplum

Snowflake

Best For

✓MPP PostgreSQL-based data warehouse for large-scale analytical workloads with familiar SQL
✓Teams wanting a PostgreSQL SQL dialect with massively parallel execution for analytical queries
✓On-premises data warehouse workloads where cloud migration is not yet feasible or desired

✓Cloud-native data warehouse with near-infinite scalability and separation of compute and storage
✓Multi-cloud environments (AWS, Azure, GCP) requiring a consistent SQL interface across providers
✓Secure data sharing and data marketplace for exchanging data between organizations

Best For

✓MPP PostgreSQL-based data warehouse for large-scale analytical workloads with familiar SQL
✓Teams wanting a PostgreSQL SQL dialect with massively parallel execution for analytical queries
✓On-premises data warehouse workloads where cloud migration is not yet feasible or desired

✓Cloud-native data warehouse with near-infinite scalability and separation of compute and storage
✓Multi-cloud environments (AWS, Azure, GCP) requiring a consistent SQL interface across providers
✓Secure data sharing and data marketplace for exchanging data between organizations

Weaknesses

•On-premises-first design; cloud deployment is less seamless than Snowflake or BigQuery
•Complex administration: table redistribution, vacuum, and resource queue tuning require expertise
•VMware/Broadcom ownership raises concerns about the long-term open-source roadmap

•Per-credit compute pricing can surprise teams without active cost governance and query optimization
•Not suitable for OLTP or high-concurrency transactional workloads
•Vendor lock-in: Snowflake SQL has proprietary extensions and features not in ANSI standard SQL

Weaknesses

•On-premises-first design; cloud deployment is less seamless than Snowflake or BigQuery
•Complex administration: table redistribution, vacuum, and resource queue tuning require expertise
•VMware/Broadcom ownership raises concerns about the long-term open-source roadmap

•Per-credit compute pricing can surprise teams without active cost governance and query optimization
•Not suitable for OLTP or high-concurrency transactional workloads
•Vendor lock-in: Snowflake SQL has proprietary extensions and features not in ANSI standard SQL

License

Apache-2.0

Commercial (Snowflake)

License

Apache-2.0

Commercial (Snowflake)

Install

pip install psycopg2-binary

pip install snowflake-connector-python

Install

pip install psycopg2-binary

pip install snowflake-connector-python

Rating

★ 4.2

★ 4.8

Rating

★ 4.2

★ 4.8

Key Features

Greenplum

1Massively parallel PostgreSQL-based analytical database
2Append-optimized column tables for analytical workload compression
3PL/Python and PL/R UDFs run Python and R code in-database
4gpfdist parallel data loading from external files at high throughput
5MADlib library for in-database machine learning on distributed data

Snowflake

1Separate compute and storage scaling — pay for query time independently
2Zero-copy cloning for instant development and test environment creation
3Snowpark Python API for pushing DataFrame operations into Snowflake compute
4Time Travel for querying historical data up to 90 days back
5Data Sharing and Marketplace for governed data exchange between accounts

How Python Data Engineers Use These Tools

Greenplum

Python data engineers use Greenplum's PL/Python extension to run Python transformation logic directly inside the database — writing Python UDFs that execute in parallel across all Greenplum segments. This in-database Python execution avoids moving large datasets out of Greenplum for transformation, keeping all processing co-located with the data.

Snowflake

Python data engineers use `snowflake-connector-python` and `snowflake-sqlalchemy` to load data into Snowflake and run queries. The Snowpark Python library pushes pandas-like DataFrame operations directly into Snowflake's compute engine — enabling engineers to write Python transformation logic that runs as a stored procedure without moving data out of Snowflake.

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Individual Tool Pages

View Greenplum details →View Snowflake details →

Side-by-Side Comparison

Greenplum

Snowflake

Greenplum

Snowflake

Best For

✓MPP PostgreSQL-based data warehouse for large-scale analytical workloads with familiar SQL
✓Teams wanting a PostgreSQL SQL dialect with massively parallel execution for analytical queries
✓On-premises data warehouse workloads where cloud migration is not yet feasible or desired

✓Cloud-native data warehouse with near-infinite scalability and separation of compute and storage
✓Multi-cloud environments (AWS, Azure, GCP) requiring a consistent SQL interface across providers
✓Secure data sharing and data marketplace for exchanging data between organizations

Best For

✓MPP PostgreSQL-based data warehouse for large-scale analytical workloads with familiar SQL
✓Teams wanting a PostgreSQL SQL dialect with massively parallel execution for analytical queries
✓On-premises data warehouse workloads where cloud migration is not yet feasible or desired

✓Cloud-native data warehouse with near-infinite scalability and separation of compute and storage
✓Multi-cloud environments (AWS, Azure, GCP) requiring a consistent SQL interface across providers
✓Secure data sharing and data marketplace for exchanging data between organizations

Weaknesses

•On-premises-first design; cloud deployment is less seamless than Snowflake or BigQuery
•Complex administration: table redistribution, vacuum, and resource queue tuning require expertise
•VMware/Broadcom ownership raises concerns about the long-term open-source roadmap

•Per-credit compute pricing can surprise teams without active cost governance and query optimization
•Not suitable for OLTP or high-concurrency transactional workloads
•Vendor lock-in: Snowflake SQL has proprietary extensions and features not in ANSI standard SQL

Weaknesses

•On-premises-first design; cloud deployment is less seamless than Snowflake or BigQuery
•Complex administration: table redistribution, vacuum, and resource queue tuning require expertise
•VMware/Broadcom ownership raises concerns about the long-term open-source roadmap

•Per-credit compute pricing can surprise teams without active cost governance and query optimization
•Not suitable for OLTP or high-concurrency transactional workloads
•Vendor lock-in: Snowflake SQL has proprietary extensions and features not in ANSI standard SQL

License

Apache-2.0

Commercial (Snowflake)

License

Apache-2.0

Commercial (Snowflake)

Install

pip install psycopg2-binary

pip install snowflake-connector-python

Install

pip install psycopg2-binary

pip install snowflake-connector-python

Rating

★ 4.2

★ 4.8

Rating

★ 4.2

★ 4.8

Key Features

Greenplum

1Massively parallel PostgreSQL-based analytical database
2Append-optimized column tables for analytical workload compression
3PL/Python and PL/R UDFs run Python and R code in-database
4gpfdist parallel data loading from external files at high throughput
5MADlib library for in-database machine learning on distributed data

Snowflake

1Separate compute and storage scaling — pay for query time independently
2Zero-copy cloning for instant development and test environment creation
3Snowpark Python API for pushing DataFrame operations into Snowflake compute
4Time Travel for querying historical data up to 90 days back
5Data Sharing and Marketplace for governed data exchange between accounts

How Python Data Engineers Use These Tools