When should I use Amazon Redshift instead of Google BigQuery?

Cloud data warehouse for petabyte-scale analytical SQL queries in the AWS ecosystem. BI and reporting workloads that need fast columnar storage and MPP query execution on large tables. Teams already standardized on AWS who need a managed data warehouse without infrastructure work

When should I use Google BigQuery instead of Amazon Redshift?

Serverless, petabyte-scale analytical SQL queries with no infrastructure to provision or manage. Teams on GCP who need sub-minute OLAP queries on very large datasets without capacity planning. ML integration via BigQuery ML and direct connectivity to Vertex AI and Looker

What are the main weaknesses of Amazon Redshift?

Expensive at scale — reserved or on-demand capacity costs grow quickly with data and query volume. COPY-based bulk loading is the fast ingest path; single-row inserts are very slow. Competes poorly with Snowflake and BigQuery on serverless scaling, ease of use, and feature velocity

What are the main weaknesses of Google BigQuery?

On-demand pricing can be expensive for large scans or poorly optimized queries without slot reservations. Not suitable for OLTP or high-concurrency transactional workloads. Vendor lock-in: BigQuery SQL dialect has proprietary extensions that differ from standard ANSI SQL

Amazon Redshift vs Google BigQuery: Key Differences for Python Data Engineering

Cloud Services

Amazon Redshift

Cloud Data Warehouse

★ 4.6

Commercial (AWS)

pip install redshift-connector

Google BigQuery

Serverless Data Warehouse

★ 4.8

Commercial (Google Cloud)

pip install google-cloud-bigquery

Side-by-Side Comparison

Amazon Redshift

Google BigQuery

Amazon Redshift

Google BigQuery

Best For

✓Cloud data warehouse for petabyte-scale analytical SQL queries in the AWS ecosystem
✓BI and reporting workloads that need fast columnar storage and MPP query execution on large tables
✓Teams already standardized on AWS who need a managed data warehouse without infrastructure work

✓Serverless, petabyte-scale analytical SQL queries with no infrastructure to provision or manage
✓Teams on GCP who need sub-minute OLAP queries on very large datasets without capacity planning
✓ML integration via BigQuery ML and direct connectivity to Vertex AI and Looker

Best For

✓Cloud data warehouse for petabyte-scale analytical SQL queries in the AWS ecosystem
✓BI and reporting workloads that need fast columnar storage and MPP query execution on large tables
✓Teams already standardized on AWS who need a managed data warehouse without infrastructure work

✓Serverless, petabyte-scale analytical SQL queries with no infrastructure to provision or manage
✓Teams on GCP who need sub-minute OLAP queries on very large datasets without capacity planning
✓ML integration via BigQuery ML and direct connectivity to Vertex AI and Looker

Weaknesses

•Expensive at scale — reserved or on-demand capacity costs grow quickly with data and query volume
•COPY-based bulk loading is the fast ingest path; single-row inserts are very slow
•Competes poorly with Snowflake and BigQuery on serverless scaling, ease of use, and feature velocity

•On-demand pricing can be expensive for large scans or poorly optimized queries without slot reservations
•Not suitable for OLTP or high-concurrency transactional workloads
•Vendor lock-in: BigQuery SQL dialect has proprietary extensions that differ from standard ANSI SQL

Weaknesses

•Expensive at scale — reserved or on-demand capacity costs grow quickly with data and query volume
•COPY-based bulk loading is the fast ingest path; single-row inserts are very slow
•Competes poorly with Snowflake and BigQuery on serverless scaling, ease of use, and feature velocity

•On-demand pricing can be expensive for large scans or poorly optimized queries without slot reservations
•Not suitable for OLTP or high-concurrency transactional workloads
•Vendor lock-in: BigQuery SQL dialect has proprietary extensions that differ from standard ANSI SQL

License

Commercial (AWS)

Commercial (Google Cloud)

License

Commercial (AWS)

Commercial (Google Cloud)

Install

pip install redshift-connector

pip install google-cloud-bigquery

Install

pip install redshift-connector

pip install google-cloud-bigquery

Rating

★ 4.6

★ 4.8

Rating

★ 4.6

★ 4.8

Key Features

Amazon Redshift

1Columnar storage with data compression for fast analytical queries
2Redshift Spectrum queries S3 data directly without loading into Redshift
3COPY command ingests bulk data from S3, DynamoDB, and Kinesis at high speed
4Concurrency scaling and Elastic Resize for variable workload demands
5Native integration with Glue, DMS, and Data Pipeline for ETL orchestration

Google BigQuery

1Serverless, petabyte-scale SQL analytics warehouse with no infrastructure to manage
2Partitioned and clustered tables for cost-efficient query execution
3Streaming inserts for real-time data ingestion with sub-second availability
4BigQuery ML runs SQL-defined ML models directly in the warehouse
5Omni extends BigQuery SQL to data in AWS S3 and Azure Blob Storage

How Python Data Engineers Use These Tools

Amazon Redshift

Python data engineers load transformed data into Redshift using the COPY command via boto3 — staging data in S3 first then issuing a COPY SQL statement for fast bulk load. Libraries like `redshift_connector` and `sqlalchemy-redshift` enable DataFrame-to-table writes and SQL queries directly from Python notebooks and Airflow tasks.

Google BigQuery

Python data engineers use the `google-cloud-bigquery` client to run analytical SQL and pull results into pandas — `client.query(sql).to_dataframe()` is the most common pattern. Engineers also use `load_table_from_dataframe()` to write pandas DataFrames back to BigQuery tables, and the BigQuery Storage API for high-throughput reads of large tables.

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Individual Tool Pages

View Amazon Redshift details →View Google BigQuery details →

Side-by-Side Comparison

Amazon Redshift

Google BigQuery

Amazon Redshift

Google BigQuery

Best For

✓Cloud data warehouse for petabyte-scale analytical SQL queries in the AWS ecosystem
✓BI and reporting workloads that need fast columnar storage and MPP query execution on large tables
✓Teams already standardized on AWS who need a managed data warehouse without infrastructure work

✓Serverless, petabyte-scale analytical SQL queries with no infrastructure to provision or manage
✓Teams on GCP who need sub-minute OLAP queries on very large datasets without capacity planning
✓ML integration via BigQuery ML and direct connectivity to Vertex AI and Looker

Best For

✓Cloud data warehouse for petabyte-scale analytical SQL queries in the AWS ecosystem
✓BI and reporting workloads that need fast columnar storage and MPP query execution on large tables
✓Teams already standardized on AWS who need a managed data warehouse without infrastructure work

✓Serverless, petabyte-scale analytical SQL queries with no infrastructure to provision or manage
✓Teams on GCP who need sub-minute OLAP queries on very large datasets without capacity planning
✓ML integration via BigQuery ML and direct connectivity to Vertex AI and Looker

Weaknesses

•Expensive at scale — reserved or on-demand capacity costs grow quickly with data and query volume
•COPY-based bulk loading is the fast ingest path; single-row inserts are very slow
•Competes poorly with Snowflake and BigQuery on serverless scaling, ease of use, and feature velocity

•On-demand pricing can be expensive for large scans or poorly optimized queries without slot reservations
•Not suitable for OLTP or high-concurrency transactional workloads
•Vendor lock-in: BigQuery SQL dialect has proprietary extensions that differ from standard ANSI SQL

Weaknesses

•Expensive at scale — reserved or on-demand capacity costs grow quickly with data and query volume
•COPY-based bulk loading is the fast ingest path; single-row inserts are very slow
•Competes poorly with Snowflake and BigQuery on serverless scaling, ease of use, and feature velocity

•On-demand pricing can be expensive for large scans or poorly optimized queries without slot reservations
•Not suitable for OLTP or high-concurrency transactional workloads
•Vendor lock-in: BigQuery SQL dialect has proprietary extensions that differ from standard ANSI SQL

License

Commercial (AWS)

Commercial (Google Cloud)

License

Commercial (AWS)

Commercial (Google Cloud)

Install

pip install redshift-connector

pip install google-cloud-bigquery

Install

pip install redshift-connector

pip install google-cloud-bigquery

Rating

★ 4.6

★ 4.8

Rating

★ 4.6

★ 4.8

Key Features

Amazon Redshift

1Columnar storage with data compression for fast analytical queries
2Redshift Spectrum queries S3 data directly without loading into Redshift
3COPY command ingests bulk data from S3, DynamoDB, and Kinesis at high speed
4Concurrency scaling and Elastic Resize for variable workload demands
5Native integration with Glue, DMS, and Data Pipeline for ETL orchestration

Google BigQuery

1Serverless, petabyte-scale SQL analytics warehouse with no infrastructure to manage
2Partitioned and clustered tables for cost-efficient query execution
3Streaming inserts for real-time data ingestion with sub-second availability
4BigQuery ML runs SQL-defined ML models directly in the warehouse
5Omni extends BigQuery SQL to data in AWS S3 and Azure Blob Storage

How Python Data Engineers Use These Tools