When should I use Amazon Redshift instead of Azure Synapse Analytics?

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 Azure Synapse Analytics instead of Amazon Redshift?

Unified analytics platform combining SQL data warehousing and Spark big data processing in Azure. Teams wanting SQL and Spark analytics in a single workspace integrated with Power BI and Azure ML. Enterprises standardized on Azure who need a single analytics platform replacing multiple tools

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 Azure Synapse Analytics?

Complex pricing model combining multiple meters: Synapse SQL pools, Spark pools, and pipelines. Slower feature velocity than Databricks or Snowflake; some integrations feel bolted on. Local development and testing experience is weaker than pure Spark or SQL warehouse alternatives

Amazon Redshift vs Azure Synapse Analytics: Key Differences for Python Data Engineering

Cloud Services

Amazon Redshift

Cloud Data Warehouse

★ 4.6

Commercial (AWS)

pip install redshift-connector

Azure Synapse Analytics

Unified Analytics Platform

★ 4.5

Commercial (Microsoft Azure)

pip install azure-synapse

Side-by-Side Comparison

Amazon Redshift

Azure Synapse Analytics

Amazon Redshift

Azure Synapse Analytics

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

✓Unified analytics platform combining SQL data warehousing and Spark big data processing in Azure
✓Teams wanting SQL and Spark analytics in a single workspace integrated with Power BI and Azure ML
✓Enterprises standardized on Azure who need a single analytics platform replacing multiple tools

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

✓Unified analytics platform combining SQL data warehousing and Spark big data processing in Azure
✓Teams wanting SQL and Spark analytics in a single workspace integrated with Power BI and Azure ML
✓Enterprises standardized on Azure who need a single analytics platform replacing multiple tools

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

•Complex pricing model combining multiple meters: Synapse SQL pools, Spark pools, and pipelines
•Slower feature velocity than Databricks or Snowflake; some integrations feel bolted on
•Local development and testing experience is weaker than pure Spark or SQL warehouse alternatives

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

•Complex pricing model combining multiple meters: Synapse SQL pools, Spark pools, and pipelines
•Slower feature velocity than Databricks or Snowflake; some integrations feel bolted on
•Local development and testing experience is weaker than pure Spark or SQL warehouse alternatives

License

Commercial (AWS)

Commercial (Microsoft Azure)

License

Commercial (AWS)

Commercial (Microsoft Azure)

Install

pip install redshift-connector

pip install azure-synapse

Install

pip install redshift-connector

pip install azure-synapse

Rating

★ 4.6

★ 4.5

Rating

★ 4.6

★ 4.5

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

Azure Synapse Analytics

1Unified analytics platform combining data integration, warehousing, and big data analytics
2Serverless SQL pools for querying data lake files without provisioning infrastructure
3Apache Spark pools for large-scale data transformation and ML workloads
4Built-in Azure Data Factory pipelines for data integration and orchestration
5Native integration with Power BI, Azure ML, and Azure Purview for governance

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.

Azure Synapse Analytics

Python data engineers use Azure Synapse Analytics via the azure-synapse-spark Python SDK and PySpark for large-scale data transformation on Synapse Spark pools. The azure-synapse-artifacts library enables Python orchestration of Synapse pipelines programmatically. Engineers use Synapse for building cloud data lakehouse architectures on Azure — combining ADLS Gen2 storage, serverless SQL for ad-hoc queries, and dedicated SQL pools for the analytical warehouse layer.

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Individual Tool Pages

View Amazon Redshift details →View Azure Synapse Analytics details →

Side-by-Side Comparison

Amazon Redshift

Azure Synapse Analytics

Amazon Redshift

Azure Synapse Analytics

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

✓Unified analytics platform combining SQL data warehousing and Spark big data processing in Azure
✓Teams wanting SQL and Spark analytics in a single workspace integrated with Power BI and Azure ML
✓Enterprises standardized on Azure who need a single analytics platform replacing multiple tools

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

✓Unified analytics platform combining SQL data warehousing and Spark big data processing in Azure
✓Teams wanting SQL and Spark analytics in a single workspace integrated with Power BI and Azure ML
✓Enterprises standardized on Azure who need a single analytics platform replacing multiple tools

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

•Complex pricing model combining multiple meters: Synapse SQL pools, Spark pools, and pipelines
•Slower feature velocity than Databricks or Snowflake; some integrations feel bolted on
•Local development and testing experience is weaker than pure Spark or SQL warehouse alternatives

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

•Complex pricing model combining multiple meters: Synapse SQL pools, Spark pools, and pipelines
•Slower feature velocity than Databricks or Snowflake; some integrations feel bolted on
•Local development and testing experience is weaker than pure Spark or SQL warehouse alternatives

License

Commercial (AWS)

Commercial (Microsoft Azure)

License

Commercial (AWS)

Commercial (Microsoft Azure)

Install

pip install redshift-connector

pip install azure-synapse

Install

pip install redshift-connector

pip install azure-synapse

Rating

★ 4.6

★ 4.5

Rating

★ 4.6

★ 4.5

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

Azure Synapse Analytics

1Unified analytics platform combining data integration, warehousing, and big data analytics
2Serverless SQL pools for querying data lake files without provisioning infrastructure
3Apache Spark pools for large-scale data transformation and ML workloads
4Built-in Azure Data Factory pipelines for data integration and orchestration
5Native integration with Power BI, Azure ML, and Azure Purview for governance

How Python Data Engineers Use These Tools