Cloud Data Platform Evaluation
A comparative evaluation of Snowflake, Azure Synapse and Amazon Redshift on a multi-year sales dataset.
Compared Snowflake, Azure Synapse and Amazon Redshift across eight business dimensions to guide a platform decision.
- Scored the three platforms across cloud flexibility, scalability, integration efficiency, visualisation compatibility, security, compliance, setup complexity and query performance.
- Validated against a four-year sales dataset with end-to-end demos covering ingestion, analytics workload benchmarking and cost-performance.
- Delivered a scoring matrix that pointed to Azure Synapse for hybrid-cloud environments and Snowflake for pure scalability, so stakeholders could match platform choice to priorities.
Benchmarked Snowflake, Azure Synapse and Amazon Redshift for AI and ML workloads and large-scale analytics.
- Tested cross-platform ingestion (CSV, JSON, APIs), finding integration on Snowflake's zero-management architecture about 98% faster than Redshift.
- Processed the four-year sales dataset on Azure Synapse serverless pools and measured a 40% query-latency reduction against traditional warehousing.
- Assessed ML readiness with Python and TensorFlow pipelines, with Snowflake Snowpark giving roughly 3x faster model training on structured data.
The study
This evaluation exists in two framings for two audiences. Use the toggle to switch between the business decision-support view and the technical and ML-readiness view. Both share the same study: a comparison of Snowflake, Azure Synapse and Amazon Redshift run against a four-year sales dataset, covering ingestion, query performance, cost and visualisation integration. The recommendation favoured Snowflake for real-time and scalability cases and Azure Synapse for hybrid-cloud and enterprise needs.