This report presents a comprehensive analysis of the cost and performance benefits of using traditional cloud data warehouse solutions (Snowflake, Databricks, and Amazon Redshift) versus a modern data lake approach using Amazon S3 and Azure Data Lake Storage (ADLS) for data storage and Fivetran Managed Data Lake Service for metadata management, open table format conversion, and automated table maintenance.
Incremental data ingestion is a significant cost driver of a data architecture, underscoring the importance of optimizing data integration and storage strategies.
We saw a substantial reduction in these data ingest cost queries when using the Fivetran Managed Data Lake Service, which incurs the cost of ingest compute when writing to a data lake. Initial sync costs were not included in our calculations.
Key Findings:
- Significant cost savings: The data lake approach yields substantial cost savings, reducing total costs from 77% to 95% as compared to traditional data warehouse solutions.
- Lower incremental sync costs: The modern data lake approach incurs lower incremental sync compute costs in our test, ranging from $0.63 to $2.82 compared to $6.13 to $14.17 per incremental sync for data warehouse solutions.
- Slightly slower incremental sync times: The modern data lake approach exhibits slightly slower incremental sync times, with a 8-10% increase in sync time compared to data warehouse solutions.
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