Snowflake Optimization with AWS Glue

Snowflake has made a significant impact on the enterprise data landscape with its groundbreaking data warehouse solution. As organizations continue to transition from traditional on-premises solutions to modern, cloud-based platforms, AWS Glue has emerged as an innovative, highly efficient, serverless data integration service. Combined, AWS Glue and Snowflake offer an incredibly powerful toolset for data processing.

With Effecual's Snowflake Optimization with AWS Glue we assess the decoupling of data processing from the data warehouse by pairing Glue with Snowflake. This optimization has proven to increase performance by as much as 120% and reduce costs by as much as 89%.

Challenges of leveraging Snowflake native ETL/ELT:

• Systemic issues – all data processing is done within Snowflake, leading to higher costs
• Limitations on types of transformations – omitting and reordering columns
• Data load is limited to SQL only – not highly performant
• Scales horizontally by instance as data load increases, leading to higher costs (not serverless)

Benefits to Customers leveraging Glue and/or EMR:

• Flexibility – Can leverage multi-language (Python, Scala, etc) for all transformations, not limited to SQL
• Error Handling – Decouple the ETL from the data warehouse – helps with root cause analysis
• Seamless Integration – suite of tools integrates seamlessly with Snowflake (crawlers, catalogue)
• Machine Learning Analytics – Robust real-time data streaming for ML workloads

Cost and Performance Optimization

• Cost Optimization – Only load data into Snowflake that is needed with pre-processing jobs, lowering costs
• Performance Optimization – Spark’s distributed nature processes large amounts of data
• Recognize cost savings up to 89%
• Recognize performance increases of up to 120%