Case Study: GEN-I Enhances Data Integration with Etlworks

GEN-I Optimizes SQL Server to Snowflake Data Integration with Parallel Processing and Wildcard-Based Workflows.

Introduction

GEN-I, a global leader in energy trading, needed a powerful data integration solution to efficiently ETL data from their SQL Server databases into Snowflake. The company sought a platform that could handle high-volume data with speed and accuracy while leveraging advanced ETL techniques to optimize performance and scalability across multiple tables.

The Challenge

GEN-I faced several key challenges in their data integration process:

High Volume: Required efficient extraction of large datasets from SQL Server.

Incremental Loads: Needed to process only the changed data (incremental loads) to reduce processing time and optimize resources.

Processing Multiple Tables: Sought a solution to handle multiple tables using wildcard-based workflows for efficient management.

Performance Optimization: Required parallel processing to extract data from multiple database partitions simultaneously.

Snowflake Integration: Sought a batch-loading mechanism to enhance performance when transferring data to Snowflake.

Why Etlworks

GEN-I chose Etlworks because it offered:

Wildcard Support: Simplified management of multiple tables by enabling workflows to handle dynamic table patterns.

Parallel Partition Processing: Capability to extract data from multiple database partitions simultaneously, significantly boosting performance.

Incremental Load: High-watermark replication ensured only changed data was processed, saving time and resources.

Snowflake Batch Loading: Native support for optimized batch loading into Snowflake for faster data transfers.

A scalable, no-code/low-code platform with advanced customization options to meet their unique requirements.

The Solution

Etlworks provided GEN-I with a streamlined solution tailored to their needs:

Wildcard-Based Workflows: Implemented wildcard processing to dynamically handle multiple tables with similar patterns, reducing manual effort and improving efficiency.

Parallel Data Extraction: Configured ETL flows to extract data concurrently from multiple SQL Server partitions, leveraging the full power of their infrastructure.

Incremental Loading: Implemented high-watermark replication to capture and process only the data that had changed since the last run.

Snowflake Integration: Used Etlworks’ native Snowflake connector to enable efficient batch loading, ensuring seamless data transfer and integration.

Results

Streamlined Management: Wildcard workflows simplified the processing of multiple tables, reducing setup time.

Improved Performance: Parallel partition processing reduced data extraction time significantly.

Optimized Resource Usage: Incremental load processing minimized overhead and reduced system strain.

Seamless Snowflake Integration: Batch loading improved the speed and reliability of data transfers to Snowflake.

Scalability: Enabled GEN-I to handle increasing data volumes and additional tables without compromising performance.

Customer Quote

“Etlworks has revolutionized how we manage data integration. The combination of parallel processing, wildcard-based workflows, and optimized Snowflake integration allows us to process large datasets across multiple tables quickly and efficiently. It’s a game-changer for our operations.”

Key Takeaways

Flexibility: Wildcard processing simplifies managing multiple tables dynamically.

Performance: Parallel partition processing maximizes extraction speed from SQL Server.

Efficiency: Incremental load processing ensures minimal resource use by handling only changed data.

Integration: Batch loading provides seamless and high-performance Snowflake integration.

Scalability: Etlworks supports growing data volumes and additional tables with ease, future-proofing data workflows.

Ready to tackle your most complex data challenges? Discover how Etlworks can transform your data integration workflows. Start your free trial today or request a demo.