Transforming data management and reporting for enhanced efficiency

Systech enhanced a top-tier retailer’s data landscape by leveraging Snowflake, resulting in faster processing, quicker reporting, and efficient storage utilization. 

BUSINESS NEED 

The client, a prominent leader in the sports retail industry, required a more robust and efficient data management solution to accommodate their rapidly expanding data volumes and ensure the prompt delivery of critical reports. Their existing system encountered performance bottlenecks, particularly in processing large-scale, time-sensitive reports, resulting in delays and impeding strategic business decisions. 

SYSTECH’S DELIVERY 

A strategic migration approach, with a proof of concept (POC) to evaluate the effectiveness of transitioning data from Oracle to Snowflake. By leveraging Snowflake’s advanced data warehousing capabilities, Systech demonstrated substantial improvements in processing speed, storage efficiency, and reporting performance. 

OVERVIEW 

The client’s data was initially managed within an Oracle database. However, to leverage Snowflake’s cloud-based data warehousing and computational efficiencies, a migration was planned. The Proof of Concept (POC) aimed to compare the performance of executing specific data processing jobs on Oracle versus Snowflake while evaluating key factors such as processing speeds, storage optimization, and report refresh rates when integrated with Power BI. 

THE CHALLENGE 

The primary challenge stemmed from performance bottlenecks in the existing Oracle system, especially when generating daily, time-sensitive reports crucial for executive decision-making. As the data requirements expanded to incorporate more detailed line-level and SKU-level information, the Oracle system encountered difficulties in managing the significantly increased data volumes. This inefficiency led to delays in report refresh times and slowed down ETL processes, ultimately impacting the client’s ability to access timely and accurate business insights. 

THE DETAILED SOLUTION PROCESS 

The POC evaluated various Snowflake warehouse sizes, including X-Small and Small, to find the optimal configuration for performance, cost-efficiency, and computational power. 

The migration focused on two critical processes: 

• A shrink and cycle count, that aggregated data across multiple intermediate tables, eventually loading it into the final table. 

• An all-in-one Line which catered a detailed categorization up to the line level, while avoiding the complexity of SKU-level data due to its volume. 

The performance benchmarking showed significant reductions in execution times of various process, when comparing Oracle to Snowflake, the most efficient being 15 minutes to 21 seconds, up until 11 minutes to 3 minutes. 

Snowflake’s data compression capabilities led to substantial storage savings, reducing a dataset from 512GB in Oracle to just 9.34GB in Snowflake. 

The integration of Snowflake with Power BI resulted in quicker report refresh rates and enabling timely access to critical business insights. 

The automation of the data migration process overcame the existing limitations, extracting data in batches and using iterative loops with optimized copy commands, ensuring a smooth transition to Snowflake. 

Enhancing the environment by increasing temporary storage space to minimize processing loops revealed further opportunities for future optimization. 

THE IMPACT 

• Reduced data processing times allowed reports to be completed in seconds instead of minutes, which streamlined decision-making and improved operational efficiency. 

• Significant cost savings with Snowflake’s optimized data storage drastically decreased the storage requirement from 512GB to just 9.34GB. 

• Improved report refresh rates with Power BI integration, providing real-time insights for faster and more informed business decisions. 

• simplified data management and accelerated new project setup due to the standardized data structures in Snowflake, which reduced complexity and deployment time. 

• cost-effective scaling, optimizing resource utilization without unnecessary expenses, leveraging Snowflake’s serverless “Pay as You Go” model 

The process also uncovered further opportunities for optimization, such as enhancing the environment to reduce processing loops and drive greater efficiency. 

THE ADDED VALUE 

The implementation created a scalable and efficient data environment, handling the client’s growing data volumes with ease. This resulted in substantial cost savings through optimized storage. Furthermore, the flexible, cloud-based infrastructure offered the client the agility to adapt to evolving data needs and align with their broader business strategies. 

Ready to take your data game to the next level? Systech specializes in cloud-based data migration and optimization. Let’s chat about how we can tailor a solution that fits your unique business needs and goals. Reach out for a custom consultation! 

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