Challenges
- The current system’s limitations in handling extended data periods lead to timeouts and inefficiencies, restricting the ability to derive comprehensive insights from historical data for trend analysis and strategic decision-making.
- The growing data size in terabytes is straining the current system, impeding efficient data processing and analysis. Moreover, the system’s inflexible structure hinders its adaptability for future growth, limiting valuable insights for informed business decisions.
There was a need to seek a scalable solution to handle large data volumes while unlocking meaningful business intelligence.
Solutions
- Successfully implemented Snowflake as a warehousing solution to enable generating analytical insights at scale. The migration from Athena to Snowflake was accomplished incrementally, starting with the delivery of new Minimum Viable Products (MVPs) using Snowflake’s capabilities.
- Subsequently, the remaining data was migrated from Athena to Snowflake in a seamless manner, allowing us to fully leverage Snowflake’s potential.
- To optimize time-to-market and data readiness, we established curated data tables. These tables ensure that data is well-organized and prepared, expediting the analysis process and enabling us to deliver actionable insights promptly to the stakeholders.
Impact generated
Performance – 86% boost
New (Snowflake): < 8 seconds as compared to old (Athena Query): > 60 seconds
Scalable – 3x range insights
Transitioning from Athena to Snowflake enabled generating insights for a larger date range that previously resulted in timeouts with Athena even for a few months.
Time to Market – 2x faster
Curated tables enabling versatile usage across dimensions, generating internal and customer-facing insights faster.
