Transforming Data Science Processes for a Leading Investment Firm

A US-based tech investment firm recognized the need to centralize its data science processes to enhance scalability, efficiency, and collaboration. Their previous workflows were disjointed, with team members working in silos and critical processes scattered across multiple location systems. 

The client faced several challenges with their existing data science processes. There was a lack of collaboration and shareability among team members, and workflows deployed on local infrastructure were disorganized and isolated.  

Additionally, there was no centralized location for productionizing and orchestrating analytical and machine learning workloads. This resulted in the duplication of code and repeated work efforts by employees due to siloed operations. 

The Aimpoint Digital team collaborated with the client to understand and address their challenges and implement a unified solution. 

We thoroughly evaluated potential data science platforms, considering the client’s need for code shareability, scalability, scheduling, and orchestration capabilities. Databricks emerged as the optimal solution because of its comprehensive features and user-friendly interface. 

Our team developed a proof-of-concept use case for each potential solution tailored to the client’s typical workflows. Quantitative weighting of the features was used to determine the most suitable platform. 

Leveraging the client’s existing investment in Snowflake for data engineering workloads, we developed wrapper functions around the Snowflake Spark connector to facilitate seamless authentication from Databricks.