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Block's Transformation of Financial Services with Databricks
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Education
- Equipment & Machinery
Applicable Functions
- Product Research & Development
- Quality Assurance
Use Cases
- Fraud Detection
- Inventory Management
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
The Challenge
Block, a global technology company, was facing challenges in managing a large volume of data crucial for graph-related use cases. This included handling graph databases, leveraging various machine learning tools, and optimizing performance for petabytes of data. Operational inefficiencies and scalability concerns arose due to the fragmented nature of data across diverse business units. The cumbersome data transfers between these systems, combined with the siloed nature of data governance policies, posed auditing and policy enforcement challenges. Block was also in need of a proper implementation and uniformity of data governance policies to ensure compliance with privacy laws like GDPR and CCPA for both customers and internal teams.
About The Customer
Block is a global technology company that champions accessible financial services, prioritizing economic empowerment. Its subsidiaries, including Square, Cash App, Spiral, TBD, and TIDAL, are committed to expanding economic access. By utilizing machine learning (ML) and artificial intelligence (AI), Block proactively identifies and prevents fraud, ensuring secure customer transactions. Moreover, Block enhances user experiences by delivering personalized recommendations, utilizing identity resolution to gain a comprehensive understanding of customer activities across their diverse services. Internally, Block optimizes operations through automation and predictive analytics, driving efficiency in financial service delivery.
The Solution
To overcome these challenges, Block chose to migrate to Spark and selected Databricks as their lakehouse platform. This allowed them to consolidate all data and AI workloads onto a unified platform, empowering data scientists, data engineers, and AI practitioners to leverage data efficiently from a centralized location. Block adopted Unity Catalog for centralized governance, which provided a unified view of their data estate across different business units and simplified access permission management. It also offered the flexibility to distribute cost attribution among teams by allowing the assignment of storage locations per team for their catalogs and schemas. Block also plans to leverage data lineage to comply with right-to-forget scenarios, ensuring adherence to data privacy regulations.
Operational Impact
Quantitative Benefit
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