Databricks Case Studies Block's Transformation of Financial Services with Databricks

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	Block's Transformation of Financial Services with Databricks Databricks

Block's Transformation of Financial Services with Databricks

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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 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.

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.

Impact #1	The migration to the Databricks platform and the implementation of Unity Catalog brought about transformative benefits for Block. It facilitated the creation of a dynamic “marketplace” for data exchange between different business units, fostering collaboration and knowledge sharing. The operational efficiency of data sharing improved significantly, reducing the time required from days to seconds. Unity Catalog also improved the ease of IAM policy management for Block, streamlining the process by configuring sub-group level access permissions in a single location. Moreover, it empowered Block to attribute data ownership more easily and decentralize decision-making, enhancing overall data governance and accountability. Looking ahead, there is a big focus on leveraging generative AI and LLMs in Block’s overall data and AI strategy, and Unity Catalog will play an important role in delivering on that strategy.

Impact #1

The migration to the Databricks platform and the implementation of Unity Catalog brought about transformative benefits for Block. It facilitated the creation of a dynamic “marketplace” for data exchange between different business units, fostering collaboration and knowledge sharing. The operational efficiency of data sharing improved significantly, reducing the time required from days to seconds. Unity Catalog also improved the ease of IAM policy management for Block, streamlining the process by configuring sub-group level access permissions in a single location. Moreover, it empowered Block to attribute data ownership more easily and decentralize decision-making, enhancing overall data governance and accountability. Looking ahead, there is a big focus on leveraging generative AI and LLMs in Block’s overall data and AI strategy, and Unity Catalog will play an important role in delivering on that strategy.

Benefit #1	Block managed to reduce compute costs by an impressive 12x
Benefit #2	Reduced data egress costs associated with cross-cloud provider data transfer by 20%
Benefit #3	Block manages 2PB of data on the Databricks Lakehouse Platform and anticipates reaching 4PB by year-end

Benefit #1

Block managed to reduce compute costs by an impressive 12x

Benefit #2

Reduced data egress costs associated with cross-cloud provider data transfer by 20%

Benefit #3

Block manages 2PB of data on the Databricks Lakehouse Platform and anticipates reaching 4PB by year-end

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Overview

Block's Transformation of Financial Services with Databricks

Operational Impact

Quantitative Benefit