Executive Summary

BerryBox, an innovative insure-tech startup, successfully implemented Databrain's analytics solution on Amazon EKS, reducing their analytics rollout time from an estimated 3+ months to just 3 weeks. This case study explores how BerryBox overcame significant challenges with their previous Power BI implementation, resulting in ~US $250K in cost savings and freeing up 6 months of valuable development time for core product work.

Before implementing Databrain, BerryBox attempted to embed Power BI dashboards into their HR and broker SaaS app. This approach quickly became problematic, creating multiple roadblocks that hampered development and threatened product timelines.

Four Critical Roadblocks

1. Cumbersome Multi-Source Data Integration
   Connecting Postgres, Stripe, and vendor analytics into Power BI required complex in-memory stitching and brittle ETL workflows.
2. Risky Deployment Processes
   Each environment needed fresh gateways and manual credential swaps, making every release potentially disruptive to production systems.
3. Steep Learning Curve
   Analysts struggled with DAX, spending days translating underwriting logic, while new hires waited weeks before they could contribute meaningfully.
4. Support Dependencies
   Vendor ticket queues turned simple schema adjustments into major release blockers.

These challenges threatened BerryBox's ability to deliver timely analytics to their customers and diverted engineering resources away from their core mission of reinventing insurance products.

After evaluating alternatives, BerryBox selected Databrain's EKS-based analytics platform to address their specific needs. This modern approach aligned perfectly with their existing tech stack and development workflows.

Key Capabilities That Delivered Results

Implementation: From Kickoff to Production in Record Time

BerryBox's implementation journey with Databrain followed a streamlined process that enabled them to move from initial setup to production in just three weeks:

Week 1: Foundation and Data Connection

• Deployment using Databrain's Elastic Kubernetes Service (EKS) solution
• Connected distinct data sources across their infrastructure
• Established security parameters and access controls

Week 2: Model Development and Testing

• Created initial data models using familiar SQL and Python
• Set up staging and QA environments for testing
• Conducted validation with actual customer data

Week 3: Final Testing and Production Launch

• Refined dashboards based on stakeholder feedback
• Implemented row-level security for broker-specific views
• Deployed to production with zero downtime

Throughout the process, BerryBox's team leveraged Databrain's white-glove support through daily Slack huddles, ensuring that any edge cases or technical challenges were quickly addressed.

The implementation of Databrain delivered immediate and significant results across multiple dimensions:

Speed to Market

• 3-week launch: Complete implementation from kick-off to live customer dashboards
• 90% faster: Compared to the 3+ months projected for their Power BI implementation

Cost Savings

• US $250,000 saved in avoided BI engineering hires and license costs
• Reduced cloud spending after retiring complex ETL pipelines

Developer Productivity

• 8 months of internal development effort redirected to core product work
• Engineering focus restored to innovative insurance products rather than BI maintenance

Enhanced Analytics Capabilities

• True self-service analytics: Underwriters now build ad-hoc views and promote to production via Git-style versioning
• Independent problem-solving: Business users answer broker questions without engineering assistance