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Cloud Data Platform Modernization for Scalable Content Recommendation Engine
  1. case
  2. Cloud Data Platform Modernization for Scalable Content Recommendation Engine

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Cloud Data Platform Modernization for Scalable Content Recommendation Engine

kitrum.com
Media
eCommerce
Information technology

Legacy Infrastructure Limitations

The organization faces significant challenges with its on-premises Hadoop-based data infrastructure, including lack of access control, scalability limitations, high maintenance costs, and inability to process massive datasets for real-time recommendations. The legacy system hinders data democratization and operational efficiency while requiring complex governance compliance.

About the Client

Digital content subscription platform offering books, audiobooks, and publications across multiple devices with personalized recommendation capabilities

Modernization Goals

  • Migrate from Hadoop to cloud-native Databricks platform on AWS
  • Implement scalable data lakehouse architecture
  • Establish secure multi-user data environment
  • Reduce infrastructure maintenance costs by 40%
  • Improve data pipeline development velocity by 60%

Core System Capabilities

  • Automated data migration from Hadoop to Databricks on AWS
  • Delta Lake implementation for transactional data management
  • Apache Airflow-based pipeline orchestration
  • Role-based access control (RBAC) for data scientists
  • Audit-compliant data versioning and change tracking

Technology Stack

Databricks
AWS
Apache Airflow
Delta Lake
Apache Spark

System Integrations

  • AWS S3 for storage
  • AWS IAM for authentication
  • Existing Ruby-based analytics tools

Operational Requirements

  • Horizontal scalability to handle petabyte-scale datasets
  • 99.95% platform availability SLA
  • End-to-end data encryption with AWS KMS
  • Automated pipeline monitoring and alerting
  • Cost-optimized resource allocation with AWS Spot instances

Business Transformation Outcomes

The modernized cloud platform will reduce infrastructure complexity by 70%, enable real-time content recommendation capabilities, and support 3x faster data pipeline development. Implementation of Databricks' lakehouse architecture will unify data warehouses and data lakes while achieving $250K+ annual savings through optimized cloud resource utilization and reduced maintenance overhead.

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