Agivant's Scalable Data Analytics Platform using Databricks Lakehouse Architectural Approach
Agivant's AI Innovation Lab Has a Rich Set of Expertise in Implementing Complex Data Engineering Services Using Databricks Lakehouse Architectural Strategy
Partitioning and clustering: Use appropriate partitioning and clustering strategies to optimize data retrieval and minimize the amount of data processed during queries
Change data capture (CDC): Utilize CDC techniques to capture incremental changes and update the Lakehouse accordingly.
Data compression: Apply appropriate data compression techniques to reduce storage costs and improve query performance.
Data lineage: Maintain a comprehensive record of data lineage to track data transformations and ensure data traceability.
Value to customer
- Provides unified Analytics Platform for data engineering, data science, and analytics to improve collaboration and quality of insights
- Implementation expertise in Microsoft Cloud Scale Analytics Reference Architecture using Databrick health lakehouse and OMOP standard data model
- Healthcare organizations deal with large and diverse datasets. A health lakehouse powered by Apache Spark offers scalability and high-performance processing capabilities. It can efficiently handle the volume, velocity, and variety of healthcare data, ensuring timely analysis and insights.
- Robust security features to protect sensitive data and ensure compliance with data privacy regulations. It provides encryption at rest and in transit, fine-grained access controls, auditing capabilities, and identity and access management (IAM) systems integration.
- Collaborative features help share code snippets and leverage version control to ensure seamless collaboration and maximize productivity.
- Lakehouse architecture supports both real-time and batch processing. It can handle streaming data ingestion, enabling real-time analytics and insights. At the same time, it can process batch data, allowing for comprehensive and historical analysis.