Lake House Ingestion Solution
AI Ready Architecture
on AWS
Transform Data Management with phData’s AI-Enabled Common Ingestion Framework
A well-architected framework is core to the success of any AI application. phData’s Common Ingestion Framework provides repeatable templates for centralizing, cataloging, and governing structured and unstructured data for use in business-driven decisions and AI.Â
Unstructured data is now among the most sought-after assets for training and fine-tuning LLMs and AI Applications. Lakehouse architecture enables unstructured data to exist alongside structured and semi-structured data to create a centralized storage and data access layer for all AWS Services.
Our Common Ingestion Framework Unlocks Data for Every Business Case:
- Secure and govern data using Glue Catalog and DataZone
- Ensure production grade observability and monitoring with AWS CloudWatch
- Future-proof architecture for AI Workloads in SageMaker Unified Studio
- Enable self-service analytics and data collaboration
How it Works
The Common Ingestion Framework program starts with discovering what data sources exist, assigning value, organizing domains, and determining priority. Our expert AWS architects then design how common datasets will be centralized into an AWS Lakehouse, enabling interoperability across various AWS services. Our engineers deploy templated Spark-based Glue or EMR jobs and make the data discoverable in DataZone and Glue Catalog. Finally, CloudWatch collects and monitors overall pipeline observability and metrics.
Why phData?
- Proven and repeatable architecture and code to accelerate AWS.
- Lakehouse data migrations End-to-end ownership of discovery, strategy, and execution.
- Support from highly knowledgeable, AWS certified architects and engineers.
Reach out to phData today to learn more about implementing an AWS Common Ingestion Framework.
