Scalable Data & AI Foundation

Organizations can dramatically accelerate their data lake and lakehouse implementations while simplifying ongoing maintenance and scalability through automation. By modernizing data operations and eliminating manual processes, businesses can unlock faster time to value and reduce operational overhead, enabling teams to focus on driving innovation rather than managing infrastructure.

This module delivers comprehensive capabilities across the full data lifecycle with built-in automation for ongoing operations. Whether you're implementing a medallion data lake using Amazon Simple Storage Service (S3), a Lakehouse combining Amazon S3 with Amazon Redshift, transactional workloads with Amazon S3 tables, the solution automates the implementation of scalable storage across multiple accounts and regions. Built-in features include versioning, encryption, lifecycle policies , orchestrator for data processing jobs, automated AWS Glue Crawlers for continuous data discovery and technical cataloging, configurable AWS Glue Data Quality checks, along with integrated consumption tools like Amazon Athena and Amazon QuickSight.

By establishing Data and AI foundation with our solution, every team member can work more effectively: Data Engineers benefit from automated data pipelines and quality controls, Data Architects gain standardized and governed data architectures, Analytics Teams get self-service access to trusted data, and Platform Teams can efficiently manage and scale the data infrastructure.

Through this comprehensive AWS Professional Services engagement, combined with our engineered solution Data Innovation Workbench (DIW), we help you deploy and maintain these modern data foundation faster compared to building the solution from scratch.

Upon completion, your organization is better positioned to have a standardized, well-architected, AWS security-approved Data and AI foundation that enables immediate implementation of various use cases such as data analytics, business intelligence, AI/ML modeling, and data science workloads. This foundation continues to optimize and improve data operations automatically, driving sustained business outcomes and continuously improving operational efficiency.