AWS Pi Day 2025: Uniting Data, Accelerating Analytics & AI Innovation

Every year on March 14, AWS Pi Day highlights AWS innovations that help you manage and work with your data. What began in 2021 as a way to celebrate the fifteenth anniversary of Amazon Simple Storage Service (Amazon S3) has evolved into an event that showcases how cloud technologies are revolutionizing data management, analytics, and AI. This year, AWS Pi Day 2025 has taken a giant leap forward by focusing on accelerating analytics and AI innovation with a unified data foundation on AWS.

A Unified Data Vision for a Changing Landscape

As AI becomes an integral part of enterprise strategies, the convergence of analytics and AI workloads means that organizations need a seamless, integrated experience to access and leverage their data. AWS recognizes this need by introducing a suite of new capabilities that not only enhance your current data workflows but also prepare you for tomorrow’s data challenges—all while aligning closely with advanced cloud storage security solutions.

The Next Generation of Amazon SageMaker: Your Center for Data, Analytics, and AI

At re:Invent 2024, AWS unveiled the next generation of Amazon SageMaker, the all-in-one hub designed to streamline data exploration, big data processing, fast SQL analytics, and machine learning (ML) model development. Central to this evolution is SageMaker Unified Studio, a single development environment that unifies all your tools and data for collaborative work among data scientists, analysts, engineers, and developers. This environment now includes enhanced features such as:

• Integration with Amazon Bedrock: Rapidly prototype and customize generative AI applications using foundation models, along with advanced capabilities like Bedrock Knowledge Bases, Guardrails, Agents, and Flows, all while adhering to responsible AI guidelines.
  
  
• Amazon Q Developer: Now generally available, this AI-powered assistant automates tasks such as writing SQL queries, constructing ETL jobs, and troubleshooting, thereby streamlining development while supporting robust security practices.
  
  
• SageMaker Lakehouse: Unifying data across Amazon S3 data lakes, Amazon Redshift data warehouses, and third-party sources, SageMaker Lakehouse provides centralized management and in-place querying with Apache Iceberg–compatible engines. Its zero-ETL integrations allow seamless data imports from AWS sources such as Amazon Aurora and DynamoDB, as well as popular applications like Salesforce and Facebook Ads, ensuring that data remains secure and compliant as it moves through your pipelines.
  
  

Building a Solid Data Foundation with Amazon S3

Amazon S3 remains the world’s premier platform for building data lakes, managing over 400 trillion objects, exabytes of data, and processing 150 million requests per second. These capabilities drive continuous innovation in data management:

• Amazon S3 Tables: Introduced at re:Invent 2024 and now further integrated with SageMaker Lakehouse, S3 Tables come with built-in support for Apache Iceberg. This integration not only boosts performance—delivering up to threefold faster query throughput and tenfold higher transaction rates—but also enables enhanced security through centralized, fine-grained access controls.
  
  
• Enhanced AWS Management Console: The console now supports streamlined creation, population, and querying of S3 Tables using Amazon Athena, simplifying both data analytics and security oversight.
  
  
• S3 Metadata: Now generally available, this feature automates the discovery and understanding of your S3 data with near real-time metadata updates using object tags. Coupled with a 35 percent reduction in pricing for object tagging, it makes securing and auditing your data both cost-effective and efficient.
  
  
• Expanded Regional Availability: With S3 Tables now rolled out in additional AWS Regions, organizations can ensure that security and compliance measures are uniformly enforced, regardless of geographic location.
  
  

Accelerating GenAI with Integrated Data Pipelines

AWS is empowering organizations to harness the full potential of Generative AI (GenAI) by integrating it into existing data architectures. Improved data transfer capabilities, along with support for vector storage and retrieval in services such as Amazon MemoryDB for Redis, Amazon Neptune, and Amazon DocumentDB, enable rapid deployment of advanced AI models. These advancements not only streamline the process of building AI applications but also synergize with cloud storage security solutions by ensuring that data remains encrypted, monitored, and auditable at every stage of its journey.

Looking Forward

Cloud Storage Security (CSS) is excited about AWS’ innovative initiatives that are making Generative AI both more cost-effective and accessible, empowering organizations to harness productivity gains and deep insights. As more organizations build GenAI applications on AWS storage, it is essential to validate the security of the data used to train models. Visit our website to learn more about how we secure the data used for—and generated by—GenAI applications.