Let’s Architect! Modern data architectures
Modern data architecture is less about choosing one “best” store and more about matching system behavior to workload shape. Real-time streams, unstructured content, analytics pipelines, and generative AI retrieval paths place different demands on latency, schema flexibility, retention, and access patterns. For architects, the practical challenge is to prevent one platform decision from forcing every use case into the same operational model.
The main architectural trade-off is consolidation versus specialization. A shared cloud data foundation can reduce integration sprawl and improve reuse, but only if teams define clear boundaries for ingestion, curation, serving, and governance. Without those boundaries, the result is often a large but fragile data estate: duplicated datasets, unclear ownership, and pipelines that are difficult to evolve when AI or analytics requirements change.
Storage strategy also becomes an operations decision, not just a capacity decision. Object storage can serve as a durable backbone for raw, intermediate, and training data, but lifecycle policies, retrieval behavior, and cost controls matter as much as throughput. That means infrastructure teams need to think in terms of tiering, access frequency, and downstream dependency impact rather than treating storage as a passive repository.
For generative AI, database and integration choices directly affect answer quality and delivery reliability. Retrieval-augmented workflows depend on timely ingestion, consistent metadata, and interoperability across search, analytics, and application layers. The architectural opportunity is to design for composable data services that support both experimentation and production, while keeping governance, performance, and maintainability visible from the start.
Key considerations when choosing a database for your generative AI applications
This blog focuses on selecting the right database for generative AI applications and provide knowledge that can enhance your understanding, guide your decision making, and ultimately lead to more successful AI projects. Selecting the right database for generative AI applications is not just about storage; it significantly impacts performance, scalability, ease of integration, and overall effectiveness of the AI solution.
Figure 1. Diagram that shows the key steps in a RAG workflow
Strategies for building a data mesh-based enterprise solution on AWS
Adopting a data mesh architecture can enhance an organization’s ability to manage data effectively, leading to improved performance, innovation, and overall business success. In this guidance, you will discover some strategies to build data mesh solutions on AWS.
Figure 2. The data mesh organizes data into domains, where data are seen as quality products to expose for consumption
Optimizing storage price and performance with Amazon S3
Amazon S3 is an object storage service that supports multiple use cases, including data architectures. Big data pipelines can use Amazon S3 to store input, output, and intermediate results. Machine learning systems use Amazon S3 to process application logs and build the datasets both for experimentation and for production model training. Given the importance of the service and the number of use cases that a foundational storage service can support, we want to share best practices, performance optimization, and cost optimization strategies to work with Amazon S3. This video shows how Anthropic designs its architecture around Amazon S3 in their data architecture.
Figure 3. Workloads with predictable patterns often have low retrieval rates for long periods of time after, so we can design to adopt cheaper storage classes for them
How HPE Aruba Supply Chain optimized cost and performance by migrating to an AWS modern data architecture
This is an AWS case study on how HPE Aruba Supply Chain successfully re-architected and deployed their data solution by adopting a modern data architecture on AWS. The new solution has helped Aruba integrate data from multiple sources, along with optimizing their cost, performance, and scalability. This has also allowed the Aruba Supply Chain leadership to receive in-depth and timely insights for better decision-making, thereby elevating the customer experience.
Figure 4. Reference architecture diagram showing HPE Aruba Supply Chain’s architecture, featuring Amazon S3
AWS Modern Data Architecture Immersion Day
This workshop highlights advantage of adopting a modern data architecture on AWS. By integrating the flexibility of a data lake with specialized analytics services, organizations can significantly enhance their data-driven decision-making capabilities. We encourage everyone to explore how this architecture can streamline their analytics processes and support diverse use cases, from real-time insights to advanced machine learning. It’s an excellent opportunity to leverage modern data architecture.
Figure 5. Data architectures are fundamental to power use cases ranging from analytics to machine learning
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