Flexibility is key for data stored across clouds, devices, and formats. This module introduces cross-platform compatibility and its role in building scalable, secure, and future-ready AI systems. Interoperability allows for seamless data exchange, avoids vendor lock-in, and leverages the strengths of diverse cloud providers and AI models. The SECURE framework will guide you through standardizing formats, unifying access, and evolving systems through monitoring. The module also introduces technologies like Docker, Kubernetes, APIs, and Model Context Protocol for portability and integration. You'll discuss how strategies around managing cost and hybrid environments help ensure organizations stay resilient. By the end, you’ll be equipped to build adaptable, secure, and optimized AI systems, regardless of where your data or models reside.

As data volumes grow, organizations must be able to locate, classify, and leverage their data with ease. This module explores how tagging systems transform large and scattered data into organized, searchable assets. You’ll discuss how metadata and tagging improve classification, retrieval, and governance by labeling data based on format, relevance, and sensitivity. Tagging also powers Retrieval-Augmented Generation (RAG), which helps AI deliver accurate, context-aware responses by filtering and contextualizing external data. The module explains how to handle integration, such as combining metadata with vector embeddings and choosing the right vector store for scalability. From data catalogs to hybrid and graph-based search, you’ll recognize how tagging assists in secure, efficient, and robust data systems. By the end, you’ll be able to implement tagging strategies that enhance search precision and unlock deeper insights.

Sophisticated AI systems are able to conduct intelligent and meaningful searches. This module introduces vector stores. These are advanced databases that convert unstructured information into high-dimensional vectors for fast, intelligent retrieval. Explore semantic, hybrid, and multimodal search, which is enhanced by metadata tagging. The module discusses how access control enables secure, compliant data handling. Vector stores enable personalized experiences across industries like e-commerce, healthcare, and entertainment. You’ll evaluate their performance, cost, and effectiveness, and recognize how approximate nearest neighbor algorithms balance speed and accuracy. The module also covers hardware accelerations like GPUs and TPUs that reduce search latency, and indexing strategies such as flat, asynchronous, dynamic, and HNSW that improve scalability and real-time retrieval. By the end, you will be able to comprehend vector-based search systems that are fast, secure, and optimized for growing datasets.

Harnessing Gen AI's full potential requires a unified approach to structured and unstructured data. This module explores the evolution of data systems, from warehouses to lakes and lakehouses, and how each stores, secures, and integrates data. You’ll examine how modern lakehouse formats like Iceberg, Delta, and DuckLake improve scalability, performance, and metadata workflows. There's a spotlight on DuckLake, a lightweight architecture that simplifies metadata management using SQL catalogs and version control. The module discusses how lakehouses integrate with vector stores to support semantic search, hybrid querying, and retrieval-augmented generation. Tools like MongoDB and Databricks are used to deliver enriched, context-aware responses in Gen AI applications. Along with data architecture types, this module will help you comprehend how tabular data forms the foundation for vector databases, and enables hybrid search across diverse data types.

Modern data management is based on principles like real-time processing, automation, governance, and modularity. This module introduces key frameworks, like DAMA-DMBOK, the EDM Council’s DMM, and RDMM, that help organizations build secure, scalable, and responsible data systems. You’ll discuss how these frameworks support governance, maturity assessment, and ethical data practices. The module highlights DuckLake as a unified catalog solution that simplifies metadata management and enables collaborative, SQL-based workflows. It also covers how explainable AI (XAI) promotes transparency and fairness in AI decisions. Security models like Zero Trust Architecture and Data Loss Prevention protect sensitive data across cloud environments. Together, these tools and practices ensure compliance, accountability, and resilience in modern data strategies. By the end, you’ll be ready to apply foundational principles, adopt industry frameworks, and implement secure and ethical AI systems.