Cloud Computing for Big Data guides learners through the essential cloud concepts that power modern data processing, helping them understand how cloud models, deployment options, and platform capabilities support large-scale workloads. The module explores Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) within real-world big data scenarios, comparing these approaches to traditional on-premises solutions to highlight the cost, scalability, and operational trade-offs. Learners investigate cloud-native features, including elasticity, managed services, global distribution, and automated scaling, and then apply these concepts to evaluate workload requirements and design effective architectures. Through videos, readings, hands-on labs, and coach-led discussions, the module equips learners to make informed decisions about cloud adoption and build scalable, resilient big data solutions.

Microsoft Azure Platform for Big Data equips learners with the practical skills needed to work confidently within Microsoft’s cloud ecosystem for large-scale data solutions. The module introduces key Azure services, demonstrates how to navigate the Azure portal, and guides learners through creating and managing resources that support big data workloads. Learners explore major Microsoft tools, including Azure Synapse Analytics, Azure Data Lake Storage, Azure Data Factory, and Microsoft Fabric, building an understanding of how these services connect to form an integrated analytics platform. Through hands-on labs, guided videos, and scenario-based activities, this module helps learners apply core Azure capabilities, effectively organize cloud resources, and select the right services to meet real-world big data requirements.

Introduction to Azure Databricks and Clusters helps learners build a practical understanding of distributed computing and the core technologies that power large-scale data processing. The module introduces the principles of cluster computing, demonstrating how distributed systems allocate workloads across multiple machines to enhance speed, resilience, and efficiency. Learners explore Azure Databricks as a unified analytics platform, set up workspaces, run basic PySpark operations, and learn how Databricks integrates with Azure services. The module also guides learners through configuring and managing clusters, selecting compute options, applying auto-scaling, and optimizing performance and cost. Through hands-on labs, code exercises, demonstrations, and scenario-based activities, learners gain the foundational skills needed to work confidently with Databricks and cluster-based big data solutions.

Cost Management and Cloud Provider Comparisons gives learners the tools to understand, predict, and optimize the costs of big data workloads in the cloud. The module breaks down Azure’s pricing structures for compute, storage, and consumption-based models, while teaching learners how to estimate expenses using calculators and automation tools. It also provides a clear framework for comparing pricing across Azure, AWS, and Google Cloud, highlighting service equivalencies, hidden costs, and strategic considerations that extend beyond price alone. Learners explore practical optimization techniques—such as auto-scaling, lifecycle policies, and reserved instance planning—and apply them to real scenarios to create cost-effective designs. Through demonstrations, hands-on labs, and structured analysis activities, this module helps learners build the confidence and skill set needed to manage cloud spend responsibly and design efficient big data solutions.