By the end of this course, you'll be able to define what makes data "big," distinguish Big Data from a traditional data warehouse, and map the core layers of a Big Data architecture from ingestion to serving. You'll compare processing approaches like batch, streaming, and Lambda, weigh core technologies such as Hadoop, Spark, and Kafka, and evaluate on-premise, cloud, and hybrid hosting against the principles that matter most: performance, availability, scalability, flexibility, and cost.

Led by a data and AI cloud architect with over a decade of hands-on experience building solutions for companies of all sizes, this course turns an intimidating buzzword into practical, decision-ready knowledge. You'll move beyond theory with a real-world case study, an inside look at cloud services, and a clear view of the business side of Big Data, from building data teams to avoiding the pitfalls that quietly derail projects. Whether you're a manager scoping your first Big Data initiative or a professional strengthening your foundations, you'll finish ready to make confident, informed choices about implementing Big Data in your own organization.