Big Data Querying with SQL and Cloud Storage

Managing massive datasets requires moving beyond traditional databases into scalable cloud storage and distributed systems. This text-based course guides you through the fundamentals of structuring, storing, and querying big data efficiently. By completing this course, you will transition from writing basic database queries to managing large-scale data pipelines. You will understand how to select optimal file formats, configure schemas, and execute distributed SQL queries to extract valuable insights from high-volume data. What you'll learn: - Understand the core architectures of cloud storage, distributed file systems, and data lakes. - Apply structure to unstructured or semi-structured datasets using schemas and metadata. - Query massive datasets using distributed SQL engines like Apache Hive and Apache Impala. - Optimize query performance by choosing the right file formats, such as Parquet and ORC, and partitioning strategies. - Configure data storage types and compression methods to balance storage cost and query speed. The curriculum begins with foundational big data concepts and storage architectures before moving into practical SQL querying techniques and performance optimization strategies. You will progress through clear written explanations, architectural breakdowns, and practical query exercises. This course is designed for aspiring data analysts, database administrators, and software developers who are new to big data and want to learn distributed querying. No prior cloud infrastructure experience is required. Start building your big data skills today and learn how to query datasets at scale.