Apache Spark 3 and Big Data Engineering with Scala

Modern data demands require tools that can handle massive datasets across distributed clusters with speed and reliability. This course provides a clear path for programmers to transition into big data engineering by mastering the core components of the Spark ecosystem. You will move from understanding basic data structures to building complex, distributed processing pipelines that can handle terabytes of information. By the end of this course, you will have a solid mental model of how distributed systems operate and the practical skills to write production-ready Scala code for data-intensive tasks. You will be able to navigate the trade-offs between different Spark APIs and implement optimizations that ensure your applications run efficiently at scale. What you'll learn: - Understand the architecture of Spark and how it manages distributed computing across a cluster. - Process structured data efficiently using the expressive Spark SQL and DataFrame APIs. - Apply type-safe data transformations with Datasets for robust and maintainable application development. - Manage low-level data abstractions and custom logic using Resilient Distributed Datasets (RDDs). - Optimize application performance using modern techniques like Adaptive Query Execution and efficient shuffling. - Implement complex data filtering, aggregation, and joining strategies for large-scale datasets. - Practice debugging and monitoring distributed applications to ensure data integrity and performance. The course begins with foundational definitions of distributed computing and Spark internals before progressing through structured data processing and low-level optimizations. Each module focuses on reading and analyzing code implementations to ensure you can apply these concepts to real-world scenarios. This course is designed for developers who have a basic grasp of Scala and want to enter the world of big data. No prior experience with Spark or distributed systems is required. Start building high-performance data solutions with Spark and Scala today.