1. Understanding the core principles of Spark, including its development history, execution architectures such as Local, Standalone, and On-Yarn, and the core concepts of RDDs, and being able to proficiently apply various operators, including transformations, actions, joins, and sorting operations, as well as mechanisms such as persistence, Checkpoint, and shared variables.
2. Being able to use IDEA for application development, process real-time streaming data with Spark Streaming, and apply DStreams, state management, and window operations, and being able to integrate Spark SQL with external systems such as MySQL, Hive, and Kafka for data reading and writing.
3. Being able to integrate multiple components to complete the full workflow of data cleaning, consumption, and storage, develop logical thinking, debugging, and optimization skills for solving practical big data engineering problems, and independently complete the entire process from environment setup to project implementation.