1. Developing a deep understanding of the design philosophies, architectures, and applicable scenarios of core frameworks such as Hadoop and Spark, not merely API usage, and select appropriate technologies based on problem characteristics such as data volume and real-time requirements.
2. Having the capability to proficiently use Spark for data cleaning, transformation, and analysis, and manage environments and schedule tasks using Shell scripts.
3. Capable of building stable and efficient ETL pipelines to ensure data quality and processing efficiency from the source to downstream systems.
4. Being able to proficiently perform complex operations on large-scale data through APIs, including aggregation, joins, filtering, and window computations.
5. Being able to describe the principles of common machine learning algorithms, such as linear regression, logistic regression, clustering, and decision trees, and implement and apply these models in big data environments using libraries such as Spark MLlib.
6. Being able to use visualization tools (such as Tableau) or programming libraries (such as Matplotlib and ECharts) to clearly and accurately present complex data relationships and analytical results.