Semi-Structured (JSON), Structured and Unstructured Data Analysis with Spark and Python & Spark Performance Tuning
InstructorAmin Karami
Instruction covers critical performance tuning topics including data skewness, memory spill prevention, and Adaptive Query execution.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This slide outlines core PySpark components including RDD, DataFrame, and Spark SQL, alongside the handling of semi-structured, structured, and unstructured data.
This slide outlines the specific development environments used in the course, contrasting a cloud-based platform like Google Colab with a desktop setup using Ubuntu VMware.
This introductory slide presents the instructor Amin Karami, highlighting his academic credentials as a Senior Lecturer and his professional experience as a Big Data Consultant.
The curriculum details Spark's internal execution model, including the distinction between narrow and wide transformations and how jobs, stages, and tasks are managed.
One sampled review suggests that learners engage with real-world data formats such as JSON, structured files, and log files through specific analysis challenges.
The practical component relies on a small number of specific file-type analysis tasks rather than diverse, end-to-end application builds.
Selected from the course's public promotional preview. These images document visible presentation material only; they do not represent the complete paid curriculum.
The curriculum aligns with the stated goal of training data developers through structured file analysis and optimization.