PySpark Professional

Apache Spark Fundamentals : You have a solid understanding of the Apache Spark architecture, its components like Spark Core, Spark SQL, Spark Streaming, MLlib, and Spark GraphX.

Python Programming : You are proficient in Python programming language as PySpark heavily relies on Python APIs for data manipulation, analysis, and processing.

Data Manipulation and Analysis : You are experienced in performing data manipulation tasks such as filtering, transforming, aggregating, and joining large datasets using PySpark DataFrame API or RDDs (Resilient Distributed Datasets).

Spark SQL : You can write SQL queries using Spark SQL for querying structured data and performing analytics operations on DataFrames and tables.

Data Processing Pipelines : You are capable of designing and building end-to-end data processing pipelines using PySpark that can handle various stages of data ingestion, cleaning, transformation, and analysis.

Performance Optimization : You have knowledge of techniques for optimizing PySpark jobs and improving the performance of Spark applications, including partitioning, caching, and tuning the execution settings.

Integration with External Systems : You can integrate PySpark with various data sources and file formats such as HDFS, S3, Hive, Parquet, Avro, JSON, CSV, etc.