Efficient Data Processing on Kubernetes with PySpark, Pandas & Polars Training Course

Module 1: Foundations of Big Data & Spark

• Overview of the Big Data ecosystem and Spark's role in modern data platforms
• Exploring Spark architecture: drivers, executors, cluster managers, lazy evaluation, DAGs, and execution planning
• Contrasting RDD and DataFrame APIs and determining the appropriate use case for each
• Creating and configuring SparkSession with a focus on application configuration fundamentals

Module 2: PySpark DataFrames

• Reading and writing data from enterprise sources and formats: CSV, JSON, Parquet, and Delta
• Mastering PySpark DataFrames: transformations, actions, column expressions, filtering, joins, and aggregations
• Implementing advanced operations including window functions, timestamp handling, and nested data manipulation
• Applying data quality checks and writing reusable, maintainable PySpark code

Module 3: Kubernetes & Docker Basics for Spark Users

• Conceptual understanding of the relationship between Docker images, Kubernetes pods, and Spark driver/executor processes — focused on cluster consumers rather than administrators
• Understanding the lifecycle of a Spark job on Kubernetes: pod scheduling, resource requests/limits, and the mapping of session settings
• Distinguishing between controls available via Spark session parameters and those managed by the platform team during cluster provisioning — and why this boundary is critical for tuning
• Hands-on exercise: Inspect a live Spark-on-Kubernetes job in a sandbox cluster to identify driver and executor pods

Module 4: In-Depth Spark Session Configuration on Kubernetes

• Analyzing the trade-offs between executor count and executor size, specifically regarding memory versus cores
• Managing driver and executor memory allocation, overhead, and the translation of these settings into pod resources
• Exploring dynamic allocation: its behavior on Kubernetes, scenarios where it saves resources, and limitations
• Hands-on exercise: Execute the same job with varying executor and core configurations to compare runtime and resource consumption

Module 5: Scaling Dynamics & Cost Optimization

• Understanding how node addition or removal impacts job behavior, completion times, and resource usage
• Evaluating the performance and cost implications of using many small executors versus fewer large ones
• Tuning shuffle behavior and partitions, including estimating the cost impact of specific configuration choices
• Hands-on exercise: Scale the sandbox cluster from five to ten nodes to observe effects on job completion time and resource consumption

Module 6: Efficient Data Ingestion & Partitioning

• Addressing the small-files problem: understanding how sources split into many 1–5 MB Parquet files degrade performance and distort partitioning
• Strategies for repartitioning and coalescing data
• Controlling partition sizes during read and write operations
• Writing Parquet efficiently to prevent the recurrence of small-file issues downstream
• Hands-on exercise: Load a dataset consisting of numerous small Parquet partitions, apply various repartitioning strategies, and compare performance before and after optimization

Module 7: Pandas Memory Management & Failure Diagnosis

• Identifying root causes of out-of-memory errors in Pandas and recognizing key symptoms
• Implementing memory-efficient conversion patterns between Spark and Pandas
• Preventing memory spikes when writing large datasets to CSV
• Utilizing chunked processing and dtype optimization for memory-constrained environments
• Hands-on exercise: Reproduce a typical Pandas out-of-memory scenario and resolve it using chunking and dtype optimization

Module 8: Polars as a Complementary Tool

• Positioning Polars relative to Pandas: analyzing performance characteristics, lazy evaluation, and memory behavior
• Understanding where Polars fits alongside PySpark and Pandas in modern cloud data stacks and migration roadmaps, including AWS environments
• Hands-on exercise: Refactor a Pandas-heavy transformation in Polars and compare memory usage and processing speed

Module 9: Applying Optimization to ETL & ML Workloads

• Integrating configuration, partitioning, and memory management principles into a realistic ETL pipeline
• Considering optimization specifics for machine learning workloads running on the same cluster
• Applying a practical tuning workflow to systematically diagnose cost and performance issues
• Hands-on exercise: Complete an end-to-end mini-project involving data loading, transformation, and basic model training, with participants tuning the Spark configuration independently