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

Module 1: Big Data & Spark Foundations

• Overview of the Big Data ecosystem and Spark's role in contemporary data platforms
• Comprehending Spark architecture: drivers, executors, cluster managers, lazy evaluation, DAGs, and execution planning
• Distinctions between RDD and DataFrame APIs, and scenarios for employing each approach
• Creation and configuration of SparkSession, along with understanding the fundamentals of application settings

Module 2: PySpark DataFrames

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

Module 3: Kubernetes & Docker Fundamentals for Spark Users

• Understanding the relationship between Docker images and Kubernetes pods in relation to Spark driver and executor processes — a conceptual view for cluster consumers rather than administrators
• Understanding the mechanics of Spark jobs on Kubernetes: pod scheduling, resource requests/limits, and mapping session settings to them
• Distinguishing between what is controlled via Spark session parameters versus what is managed by the platform team through cluster provisioning — and why this boundary is critical for tuning
• Hands-on exercise: Examine a running Spark-on-Kubernetes job in the sandbox cluster and identify the driver and executor pods

Module 4: Spark Session Configuration on Kubernetes — Deep Dive

• Understanding the trade-off between executor count and size: memory-versus-cores considerations and reasoning methods
• Memory allocation for drivers and executors, memory overhead, and their translation into pod resources
• Dynamic allocation: its behavior on Kubernetes, scenarios where it conserves resources, and where it does not
• Hands-on exercise: Execute the same job under various executor and core configurations to compare runtime and resource usage

Module 5: Scaling Behaviour & Cost Optimisation

• Understanding how node addition or removal impacts job behavior, completion time, and resource consumption
• Comparing numerous small executors against fewer large ones: analyzing performance and cost trade-offs
• Shuffle behavior and tuning shuffle partitions, including estimating the cost implications of configuration choices
• Hands-on exercise: Scale the sandbox cluster from five to ten nodes and monitor the effect on job completion time and resource consumption

Module 6: Efficient Data Ingestion & Partitioning

• Understanding the small-files problem: why 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 composed of many small Parquet partitions, apply various repartitioning strategies, and compare performance before and after optimization

Module 7: Pandas Memory Management & Failure Diagnosis

• Understanding the root causes of out-of-memory errors in Pandas and recognizing the symptoms
• Applying memory-efficient conversion patterns between Spark and Pandas
• Avoiding memory exhaustion when writing large datasets to CSV
• Utilizing chunked processing and dtype optimization for 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: 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: Rewrite a Pandas-heavy transformation in Polars and compare memory usage and processing speed

Module 9: Applying Optimisation to ETL & ML Workloads

• Applying configuration, partitioning, and memory management principles across a realistic ETL pipeline
• Understanding optimization considerations specific to 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 combining data loading, transformation, and a simple model training step, with participants independently tuning the Spark configuration