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 modern data platforms
• Spark architecture deep-dive: drivers, executors, cluster managers, lazy evaluation, DAGs, and execution planning
• Distinguishing between RDD and DataFrame APIs and determining when to apply each
• Creating and configuring SparkSession and understanding core application settings

Module 2: PySpark DataFrames

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

Module 3: Kubernetes & Docker Fundamentals for Spark Users

• Conceptual view for cluster consumers (not admins) on how Docker images and Kubernetes pods map to Spark driver and executor processes
• What happens when a Spark job runs on Kubernetes: pod scheduling, resource requests/limits, and mapping session settings
• Distinguishing between user-controlled Spark session parameters and platform-team cluster provisioning, and why this boundary is critical for tuning
• Hands-on exercise: Inspect a live Spark-on-Kubernetes job in the sandbox to identify driver and executor pods

Module 4: Spark Session Configuration on Kubernetes — Deep Dive

• Executor count vs. size: analyzing memory-versus-cores trade-offs and reasoning about optimal configurations
• Driver and executor memory allocation, overhead, and how these translate into pod resources
• Dynamic allocation: behavior on Kubernetes, scenarios where it saves resources, and when it does not
• Hands-on exercise: Execute the same job under varying executor and core configurations to compare runtime and resource utilization

Module 5: Scaling Behaviour & Cost Optimisation

• Impact of adding or removing nodes on job behavior, completion time, and resource consumption
• Comparing many small executors versus fewer large ones: analyzing performance and cost trade-offs
• Shuffle behavior and partition tuning, including estimating the cost impact of specific configuration choices
• Hands-on exercise: Scale the sandbox cluster from five to ten nodes and monitor the effect on job completion and resource usage

Module 6: Efficient Data Ingestion & Partitioning

• The small-files problem: why sources split across many 1–5 MB Parquet files degrade performance and distort partitioning
• Strategies for repartitioning and coalescing data
• Controlling partition sizes during both read and write operations
• Writing Parquet efficiently to prevent the creation of small-file issues downstream
• Hands-on exercise: Load a dataset composed of many small Parquet partitions, apply various repartitioning strategies, and compare performance pre- and post-optimization

Module 7: Pandas Memory Management & Failure Diagnosis

• Root causes of out-of-memory errors in Pandas and recognizing their symptoms
• Applying memory-efficient conversion patterns between Spark and Pandas
• Preventing memory blow-ups when writing large datasets to CSV
• Utilizing chunked processing and dtype optimization for resource-constrained environments
• Hands-on exercise: Reproduce a common Pandas OOM 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: Refactor a Pandas-heavy transformation into 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
• Optimization considerations specific to machine-learning workloads running on the same cluster
• Implementing 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 simple model training, with participants tuning the Spark configuration independently