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
• Exploring Spark architecture: drivers, executors, cluster managers, lazy evaluation, DAGs, and execution planning
• Distinguishing between RDD and DataFrame APIs and determining when to apply each method
• Creating and configuring SparkSession while understanding the fundamentals of application configuration

Module 2: PySpark DataFrames

• Reading and writing data from enterprise sources and formats: CSV, JSON, Parquet, and Delta
• Working with 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

• 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 a Spark job running on Kubernetes: pod scheduling, resource requests and limits, and the mapping of session settings to these resources
• Understanding the distinction between controls available via Spark session parameters and those managed by the platform team through cluster provisioning — and why this boundary is critical for tuning
• Hands-on exercise: Inspect a live Spark-on-Kubernetes job in the sandbox cluster to identify driver and executor pods

Module 4: Spark Session Configuration on Kubernetes — Deep Dive

• Understanding the trade-offs between executor count and executor size: memory versus cores and how to reason about them
• Driver and executor memory allocation, memory overhead, and how these factors translate into pod resources
• Dynamic allocation: its behaviour on Kubernetes, when it conserves resources, and when it may not
• Hands-on exercise: Execute the same job under varying executor and core configurations to compare runtime and resource usage

Module 5: Scaling Behaviour & Cost Optimisation

• Understanding how adding or removing nodes alters job behaviour, completion time, and resource consumption
• Comparing the performance and cost trade-offs between many small executors and fewer large ones
• Shuffle behaviour and shuffle partition tuning, including estimating the cost impact of configuration choices
• Hands-on exercise: Scale the sandbox cluster from five to ten nodes and observe the impact on job completion time and resource consumption

Module 6: Efficient Data Ingestion & Partitioning

• Understanding the small-files problem: why sources split across many 1–5 MB Parquet files degrade performance and disrupt partitioning
• Repartitioning and coalescing strategies
• Controlling partition sizes during both read and write operations
• Writing Parquet efficiently to prevent the recurrence of the small-files problem downstream
• Hands-on exercise: Load a dataset composed of numerous small Parquet partitions, apply different repartitioning strategies, and compare performance before and after optimisation

Module 7: Pandas Memory Management & Failure Diagnosis

• Understanding the root causes of out-of-memory errors in Pandas and recognising the associated symptoms
• Applying memory-efficient conversion patterns between Spark and Pandas
• Avoiding memory spikes when writing large datasets to CSV
• Utilising chunked processing and dtype optimisation for constrained environments
• Hands-on exercise: Reproduce a typical Pandas out-of-memory scenario and resolve it using chunking and dtype optimisation

Module 8: Polars as a Complementary Tool

• Positioning Polars relative to Pandas: performance characteristics, lazy evaluation, and memory behaviour
• 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 optimisation 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 tuning the Spark configuration themselves