PySpark Distributed Computing & Practice
Master enterprise distributed data processing. Write high-performance DataFrame transformations, construct complex window pipelines, optimize broadcast joins, and solve data skew.
Every problem includes a side-by-side SQL translation so you can map relational concepts directly into distributed PySpark DataFrame APIs.
PySpark Study Guides & Practice Material
Production reference cards covering core architecture, window expressions, skew mitigation, and SQL translation.
PySpark Architecture & SparkSession Deep Dive
Driver vs Executors, DAG scheduling, Stage boundaries, Catalyst Optimizer phases, Tungsten whole-stage code-gen, and Adaptive Query Execution (AQE).
Lazy Evaluation, Narrow vs Wide Transforms
How lazy evaluation builds the DAG, the difference between narrow and wide transformations, shuffle boundaries, and when Actions trigger execution.
Advanced Window Functions & Frame Specifications
Window specs, frame boundaries (ROWS vs RANGE), ranking functions, cumulative aggregations, lead/lag access, percentiles, and running differences.
Join Strategies, Broadcast & Data Skew Salting
When Spark chooses each join strategy, broadcast thresholds, semi/anti joins, and the key-salting technique for handling skewed distributions.
SQL ↔ PySpark Rosetta Stone Cheatsheet
Direct mapping between SQL statements and their exact PySpark DataFrame equivalents — covering SELECT, WHERE, GROUP BY HAVING, CASE WHEN, CTEs, and window queries.
Partitioning, Caching & Memory Management
repartition vs coalesce, cache vs persist storage levels, partition pruning, bucketing, and preventing the small-files problem.
Complex Types: Structs, Arrays, Maps & JSON
Parse nested JSON, explode arrays, access struct fields, work with MapType columns, and flatten deeply nested API responses into tabular format.
Data Quality Patterns: Dedup, SCD Type 2 & Validation
Production deduplication using row_number, SCD Type 2 merge logic with effective dating, data quality assertions, and idempotent write patterns.
UDFs, Pandas UDFs & Vectorized Arrow Processing
When to use built-in functions, when you must fall back to UDFs, and how Pandas UDFs (Arrow-optimized) offer 10-100x speedup over row-at-a-time Python UDFs.
Structured Streaming: Real-Time Pipelines
Micro-batch vs continuous processing, watermarks, output modes (append/update/complete), window aggregations, and exactly-once semantics with checkpointing.
Delta Lake: ACID Transactions, Time Travel & MERGE
ACID guarantees on object storage, schema enforcement, schema evolution, time-travel queries, MERGE INTO for upserts, OPTIMIZE for compaction, and Z-ORDER for multi-dimensional clustering.
PySpark Interview Patterns & Common Anti-Patterns
The top patterns and gotchas interviewers test: collect() misuse, UDF overhead, shuffle explosions, skew detection, and optimal partition sizing.
PySpark Practice Problems
65 problems available