Full Course - Spark / PySpark For Industry | Hindi - Industry Practices for Cache and Persist | Pyspark

01:11 – Real Life examples 03:31 – Technical example in PySpark (Hands-on) 07:55 – Where cached DFs & RDDs are stored in PySpark 10:56 – Important terminologies for PySpark caching 12:34 – When to use caching in PySpark 16:20 – Can PySpark caching store fraction of DF or RDD? 21:50 – StorageLevel in PySpark caching 27:16 – Serialization / deserialization in PySpark caching 40:05 – Cache vs Persist, industry practices in PySpark 47:46 LRU cache in Spark (continuation on cache/persist) Want to know when to use `.cache()` vs `.persist()` in PySpark like the pros do? This video digs deep into the differences, trade‑offs, and real‑world industry practices for optimizing big data pipelines. You’ll learn: What `.cache()` actually does in Spark / PySpark (default storage level, memory‑only etc.) :contentReference[oaicite:0]{index=0} What `.persist()` allows (different storage levels: MEMORY_AND_DISK, MEMORY_ONLY_SER, serialisation, fault tolerance) :contentReference[oaicite:1]{index=1} How the two differ in performance, memory usage, and durability, especially when datasets are large or reused in multiple operations :contentReference[oaicite:2]{index=2} Best practices from industry: when to cache, when to persist, when neither is needed, and how to manage/unpersist to free resources :contentReference[oaicite:3]{index=3} Examples in PySpark showing usage and benchmarks If you build Spark or PySpark pipelines, you’ll get tips you can directly apply to make your jobs faster and more resource efficient. Leave a comment: which storage level you struggle with (memory vs disk vs serialized) & I’ll maybe do a deep dive. Subscribe for more Spark internals, performance engineering & real industry case studies! #PySpark #SparkCache #SparkPersist #BigDataOptimization #SparkPerformance #DataEngineering #SparkTips #CacheVsPersist