20 Data Caching in Spark | Cache vs Persist | Spark Storage Level with Persist |Partial Data Caching

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Video explains - How Spark works with Cache data? What is the difference in Spark Cache vs Persist ? Understand what is the impact of partial caching.

Chapters
00:00 - Introduction
00:29 - Demonstration
03:20 - Spark Cache
09:20 - Spark Storage Level with Persist
12:54 - Cache vs Persist

The series provides a step-by-step guide to learning PySpark, a popular open-source distributed computing framework that is used for big data processing.

New video in every 3 days ❤️

#spark #pyspark #python #dataengineering
  1. Ease With Data
  2. apache spark
  3. big data analytics
  4. big data tutorial
  5. data science
  6. data science course
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Комментарии
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Excellent content in this playlist! Thanks for sharing and keep up the good work 🚀

reslleygabriel
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Great way of explanation.
Just one question at 12:25 you had mentioned by default MEM AND DISK is serilalized. But what we saw from the demo is that default cache MEM AND DISK, data is De-serialized. So I hope its just typo or is my understanding wrong.

satheshkumar
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all 4 buckets will reside inside all 16 partitions ? Is this understanding correct ?

satheshkumar
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Thanks. Your explanation is too good. Keep making such videos.
Also, if possible, make some videos on scenario based interview questions

mohammedshoaib
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Nice job and can you please provide more details on serialized and deserialized when dealing with cache/persist in upcoming lectures ?

sureshraina
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one of the best explanation in depth, Thanks :)
Could you please make a video on "end to end Data engineering" project, from requirement gathering to the deployment.

nishantsoni
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I have one query, Cache() is equal to Only difference in this scenario is that cache() uses deserialized and persist used serialized data. So, if persist is better in terms of data serialization and functionality, what is the use case of using cache over persist ?

sayantabarik
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as already mentioned in a comment, pls make a video on ser/deserialization of the objects

at-cvky
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Consider you have a orders dataframe with 25 million records
now you applied a projection and a filter and cached this dataframe as shown below
orders_df.select("order_id", "order_status").filter("order_status == 'CLOSED'").cache()
Now you execute the below statements...
1) orders_df.select("order_id", "order_status").filter("order_status == 'CLOSED'").count()
2) == 'CLOSED'").select("order_id", "order_status").count()
3) == 'CLOSED'").count()
4) orders_df.select("order_id", "order_status").filter("order_status == 'OPEN'").count()
please answer the below queries...
question 1) what point of time the data is cached (partially/completely) ?
question 2) Which all queries serves your request from the cache, and which all will have to go to the disk. Please explain.

VikasChavan-vc