Pyspark Scenarios 18 : How to Handle Bad Data in pyspark dataframe using pyspark schema #pyspark

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Complete Pyspark Real Time Scenarios Videos.

Pyspark Scenarios 1: How to create partition by month and year in pyspark
pyspark scenarios 2 : how to read variable number of columns data in pyspark dataframe #pyspark
Pyspark Scenarios 3 : how to skip first few rows from data file in pyspark
Pyspark Scenarios 4 : how to remove duplicate rows in pyspark dataframe #pyspark #Databricks
Pyspark Scenarios 5 : how read all files from nested folder in pySpark dataframe
Pyspark Scenarios 6 How to Get no of rows from each file in pyspark dataframe
Pyspark Scenarios 7 : how to get no of rows at each partition in pyspark dataframe
Pyspark Scenarios 8: How to add Sequence generated surrogate key as a column in dataframe.
Pyspark Scenarios 9 : How to get Individual column wise null records count
Pyspark Scenarios 10:Why we should not use crc32 for Surrogate Keys Generation?
Pyspark Scenarios 11 : how to handle double delimiter or multi delimiters in pyspark
Pyspark Scenarios 12 : how to get 53 week number years in pyspark extract 53rd week number in spark
Pyspark Scenarios 13 : how to handle complex json data file in pyspark
Pyspark Scenarios 14 : How to implement Multiprocessing in Azure Databricks
Pyspark Scenarios 15 : how to take table ddl backup in databricks
Pyspark Scenarios 16: Convert pyspark string to date format issue dd-mm-yy old format
Pyspark Scenarios 17 : How to handle duplicate column errors in delta table
Pyspark Scenarios 18 : How to Handle Bad Data in pyspark dataframe using pyspark schema
Pyspark Scenarios 19 : difference between #OrderBy #Sort and #sortWithinPartitions Transformations
Pyspark Scenarios 20 : difference between coalesce and repartition in pyspark #coalesce #repartition
Pyspark Scenarios 21 : Dynamically processing complex json file in pyspark #complexjson #databricks
Pyspark Scenarios 22 : How To create data files based on the number of rows in PySpark #pyspark

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which role is most likely to use azure data factory to define a data pipeline for an etl process?
broadcast variable in spark
which one of the following tasks is the responsibility of a database administrator?
google colab
case class in scala
pyspark documentation
spark architecture
window function in sql
which single service would you use to implement data pipelines, sql analytics, and spark analytics?
apache spark architecture
hadoop vs spark
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[arshiyakub]

Thank you so much for the video on this. I have been searching for this for a long time and finally got what I needed from this video.

[anandattagasam]

Thanks for your brief explaination, i would go with 4th option (BadRecords path) instead of 5th (ColumnNnamedBadRecords).

[tusharhatwar]

This channel is Goldmine for Pyspark Data engineers.

[manjulakumarisammidi]

instead of caching the dataframe @14:17 defining bad_data_df before good_data_df will also work, just another approach. Thanks for the video sir.

[jobiquirobi]

Just find out your tutoriales, they look pretty nice, thank you!

[mesukanya]

Thank you so much... very well explained :)

[ketanmehta]

Excellent ! Clearly explained each and every option to load the data. @TeckLake Can we use this option with the JSON data as well?

[mohitupadhayay]

Can we do the same for XML and JSON files?

[bharathsai]

Permissive mode is not detecting malformed date types i mean if we have date as 2013-02-30 spark read in permissive mode is not detecting this as bad data

[srijitachaturvedi]

Is this approach works while reading json data instead of csvs?

[saisaranv]

Hi TechLake Team, thanks for the wonderful video and helped a lot. can you please help me with 2 errors which am facing right now : 1. "cannot cast string into integer type" even after specific data schema defined 2. complex json flattening (i had gone through video 13 but my data is too complex in nature to flatten). appreciated in help please

[hannawg]

Hi Sir, Do you provide training Azure ADB ADF ?

[chriskathumbi]

Hello, good video.
I have a question concerning spark. When I use local data like parquet and csv and make a tempview or or just normal spark, and try to use distinct/group by or window functions, I get an error and I've seen this on my windows/linux and docker container. What could be causing this?

[mohitupadhayay]

Still we could not find the proper reason why the records went into corrupt when the column are very huge

[Ameem-rwir]

bro, thanks for your inputs. can you please help me how to handle this?

Expected output
empid, fname, lname, sal, deptid
1, mohan, kumar, 5000, 100
2, karan, varadan, 3489, 101
3, kavitha, gandan, 6000, 102