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Problem solution for Spark use case

1) SO I have 60 Millions usa and canada postals

Created dataframe customer_df => good and bad => 60M

2) downloaded some Good postals from internet to filter the customer_df data

Created one dataframe good_df => which is good postals => 1M

3) Perfomed Join between customer_df and good_df wiht zipcode to seperate the good values

filter_df = good zip [url removed, login to view](cus_df,zipcode)

4) Then seperated bad data with the below logic

bad_df = [url removed, login to view](filter_df)

Now still we can filter bad_df with city names

city_df = [url removed, login to view](bad_df,city)

Then did unioin between both df's

total_filter = [url removed, login to view](city_df)

it taking 1.30 mints (used spark with 8 node cluster each node 32 gb => spark-submit driver memory -8g and num-executors - 8 and executor-memory- 8g)

any other technology or any other tool to clean-up the data within 15 to 20 mints(again customer data is 60M

Habilidades: Grande Volume de Dados, Hadoop, Hive, Spark

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( 0 comentários ) United States

ID do Projeto: #14803384

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rutvikparmar

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