ā01-17-2022 12:24 PM
What would be the best way of loading several files like in a single table to be consumed?
ā01-31-2022 05:36 AM
Yes,
1) Downloaded the files using sh from here https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_<year>-<month>.csv to /mnt
2) Loaded a dataframe with the csv files
3) Stored as a partitioned table
I don“t know if this the best approach, but its working
ā01-18-2022 12:49 PM
New Your Taxi data from your example is already included in your workspace as it is demo dataset.
It is enough to read "yellow" folder and it will read all csvs from there.
If you want to save it as a single file you can do .repartition(1).write.csv(destination_folder).save()
ā01-18-2022 12:56 PM
Great!
ā01-19-2022 07:45 AM
Unfortunately it seems that nytaxi is outdated. there is no records from 2021 and 2020 and 2019 is barely uncomplete
+-----------+------------------+
| 2010| 169001154|
| 2011| 176897208|
| 2015| 146112990|
| 2014| 165114361|
| 2013| 173179759|
| 2012| 178544324|
| 2009| 170896987|
| 2016| 131165043|
| 2017| 113496933|
| 2018| 102803387|
| 2041| 3|
| 2008| 585|
| 2001| 15|
| 2029| 6|
| 2002| 33|
| 2053| 2|
| 2003| 23|
| 2020| 438|
| 2019| 84397753|
| 2037| 1|
+-----------+------------------+
ā01-31-2022 04:04 AM
Thanks Kaniz, I already have the files. I was discussing about the best way to load them
ā01-31-2022 05:36 AM
Yes,
1) Downloaded the files using sh from here https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_<year>-<month>.csv to /mnt
2) Loaded a dataframe with the csv files
3) Stored as a partitioned table
I don“t know if this the best approach, but its working