ETL Processing on Google Cloud Using Dataflow and BigQuery (Python) Reviews

Done

Carlos L. · Reviewed about 7 years ago

Huzaen J. · Reviewed about 7 years ago

Puneet C. · Reviewed about 7 years ago

Wei Lun K. · Reviewed about 7 years ago

Ankit G. · Reviewed about 7 years ago

Puneet C. · Reviewed about 7 years ago

Chun Yu C. · Reviewed about 7 years ago

Good but little slow

Eli K. · Reviewed about 7 years ago

Sridhar E. · Reviewed about 7 years ago

The lab needs more time. There is just enough time to give a quick read of the python code files. An additional 30 min should be good. The explanation in Step5 & Step6 (see below) just repeat of that from the Step2. Adding a block diagram like in the DataFlow charts would be good. You will now build a Dataflow pipeline with a TextIO source and a BigQueryIO destination to ingest data into BigQuery. More specifically, it will: Ingest the files from GCS. Filter out the header row in the files. Convert the lines read to dictionary objects. Output the rows to BigQuery.

Sreedevi G. · Reviewed about 7 years ago

Punyanuch S. · Reviewed about 7 years ago

Andrew Z. · Reviewed about 7 years ago

Dataflow is long shutting down worker it may longer than 3 min calculate to 50% of all processes that's very inefficiency.

Thatchapoom T. · Reviewed about 7 years ago

Graciela G. · Reviewed about 7 years ago

Jorge F. · Reviewed about 7 years ago

Nicholas F. · Reviewed about 7 years ago

Dmytro R. · Reviewed about 7 years ago

Cliff L. · Reviewed about 7 years ago

atmi t. · Reviewed about 7 years ago

Murali K. · Reviewed about 7 years ago

Too slow execution of dataflow, I need more practice it seems

Salvador L. · Reviewed about 7 years ago

Too slow execution of dataflow

Salvador L. · Reviewed about 7 years ago

Good lab but too long to finish it one hour Dataflow is quite slow

Pierre G. · Reviewed about 7 years ago

Connor M. · Reviewed about 7 years ago