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

It is a best lab!~

승남 왕. · Reviewed около 7 лет ago

CG K. · Reviewed около 7 лет ago

AASTHA A. · Reviewed около 7 лет ago

Britter Johagen Q. · Reviewed около 7 лет ago

Bill B. · Reviewed около 7 лет ago

Daniel B. · Reviewed около 7 лет ago

Vadim V. · Reviewed около 7 лет ago

Alexis T. · Reviewed около 7 лет ago

A little too much copy paste and would like more learning material.

Michael G. · Reviewed около 7 лет ago

Federico F. · Reviewed около 7 лет ago

Done

Carlos L. · Reviewed около 7 лет ago

Huzaen J. · Reviewed около 7 лет ago

Puneet C. · Reviewed около 7 лет ago

Wei Lun K. · Reviewed около 7 лет ago

Ankit G. · Reviewed около 7 лет ago

Puneet C. · Reviewed около 7 лет ago

Chun Yu C. · Reviewed около 7 лет ago

Good but little slow

Eli K. · Reviewed около 7 лет ago

Sridhar E. · Reviewed около 7 лет 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 около 7 лет ago

Punyanuch S. · Reviewed около 7 лет ago

Andrew Z. · Reviewed около 7 лет 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 около 7 лет ago

Graciela G. · Reviewed около 7 лет ago