#542 overall
Google Cloud Data Engineering Specialization
Coursera
1 recommender · 1 thread · recommended in 1 year, 2018 · score 1.0
A Coursera specialization from Google Cloud covering how to design and build data pipelines on Google Cloud Platform, from ingesting data through to machine learning. The title and linked page refer to data engineering with machine learning on GCP.
One commenter says they highly recommend it as a Google Cloud data engineering course.
Databases & Data Engineeringintermediatemixed#google cloud#data pipelines#bigquery#dataflow#machine learning#coursera
Recommendations by year
20062026
Former GCP support here. Bigtable and Cloud Datastore (or the newer, shinier Firestore) are very different, and meant for different purposes.
Bigtable is meant for wide-column data at high volume. If your data can be organized into simple rows and columns, and you plan on using massive amounts of it at high throughout (think IoT transactions, for example), then bigtable is the right choice.
Datastore, on the other hand, is for semi-structured data, with parent/child hierarchies, key value pairs, etc. It isn't run on a cluster of nodes like bigtable, but is managed behind the scenes as part of App Engine. It is slower than bigtable, but is more sophisticated, and offers client libraries for ORMs (ndb), SQL-like queries, and the like.
There's a brief comparison chart here: https://cloud.google.com/storage-options/
I also highly recommend the Google cloud data engineering course at Coursera: https://www.coursera.org/specializations/gcp-data-machine-le...
Or the instructor's book, " Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning"
https://www.amazon.com/dp/1491974567/ref=cm_sw_r_cp_apa_DILV...