Google Cloud – Professional Data Engineer Certification learning pathway

Google Cloud – Professional Data Engineer Certification Learning Path
I recently recertified for my Google Cloud Certified – Professional Data Engineer certification. It took me 2 long years to attempt the Data Engineer exam. It was 4 hours long and had 95 questions. Similar to other Google Cloud certification exams the Data Engineer exam covers a wide range of concepts and services. However, it also focuses on practical experience and logical thinking.
Google Cloud – Professional Cloud Data Engineer Certification Summary
Cloud Data Engineer exam had 50 questions that needed to be answered in two hours
This book covers a wide range data services, including machine learning and storage.
Exam does not cover case studies
The exam covers the most recent services, but it has not been updated to Cloud Monitoring and Logging. It still refers back to Stackdriver.
Compute and Network are not covered.
Google Cloud questions are more about your logical thinking than any concept.
You must be hands-on if you are new to GCP. Otherwise, you will not know how to answer some of the questions or what commands to use.
You should be aware that no online course or practice test will cover everything. Coursera, LinuxAcademy was really extensive, but practical or hands-on knowledge is a MUST. Google Cloud – Professional Cloud Data Engineer Certification Resources
Guide for Professional Data Engineers Certified by Google Cloud
Online CoursesUdemy – Google Cloud Professional Data Engineer Certification
Coursera – Preparing for the Google Cloud Professional Data Engineer Exam. This course is an overview but not a detailed one.
Coursera – Data Engineering on Google Cloud Platform
Linux Academy – Google Cloud Certified – Professional Cloud Architect is very detailed.
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Practice TestsBraincert Google Cloud Certified – Professional Data Engineer Practice Exams
Whizlabs – Practice Questions for Google Cloud Certified Professional data Engineers
As much as possible, use Qwiklabs and Google Free Tier.
Google Cloud – Professional Cloud Data Engineer Certification Topics
Data & Analytics Services
There are many data and related services available.
Google Cloud Data & Analytics Services Cheatsheet
Learn the Big Data stack to understand which service is best suited for the various layers of ingest and storage, process, and analytics
Cloud BigQuery provides a fully managed, scalable enterprise data warehouse (EDW), with SQL and fast ad hoc queries.
Ideal for storage and analytics.
Cloud Storage is a cost-effective storage option that is just as affordable.
Understand BigQuery SecurityUse BigQuery IAM access rights to control data access and querying access
Use Authorized views to gain access to control tables, columns within tables, or query results. HINT: Authorized Views must be in a different dataset than the source dataset.
support data encryption
BigQuery Best Practices: Key strategy, cost optimization and partitioning. Use dry run to estimate costs
Use clustering and partitioning to limit the data being scanned
External data sources can cause query performance degradation. It is better to import the data.
Only the creation of a dataset can determine its location.
Supports schema auto-detection of JSON and CSV files
Learn how BigQuery Streaming works
BigQuery limitations With updates and inserts
Supports an external data source (federated source) which is a data source that can directly be queried even though it is not stored in BigQuery.
Cloud Bigtable provides support for querying data directly
Cloud Storage
Google Drive
Cloud SQL
Multiple times query an external data source using the Permanent Table
Use Temporary table to quer