Google Cloud Platform GCP is the fastest growing public cloud. The PDE (Professional Cloud Data Engineer) certification is a certification that helps you deploy your data pipeline within the GCP cloud.
This course has over 16 hours of insanely great video content and over 80 hands-on labs (the most practical course)
Do you want to deploy your data pipeline within GCP?
Want to learn about the different storage, databases, processing, and ML products that GCP offers to gain insights into your data?
Do the largest internet apps such as Google Search, YouTube, and GMAIL (a billion user app) store data, process it, and use ML to process it to find meaningful insights?
If yes, you are in the right place.
Why cloud, GCP, certification, data engineering?
The cloud is the future, and GCP is the fastest growing public cloud.
87% of Google Cloud certified people are confident in their cloud skills.
More than one in four Google Cloud certified people have more responsibility or leadership roles in the workplace.
Google Cloud: Professional Cloud Data Engineer certification is perfect for investing time and energy to increase the demand for data storage and processing.
We are pleased to help you on your way to becoming a Google Cloud Professional Cloud Data Engineer credential.
So we have created the most practical and comprehensive course to prepare for professional cloud data engineer certification with over 16 hours of HD quality video content.
Why enroll in this course?
I believe in doing and learning and it’s a very hands-on course
Over 80 hands-on demos
80% Practical + 20% Theory-Very Practical Course
Highly relevant to exam topics
Covers all major topics related to storage, database, processing and ML
Minimum slide + maximum GCP cloud console
To see the depth of the course coverage, see the course curriculum.
The main themes of this certification course are:
1. Data engineering and GCP basic services
In this module, we will start with the data engineering pipeline.
Different types of data: structured data, semi-structured data, unstructured data, some concepts related to batch data processing and stream data GCP-related concepts such as GCP regions and zones, how to create a GCP account and Various GCP service engineering perspectives provided by the data.
The following describes GCP basic infrastructure services such as IAM, VM, kubernetes provisioning, app engine, cloud run, and cloud feature deployment.
2. GCP data storage
This module describes various data storage products for storing unstructured data, Google cloud storage, file stores, persistent disk storage, local SSD storage, and how to migrate data from on-premises to GCP.
3. Providing a database by GCP
This module describes a database solution for storing structured and semi-structured data.
There is Google Cloud SQL and a cloud spanner is available to store structured data within GCP.
Semi-structured data in GCP includes Google Cloud BigTable, DataStore / firestore, and MemoryStore in memory.
4. Data processing with GCP
This data processing section starts with Google Cloud BigQuery, a data warehousing analytics data processing solution, and describes the Google Cloud PubSub service for asynchronous communications.
When developing a complete pipeline within GCP-
Dataflow Apache Beam Solution in Google Cloud
Google Cloud DataProc for Lift and Shift Hadoop and Spark Jobs
Build a complete pipeline with cloud data fusion without writing code with just drag and drop
Monitor authors and schedule complete workflows with Apache airflow-Cloud Composer available
Data loss prevention API for data detection that is highly confidential and personally identifiable-DLP
Find all datasets or access all data catalogs in one place
5. ML / AI offering on GCP
In this module we will start with the basics of machine learning
Prepare the data using the intelligent data preparation tool Dataprep before submitting all the data into the machine learning algorithm.
Shows a variety of pre-built machine learning APIs for visual, language, and audio
Dual automatic machine learning model using AutoML
Build custom machine learning models using life tensor flow of various frameworks, scikit learn, and Pytorch
BigqueryML for machine learning training using SQL
Finally, we’ll show you how to use your browser’s Google Cloud Data Studio tools to create beautiful reports and visualizations.
This course also includes:
Lifetime access to all course materials and updates
Q & A section
30-Day Money Back Guarantee-“No Questions”
Udemy Certificate of Completion
So what are you waiting for, register now. See you in the course.
nice to meet you
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