Google Cloud
Google Cloud-Professional Data Engineer
With the Google Cloud Professional Data Engineer Training by Techpratham, you can learn how to process, store, and use machine learning solutions on GCP. This course will help you pass the Professional Data Engineer certification exam and make cloud apps that can grow and are based on data.
5/5(4,890 Reviews)
Level
Advanced
Duration
8 Weeks
What is Google Cloud-Professional Data Engineer?
The Google Cloud Professional Data Engineer Training by Techpratham will help you become an expert at designing, building, and running data processing systems on GCP. You will learn how to use BigQuery, Dataflow, Pub/Sub, Cloud Storage, and machine learning to solve real-world data problems. This training focuses on making sure that data is reliable, scalable, secure, and follows the rules in business settings. It fully prepares you for the Google Cloud Professional Data Engineer certification exam and high-demand cloud data engineering jobs with hands-on labs and projects.
Training Plan
About trainer
Working professional who is carrying more then 10 years of industry experience.
Decks
The candidate will get access to the presentation taken-up during the session.
Notes
Consolidated notes in word document.
Assignments
Assignments for every module will be covered.
MCQs
MCQs for every module covered in the session would be provided.
Video Recording
Daily Session would be recorded and shared to the candidate.
Dumps for Certification
We will provide dumps for the certification exam, which will help you to prepare for the exam.
Updated Content
We provide Generative AI Driven content By experts.
Projects
3 Live Projects will be provided for practice.
Google Cloud-Professional Data Engineer Course Curriculum
.
Introduction to Google Cloud Data Engineering
Section 1
Learn the fundamentals of Google Cloud Platform and the role of a data engineer in cloud ecosystems.
GCP services overview – Compute, Storage, Networking
Data engineer roles – responsibilities & skills
Cloud resource hierarchy – projects, billing, IAM
GCP console & CLI – navigation & commands
Data engineering use cases – real-world applications
.
Google Cloud-Professional Data Engineer
Section 2
Learn how to use Google Cloud to create scalable batch and stream data processing solutions.
Batch vs Stream Data Processing
Dataflow and Apache Beam Fundamentals
Designing ETL and ELT Pipelines
Data Transformation and Enrichment
Integrating Data Sources with Pub/Sub and Cloud Functions
.
Building and Managing Data Storage Solutions
Section 3
Discover how to create scalable, effective, and safe data storage systems using GCP.
Cloud Storage and Data Lake Design
Cloud SQL and Cloud Spanner
BigQuery Architecture and Best Practices
Firestore and Datastore for NoSQL
Data Warehousing Concepts on GCP
.
Designing and Building Data Pipelines
Section 4
Create automated pipelines with Google Cloud tools for both batch and real-time data ingestion.
Data Ingestion with Pub/Sub
Real-Time Processing with Dataflow
Batch Processing with Dataproc (Hadoop/Spark)
Orchestration using Cloud Composer (Airflow)
Managing Pipeline Dependencies and Failures
.
Operationalizing Machine Learning Models
Section 5
By operationalizing ML models, you can close the gap between data science and data engineering.
Introduction to AI and ML in GCP
Using AI Platform for Model Training and Deployment
TensorFlow on GCP
Automating Model Pipelines (MLOps)
Model Monitoring and Versioning
.
Ensuring Data Quality, Security, and Governance
Section 6
Use best practices for data governance, security, and compliance throughout your data ecosystem.
Data Validation and Quality Management
IAM and Access Controls for Data Services
Data Encryption and Key Management (KMS)
Compliance and Privacy (GDPR, HIPAA, etc.)
Metadata and Lineage with Data Catalog
.
Monitoring, Optimizing, and Troubleshooting Data Systems
Section 7
Develop your skills in cost optimization, performance tuning, and GCP data workflow troubleshooting.
Cloud Monitoring for Data Pipelines
Logging and Auditing for Data Workflows
Performance Tuning for BigQuery and Dataflow
Cost Optimization Strategies for Data Processing
Troubleshooting and Error Handling
.
Managing Data Solutions for Reliability and Scalability
Section 8
Create data systems that are highly available and resilient while adhering to enterprise performance standards.
Designing Fault-Tolerant Data Systems
High Availability and Disaster Recovery Strategies
Scaling Data Pipelines and Databases
Data Replication and Backup Planning
Resource Management and Quota Optimization
.
Preparing for the Professional Data Engineer Exam
Section 9
Use case studies and practice exams to ensure you are completely ready for the Google Cloud Data Engineer exam.
Overview of the Google Cloud Professional Data Engineer Exam
Key Domains and Weightage
Case Studies and Scenario-Based Questions
Practice Tests and Hands-on Labs
Exam Tips, Strategy, and Review
Key Projects
Google Cloud-Professional Data Engineer
Real-Time Data Pipeline with Pub/Sub and Dataflow
You will use Cloud Pub/Sub and Dataflow to build a pipeline that takes in and processes data in real time for this project. BigQuery will collect, change, and store streaming data so that it can be analyzed. This project focuses on scalability, fault tolerance, and an architecture based on events.
Big Data Analytics with BigQuery and Looker Studio
This project is all about using BigQuery to look at big datasets and Looker Studio to make interactive dashboards. You will use advanced SQL, optimize queries, and work with partitioned and clustered tables. The final dashboard will give you business intelligence insights and let you report on them in real time.
Machine Learning Integration with Vertex AI & BigQuery ML
You will use BigQuery ML and Vertex AI to train and deploy a machine learning model in this project. This will help you make predictions. You will put the model into a data pipeline so that it can make predictions automatically. This will show how AI can be used to make decisions in the cloud.
Google Cloud-Professional Data Engineer – Associate Training Program
Category
Associate
Exam Name:
Google Cloud-Professional Data Engineer – Associate
Exam Code:
N.A.
Exam Duration:
N.A.
Exam Format:
N.A.
Passing Score:
N.A.
Our Testimonials
Frequently Asked Questions (FAQs)
What is Google Cloud Professional Data Engineer Training?
The Google Cloud Professional Data Engineer Training is a program that helps people learn how to design, build, and manage scalable data pipelines on GCP. It is aimed at getting people certified. It includes real-world cloud data solutions that use BigQuery, Dataflow, Pub/Sub, Cloud Storage, Dataproc, and ML integration.
Who can take the Data Engineer certification course from Tech Pratham?
Those who wish to become proficient in GCP data solutions include IT specialists, data analysts, cloud engineers, and future data architects.
What tools are covered in the Google Cloud Data Engineer course?
Cloud Composer, BigQuery, Dataflow, Pub/Sub, Dataproc, AI Platform, and Data Catalog.
What is Cloud Composer, and why is it used?
Data pipeline dependencies can be automated with this workflow orchestration service, which is based on Apache Airflow.
What is the format of the Professional Data Engineer exam questions?
The scenario-based questions demand both conceptual knowledge and real-world judgment.
What are some common challenges Data Engineers face?
managing massive data sets, maintaining data consistency, controlling expenses, and ensuring real-time dependability.
ERP Partners
Other Google Cloud Courses
📚
No Related Courses Found
We couldn't find any other courses in the "Google Cloud" category at the moment.
Registered Office:
Noida Office:
Hyderabad Office:
Trending Certification Courses
Company
© 2025 TechPratham. All rights reserved.An ISO 9001:2015 Certified Company