Google Cloud Certified Professional Data Engineer

24 Hours of Expert-Led Training

87% Certification Exam Success Rate

Global Job Market Opportunities

Master Big Data and ML Solutions

Flexible Payment Options Available

4.85/5

6585 Enrolled

Overview

Skills we aim to impart

Create data pipelines for various processing needs
Implement lifecycle management for data
Continuously evaluate and retrain models
Monitor and improve data processing infrastructure
Validate and monitor migrated systems

Learning Objectives

After training completion, you will master the following

Explore fundamental principles of data engineering using the Google Cloud Platform

Gain a comprehensive knowledge of Google Cloud Platform services to strengthen your foundation in data engineering and machine learning

Learn how to design, build, and operationalize effective data solutions

Develop skills to build and manage data processing systems on the Google Cloud Platform

Master techniques for efficient cloud usage to store, retrieve, and manage data effectively

Ready to get started?

Prerequisites

The prerequisites for enrolling in the Google Cloud Certified Professional Data Engineer Course are listed. Candidate must meet one of the following criteria

Candidates should be well-versed in data management, processing, and security, as well as data warehousing. They should also have practical expertise with the Google Cloud Platform
Strong programming skills in SQL and at least one other programming language
Knowing Hadoop, MapReduce, and Spark, as well as other related distributed systems technologies
Working knowledge of big data analysis and processing tools including Apache Beam, Apache Flink, and Apache Spark
Expertise with the Google Cloud AI Platform and understanding of machine learning methods
Knowledge of cloud computing ideas and familiarity with cloud-based services and infrastructure

Overall ratings by our students

Upcoming sessions

Our Trainers

We, at Learners Point, take immense pride in our teaching methods and instructors. Our instructors are some of the best experts in their fields and employ a practical approach to learning. Many of them are globally recognised and have a diverse set of experience in their field of expertise. You are always sure to have the best in the industry as your teachers who are ready to guide you at every step and make the experience informative yet enjoyable. Apart from the focus on learning your chosen course, our instructors also encourage students to develop communication skills and interpersonal skills necessary to excel in the practical world.

Our cutting edge teaching methods make every program an immersive and productive experience for the learners. Our learning methods are research-driven and are continuously updated to stay relevant to present times as well as the future. You will enjoy practical applications of everything learned through theory and regular mock examinations to help monitor your progress. Our courses are led by an instructor in a classroom setup and we do offer online high-quality sessions as well for individuals. We also monitor the training sessions with a progress tracker to maintain high standards of instruction & ethics.

Learners Point Certificate

Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.

KHDA Certificate

Earn a KHDA attested Course Certificate. The Knowledge and Human Development Authority (KHDA) is the educational quality assurance and regulatory authority of the Government of Dubai, United Arab Emirates.

Related courses

Curriculum

Selecting the appropriate storage technologies

Mapping storage systems to business requirements
Data modeling
Trade-offs involving latency, throughput, transactions
Distributed systems
Schema design

Designing data pipelines

Data publishing and visualization
Batch and streaming data
Online (interactive) vs. batch predictions
Job automation and orchestration

Designing a data processing solution

Choice of infrastructure
System availability and fault tolerance
Use of distributed systems
Capacity planning
Hybrid cloud and edge computing
Architecture options

Migrating data warehousing and data processing

Awareness of current state and how to migrate a design to a future state
Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
Validating a migration

Building and operationalizing storage systems

Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Datastore, Memorystore)
Storage costs and performance
Life cycle management of data

Building and operationalizing pipelines

Data cleansing
Batch and streaming
Transformation
Data acquisition and import
Integrating with new data sources

Building and operationalizing processing infrastructure

Provisioning resources
Monitoring pipelines
Adjusting pipelines
Testing and quality control

Leveraging pre-built ML models as a service

ML APIs
Customizing ML APIs
Conversational experiences

Deploying an ML pipeline

Ingesting appropriate data
Retraining of machine learning models (AI Platform Prediction and Training, BigQuery ML, Kubeflow, Spark ML)
Continuous evaluation

Choosing the appropriate training and serving infrastructure

Distributed vs. single machine
Use of edge compute
Hardware accelerators

Measuring, monitoring, and troubleshooting machine learning models

Machine learning terminology
Impact of dependencies of machine learning models
Common sources of error

Designing for security and compliance

Identity and access management
Data security (encryption, key management)
Ensuring privacy
Legal compliance

Ensuring scalability and efficiency

Building and running test suites
Pipeline monitoring
Assessing, troubleshooting, and improving data representations and data processing infrastructure
Resizing and autoscaling resources

Ensuring reliability and fidelity

Performing data preparation and quality control
Verification and monitoring
Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
Choosing between ACID, idempotent, and eventually consistent requirements

Ensuring flexibility and portability

Mapping to current and future business requirements
Designing for data and application portability
Data staging, cataloging, and discovery

Frequently asked questions

With Big Data and Data Science taking over today’s business world, it is always a great choice to flourish in this domain. This sector offers ample job opportunities for professionals with the right skillset. With nearly every industry at its peak of major digital transformation, professionals can have a promising future with a career in Data Engineering.

With technological advancements in the UAE, Dubai is now becoming the home to tech savvies. Professionals with significant experience in Data Engineering and conceptual understanding of AL and ML can bring their careers one level up. The demand for Data Engineering skilled professionals is all set to rise at an impressive rate in the UAE making it the ideal location for a futuristic career.

The Google Cloud Certified Professional Data Engineer is one of the most sought-after credentials among big data and analytics professionals. Issued by Google, this GCP Data Engineer credential is valid for 2 years from the date of certification and is widely recognized by top-notch organizations. Owing to its value in the global business world, this certification can bring certified professionals a plethora of job opportunities and increased earning potential.

The potential for Big Data and Cloud technology is huge in the future as it is an unavoidable trend in a wide range of industries. Due to its complexity, professionals are to be trained by industry experts to master this field. This Google Cloud Certified Professional Data Engineer training in Dubai explains core concepts of data engineering using the Google cloud platform. Furthermore, this GCP Data Engineer training is a full-fledged training program that increases your proficiency and competency and imparts critical job skills for Data Engineer roles.

The Google Cloud Certified Professional Data Engineer training in Dubai is a perfect fit for any professional interested in enhancing their competency in Data Engineering and the ML field or wanting to prepare for the Google Cloud Certified Professional Data Engineer certification exam.

There are no specific prerequisites for enrolling in this Google Cloud Certified Professional Data Engineer course. However, we do recommend participants have a basic understanding of the cloud, databases, and data analysis.
In order to appear for the Google Cloud Certified Professional Data Engineer certification exam, candidates are to have 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud.

The training sessions at Learners Point are interactive, immersive, and intensive hands-on programs. We offer 3 modes of delivery and participants can choose from instructor-led classroom-based group coaching, one-to-one training session, or high-quality live and interactive online sessions as per convenience.

At Learners Point, if a participant doesn’t wish to proceed with the training after the registration due to any reason, he or she is entitled to a 100% refund. However, the refund will be issued only if we are notified in writing within 2 days from the date of registration. The refund will be processed within 4 weeks from the day of exit.