Google Cloud Professional Data Engineer Certification Course

Certified GCP Data Engineer professional is a globally recognized and valued certification focusing on honing an individual's skills to make data-driven decisions by optimizing the obtained data. This Certification course helps pupils in enabling to learn the concepts of Data Science from scratch. Master your skills with Wissenhive by studying important concepts.

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Course Features
  • Lifetime Training Access
  • Study Guides
  • fddsaCourse Completion certificate
  • asas24/7 Support
  • rtyrGoogle Cloud Professional Instructors

Overview

The certification expertly demonstrates how to efficiently collect, transform, and visualize data for generating valuable insights with the aim to render practical understanding to design, develop, manage, and troubleshoot the data processing systems while emphasizing and maintaining the crucial aspects of the system, including scalability, reliability, fault-tolerance, security, fidelity, and efficiency.

What you will learn

  • Designing a data processing system
  • Building and maintaining the structures and databases of data 
  • Analyzing data and enabling machine learning
  • Optimizing data infrastructure performance, data representations, and cost
  • Ensuring reliability of data processing infrastructure
  • Visualize data
  • Design secure data processing systems

Syllabus

International industry expertise at your disposal as you deep-dive into the research topic and sector of your choice.

Career Options

Professional Data Engineers enable data-driven decision-making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability.

Job Roles

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Course Content

Designing data processing systems (5 Lectures)

Mapping storage systems to business requirements

Data modeling

Trade-offs involving latency, throughput, transactions

Distributed systems

Schema design

Designing data pipelines (4 Lectures)

Data publishing and visualization (e.g., BigQuery)

Batch and streaming data (e.g., Dataflow, Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Pub/Sub, Apache Kafka)

Online (interactive) vs. batch predictions

Job automation and orchestration (e.g., Cloud Composer)

Designing a data processing solution (7 Lectures)

Choice of infrastructure

System availability and fault tolerance

Use of distributed systems

Capacity planning

Hybrid cloud and edge computing

Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)

At least once, in-order, and exactly once, etc., event processing

Migrating data warehousing and data processing (3 Lectures)

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 data processing systems (3 Lectures)

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 (5 Lectures)

Data cleansing

Batch and streaming

Transformation

Data acquisition and import

Integrating with new data sources

Building and operationalizing processing infrastructure (4 Lectures)

Provisioning resources

Monitoring pipelines

Adjusting pipelines

Testing and quality control

Operationalizing machine learning models (4 Lectures)

Leveraging pre-built ML models as a service

ML APIs (e.g., Vision API, Speech API)

Customizing ML APIs (e.g., AutoML Vision, Auto ML text)

Conversational experiences (e.g., Dialogflow)

Deploying an ML pipeline (3 Lectures)

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 (3 Lectures)

Distributed vs. single machine

Use of edge compute

Hardware accelerators (e.g., GPU, TPU)

Measuring, monitoring, and troubleshooting machine learning models (3 Lectures)

Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)

Impact of dependencies of machine learning models

Common sources of error (e.g., assumptions about data)

Ensuring solution quality (5 Lectures)

Designing for security and compliance

Identity and access management (e.g., Cloud IAM)

Data security (encryption, key management)

Ensuring privacy (e.g., Data Loss Prevention API)

Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))

Ensuring scalability and efficiency (4 Lectures)

Building and running test suites

Pipeline monitoring (e.g., Cloud Monitoring)

Assessing, troubleshooting, and improving data representations and data processing infrastructure

Resizing and autoscaling resources

Ensuring reliability and fidelity (4 Lectures)

Performing data preparation and quality control (e.g., Dataprep)

Verification and monitoring

Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)

Choosing between ACID, idempotent, eventually consistent requirements

Ensuring flexibility and portability (3 Lectures)

Mapping to current and future business requirements

Designing for data and application portability (e.g., multicloud, data residency requirements)

Data staging, cataloging, and discovery

Course Details

  • Designing and building the data processing systems on Google Cloud
  • Lifting and shifting existing Hadoop workloads to the Cloud by utilizing Cloud Dataproc
  • Processing the streaming and batch data for the implementation of autoscaling data pipelines on Cloud Dataflow
  • Managing data pipelines with Cloud Composer and Data Fusion 
  • Derive the ideal insights for business from extremely large datasets by BigQuery
  • Learning how to utilize pre-built ML APIs
  • Enabling the instant insights from streaming data

Who should take this course?

The Google Cloud Professional Data Engineer Certification is ideal for IT professionals who are already working or want to make a career as a senior or professional in 

  • Newbies and beginners 
  • Data Engineers
  • Programmers
  • Machine Learning Engineers
  • Data Scientists
  • Data Consultants
  • Data Analytics Consultants/ Senior Data Scientists 
  • Software Engineers
  • Python Developers
  • Data Science Managers
  • Digital Data Analysts

Prerequisites

There are certain prerequisites needed for taking the Google Cloud Professional Data Engineer Certification, Wissenhive and Google recommend having 

  • Basic knowledge of fundamentals of cloud computing and relevant practical experience 
  • 3+ years of experience in industry experience
  • 1+ years of experience in designing and managing solutions using Google Cloud

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Training Options

Self-paced Training

599
549 (Onwards)
  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • Hands-on projects to perfect the skills learned
  • Simulation test papers for self-assessment
  • 24x7 learner assistance and support

Live Virtual Classes

799
749 (Onwards)
  • Online Classroom Flexi-Pass
  • Lifetime access 
  • Hands-on projects to perfect the skills learned
  • Simulation test papers for self-assessment
  • 24x7 learner assistance and support
Classes starting from:-

One on One Training

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  • Blended learning delivery model (self-paced eLearning and/or instructor-led options)
  • Flexible pricing options
  • Enterprise-grade Learning Management System (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

Exam & Certification

To achieve the Google Cloud Certified Professional Data Engineer certification, the candidates need to pass an exam that is conducted by Google. This exam can be taken remotely with an online proctoring facility or it can be taken from the designated test centres across the world.

The important details regarding the exam are given below:

  • Duration: 2 Hours
  • Number of questions: 50
  • Format: Multiple choice questions

The questions in the exam belong to different topics including the following:

  1. Designing data processing systems
  2. Building and operationalizing data processing systems
  3. Operationalizing machine learning models
  4. Ensuring solution quality

The candidates appearing in this exam should align themselves with the aforementioned topics and ensure a strong understanding of these skills. However there is no information on the minimum passing score officially from Google, but as per the successful candidates, you must score at least 70% to pass the exam. 

Google Cloud Professional Data Engineer Certification Course

Frequently Asked Questions

Google has put up a dedicated certification for IT professionals who intend to be data engineers over the Google Cloud Platform. It is crucial for you to go with the Professional Data Engineer certification because data analytics and big data analytics are considered the lifeline of any organization or business. Therefore, it is important for individuals to master this technology to take up data-oriented initiatives for helping the business thrive.

For successfully implementing the big data or data initiatives, you will need more proficient skills than being a data analyst or data scientist. You will initially need the skills of a data architect who will be designing the entire framework of data management for your organization. Following that, you will need data engineers to build that designed framework and bring the data pipelines into action for deriving business value out of collected data.

Therefore, if you are willing to pursue your career in the field of data management and optimization, then this certification is a good supportive qualification for you. Getting an edge with this profession demands ideal certification, and Google Professional Data Engineer is an ideal pick for you.

While preparing for this certification, you will gain your skills of using the right tools from the open-source ecosystem of big data. Along with that, you also need to have theoretical and practical knowledge of Python, Scala, or Java to clear this examination with good grades and on the first attempt.

For clearing the certification exam, you need to be aware of the important areas to consider while you prepare. This certification is going to test your abilities in the areas such as:

1. Designing Data Processing Systems

  • Selection of the appropriate storage technologies.
  • Designing of the data pipelines.
  • Designing a data processing solution.
  • Migrating data processing and data warehousing

2. Building & Operationalizing Data Processing Systems

  • Building & Operationalizing storage systems
  • Building & Operationalizing pipelines. (Data cleansing, transformation, batch & streaming, data acquisition, etc.)
  • Building & Operationalizing processing infrastructure. (Provisioning resources, adjusting pipelines, monitoring pipelines, etc.)

3. Operationalizing Machine Learning Models

  • Leveraging pre-built ML models as a service.
  • Deploying Machine Learning Pipeline.
  • Selecting the appropriate training & serving infrastructure.
  • Measuring, monitoring & troubleshooting the ML models.

4. Ensuring Solution Quality

  • Designing for Security & Compliance.
  • Ensuring Scalability & Efficiency
  • Ensuring Reliability & Fidelity
  • Ensuring Flexibility & Portability.

These are the areas in which you need to direct your preparation strategies. Moreover, these are the topics on which the certification exam will be based so as to test your skills. Therefore, make sure you put up your efforts towards adapting the right learning methods in order to get your concepts clear and take on the examination confidently.

  1. Go Through the Exam Guide
  2. Take up the Official Learning Path Courses Offered by Google Cloud
  3. Take Instructor-led Training or Webinars by GCP for In-depth Preparation
  4. Solve the Sample Questions and Get Hands-on Practice
  5. Register for the Examination

The average salary for a Google Professional Data Engineer in the USA is around $147,000 per year. It is a whooping pay-out for this profession that gives clarity on the career-oriented scope. The lowest entry-level salary of a Google Data Engineer is $141,375 per year, whereas the highest pay-out for experienced data engineers is recorded to be $175,000 per year.

Therefore, you can conclude that Google Data Engineer certification is the best you can pursue to obtain a high-paying job. With experience over time, the salary will also hike gradually.

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