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Learn to develop reliable data processing systems. Join today to learn from an expert Google Data engineer.

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Course Duration

30 Hrs.

Live Project

2 Project

Certification Pass

Guaranteed

Training Format

Live Online /Self-Paced/Classroom

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Google Cloud

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250+

Professionals Trained
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3+

Batches every month
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20+

Countries & Counting
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Corporate Served

  • In the Google Cloud-Professional Data Engineer training program, you will learn to build and operationalize data processing systems. Besides this, you will learn to deploy and train ML learning models. You will also learn to model processes (business processes) for the purpose of analysis and optimization. Moreover, you will also acquire all the skills that a skilled Google Cloud Data Engineer must possess.
  • Furthermore, you will learn about data processing/data storage fundamentals, designing data pipelines, etc. You will also learn about data publishing, data visualization, and pipeline life cycle.
  • Things you will learn:
    • Data processing fundamentals

      Data storage fundamentals

      Mapping storage systems

      Data modeling

      Streaming data

      Dataflow pipelining

      How to operationalize data processing systems

  • Prerequisites:
    • Basic computer knowledge

      Basic knowledge of data processing systems

      Passion for learning

      Basic knowledge about GCP

  • This course is for:
    • GCP newbies

      Aspiring Google Cloud Data Engineers

      Engineering graduates

      Google Cloud experts who wish to train for other roles

      Students who want to clear the Google Cloud Data Engineer exam

Google Cloud-Professional Data Engineer

About-Us-Course

  • The Google Cloud-Professional Data Engineer training course is ideal for aspiring Google Cloud Data Engineers who wish to become experts in designing and operationalizing data processing systems with GCP.
    • Provide quality knowledge about data processing and data processing systems

      Make students expert in data modeling

      Help students learn how to stream data

      Make students familiar with the duties of a Google Cloud Data Engineer

      Make students proficient in dataflow pipelining

      Prepare students so that they are able to clear the Google Cloud Data Engineer exam without any difficulty

      Make students expert in operationalizing ML models

  • Our top-notch faculty will help you:
    • Become comfortable in the data processing

      Understand how to develop data processing systems

      Understand how to stream data

      Master key concepts of data modeling

      Become a competent Google Cloud Data Engineer

      Understand the responsibilities of a Cloud Data Engineer

      Become an expert in operationalizing data systems

      Become an expert in operationalizing ML models

  • A large number of professionals want to pursue their careers in the cloud computing domain. This is because of the growth options that this domain offers and the amount of money a professional can earn by pursuing their career in the cloud computing industry. As per leading job portals and websites, a Google Cloud Data Engineer can earn a hefty salary for his services.
  • As per Glassdoor, a Google Cloud Data Engineer can earn almost 2 LPA to 3 LPA as a fresher, while an experienced Google Cloud Data Engineer can earn around 14 LPA to 23 LPA. But it all depends upon your skillset and proficiency as a data engineer.

  • The cloud computing domain offers lots of growth options to professionals who pursue their careers in this domain. Moreover, a cloud computing professional can also earn a very decent amount of money in exchange for their services. In short, you can expect lots of growth opportunities and handsome remuneration if you pursue your career as a cloud computing professional.
  • Roles that you can take after doing the Google Cloud Data Engineer training program:
    • Google Cloud Data Engineer

      Train for roles like data architect, data solutions architect, etc.

      Google Cloud professional

      Google Cloud trainer

      Contributor to the community

  • The demand for skilled Google Cloud Data Engineers is increasing in the job market with each passing day, and as per various studies, this trend is not going to end anytime soon. What does this mean It means pursuing your career in the cloud computing domain will be very beneficial for you.

  • A Google Cloud Data engineer helps a company in making data-driven decisions by storing, processing, and publishing data. Furthermore, he helps a firm in designing and operationalizing data processing systems. These are the primary reasons why there is a constant increase in the demand for skilled Google Cloud Data Engineers in the market.
  • Here’s why cloud data engineers are so much popular:
    • Mentor team members

      Operationalizes data processing systems

      Operationalizes ML models

      Helps company in making data-driven decisions

  • The job of a Google Cloud Data Engineer is not easy. He has to perform lots of complex tasks as well as duties in an organization. For example, he develops and operationalizes data processing systems and helps a company in data-driven decision-making. In short, the job of a Google Cloud Data Engineer is not as easy as it may seem or appear to be, and one must possess a decent amount of knowledge about data engineering and GCP for becoming a competent Google Cloud Data Engineer.
  • Key Responsibilities of a Google Cloud Data Engineer:
    • Collect datasets that fulfill the requirements of the business

      Design algorithms for transforming data into helpful and actionable information

      Design database pipeline architecture

      Create data validation methods

      Operationalize data processing systems

      Operationalize machine learning models

      Mentor team members

  • A Google Cloud Data Engineer is a very important part of an organization. He performs lots of important duties and responsibilities for a company like helping stakeholders in data-driven decision making, developing data processing systems, etc. This is the primary reason why cloud data engineers are paid very well, and all the firms who wish to leverage the power of cloud computing are always looking for skilled cloud data engineers for their organizations.
  • Top hiring companies for Google Cloud Data Engineers:
    • Google

      Virtusa

      CloudThat Technologies Pvt. Ltd.

      Accenture

      Code Clouds

      VMware

  • Once students submit all their assignments and complete their training, they will receive a training completion certificate from Croma Campus. Our training certificate is industry-recognized, and with it, you can easily secure yourself a job as a cloud data engineer in any top-rated company or MNC.
  • Furthermore, you will also get lots of other services from our HR and placement department like:
    • 100% placement support

      Interview grooming sessions

      Resume preparation

      Access to the job portal of Croma Campus

  • Advantages of getting certification:
    • Industry-recognized certification

      Hefty remuneration

      Help you show your skills as a Google Cloud Data engineer

      Hike in salary

      Lots of job opportunities

Why Should You Do Google Cloud-Professional Data Engineer Training Program?

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Plenary for Google Cloud-Professional Data Engineer

TrackWeek DaysWeekendsFast Track
Course Duration 40-45 Days 7 Weekends 8 Days
Hours 1 Hrs. Per Day 2 Hrs. Per Day 6+ Hrs. Per Day
Training ModeClassroom/OnlineClassroom/OnlineClassroom/Online
Course Price at :
24,750 27,500
10% OFF Expires in01D 08H 53M 23S

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Program Core Credentials

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Trainer Profiles

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Trained Students

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Success Ratio

100%

Corporate Training

For India & Abroad

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Job Assistance

100%

BATCH TIMINGS

Google Cloud-Professional Data Engineer Upcoming Batches

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12-Dec-2022*

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03-Jan-2023*

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07-Dec-2022*

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05-Jan-2023

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CURRICULUM & PROJECTS

Google Cloud-Professional Data Engineer

  • A Google Certified Professional - Data Engineer enables data-driven decision making by collecting, transforming, and visualizing data. With our Professional Data Engineer Certification Training Program, you will learn how to design, build, maintain, and troubleshoot data processing systems with a particular emphasis on the security, reliability, fault-tolerance, scalability, fidelity, and efficiency of such systems.
  • In this program you will learn:
    • Data Processing and Data Storage Fundamentals in GCP

      Designing Data pipelines

      Design a Data Processing Solution

      Building and operationalizing data processing systems

      Operationalizing machine learning models

      Ensuring Solution Quality

Get full course syllabus in your inbox

  • Data Processing and Data Storage Fundamentals in GCP
    • Data processing Fundamentals

      • Data Processing Concepts
        • Data Processing Pipelines

          Data Storage Fundamentals

          • About GCP
            • Data Storage in GCP
              • Working with Data
                • Cloud Storage
                  • Data Transfer Services
                    • Cloud Fire Store
                      • Cloud Spanner
                        • Cloud Memory Store
                          • Different Memory options

                            Selecting the best memory storage

                            • Compare storage options
                              • Mapping storage systems to business requirements
                                • Data modeling
                                  • Trade-offs involving latency, throughput, transactions
                                    • Distributed systems
                                      • Schema design
                                    Get full course syllabus in your inbox

                                    • Designing Data pipelines
                                      • Data publishing and visualization

                                        Online (interactive) vs. batch predictions

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

                                        Big Data Ecosystem

                                        • MapReduce
                                          • Hadoop & HDFS
                                            • Apache Pig
                                              • Apache Spark
                                                • Apache Kafka

                                                  Real-time Messaging with Pub/Sub

                                                  • Pub/sub basics
                                                    • pub/Sub Terminologies
                                                      • Advanced Pub/Sub Concepts
                                                        • Working with Pub/Sub

                                                          Cloud Data Flow Pipelining

                                                          • Introduction to Data flow
                                                            • Pipeline Lifecycle
                                                              • Dataflow pipeline concepts
                                                                • Advanced Dataflow concepts
                                                                  • Dataflow security and access
                                                                    • Using Dataflow

                                                                      Cloud Dataproc

                                                                      • Dataproc Basics
                                                                        • Working with Dataproc
                                                                          • Advanced Dataproc

                                                                            NoSQL Data with Cloud Big Table

                                                                            • Big Table Concepts
                                                                              • Big Table Architecture
                                                                                • Big Table Data Model
                                                                                  • Big Table Schema Design
                                                                                    • Big Table Advanced Concepts

                                                                                      Data Analytics using BigQuery

                                                                                      • BigQuery Basics
                                                                                        • Using BigQuery
                                                                                          • Partitioning and Clustering
                                                                                            • Best Practices
                                                                                              • Securing BigQuery
                                                                                                • BigQuery Monitoring and Logging
                                                                                                  • Machine Learning with BigQuery ML
                                                                                                    • Working with BigQuery
                                                                                                      • Advanced BigQuery Concepts

                                                                                                        Data Exploration with Cloud Datalab

                                                                                                        • Datalab Concepts
                                                                                                          • Working with Datalab

                                                                                                            Visualization with Cloud Data Studio

                                                                                                            • Reporting & Business intelligence
                                                                                                              • Data Distribution
                                                                                                                • Introduction to Cloud Data Studio
                                                                                                                  • Charts and Filters

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

                                                                                                                    • Orchestration with Cloud Composer
                                                                                                                      • Cloud Composer Overview
                                                                                                                        • Cloud Composer Architecture
                                                                                                                          • Working with Cloud Composer
                                                                                                                            • Advanced Cloud Composer Concepts
                                                                                                                          Get full course syllabus in your inbox

                                                                                                                          • Design a Data Processing Solution
                                                                                                                            • Steps for Designing

                                                                                                                              • 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

                                                                                                                                            • 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
                                                                                                                                              Get full course syllabus in your inbox

                                                                                                                                              • Building and operationalizing data processing systems
                                                                                                                                                • Building and operationalizing Storage Solutions

                                                                                                                                                  • Cloud Managed Services
                                                                                                                                                    • Effectives Use of Managed Services
                                                                                                                                                      • Storage Cost and performance
                                                                                                                                                        • Lifecycle 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
                                                                                                                                                                        Get full course syllabus in your inbox

                                                                                                                                                                        • Operationalizing machine learning models
                                                                                                                                                                          • Introduction to Machine Learning

                                                                                                                                                                            • Machine Learning Introduction
                                                                                                                                                                            • Machine Learning Basics
                                                                                                                                                                            • Machine Learning Types and Models
                                                                                                                                                                            • Overfitting
                                                                                                                                                                            • Hyperparameters
                                                                                                                                                                            • Feature Engineering

                                                                                                                                                                            Machine Learning with TesnorFlow

                                                                                                                                                                            • Deep Learning with TensorFlow
                                                                                                                                                                            • Introduction to Artificial Neural Networks
                                                                                                                                                                            • Neural Network Architectures
                                                                                                                                                                            • Building a Neural Network

                                                                                                                                                                            Leveraging pre-built ML models as a service. Considerations include:

                                                                                                                                                                            • 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

                                                                                                                                                                            • Ingesting appropriate data
                                                                                                                                                                            • Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
                                                                                                                                                                            • Continuous evaluation

                                                                                                                                                                            Choosing the appropriate training and serving infrastructure

                                                                                                                                                                            • Distributed vs. single machine
                                                                                                                                                                            • Use of edge compute
                                                                                                                                                                            • Hardware accelerators (e.g., GPU, TPU)

                                                                                                                                                                            Measuring, monitoring, and troubleshooting machine learning models

                                                                                                                                                                            • 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)
                                                                                                                                                                        Get full course syllabus in your inbox

                                                                                                                                                                        • Ensuring Solution Quality
                                                                                                                                                                          • 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

                                                                                                                                                                            • Building and running test suites
                                                                                                                                                                            • Pipeline monitoring (e.g., Stack Driver)
                                                                                                                                                                            • Assessing, troubleshooting, and improving data representations and data processing infrastructure
                                                                                                                                                                            • Resizing and autoscaling resources

                                                                                                                                                                            Ensuring reliability and fidelity

                                                                                                                                                                            • Performing data preparation and quality control (e.g., Cloud 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

                                                                                                                                                                            • Mapping to current and future business requirements
                                                                                                                                                                            • Designing for data and application portability (e.g., multi-cloud, data residency requirements)
                                                                                                                                                                            • Data staging, catalog, and discovery
                                                                                                                                                                        Get full course syllabus in your inbox

                                                                                                                                                                        • Placement Guide
                                                                                                                                                                          • What is an Interview

                                                                                                                                                                            Tips to clear an Interview

                                                                                                                                                                            Common Interview questions and answers

                                                                                                                                                                            GCP Interview Questions and Answers

                                                                                                                                                                            Resume Building Guide

                                                                                                                                                                            Attempt for GCP Professional Data Engineer Certification Exam

                                                                                                                                                                            Earn Credentials and Start applying for Jobs

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                                                                                                                                                                        FAQ's

                                                                                                                                                                        • Basic computer knowledge
                                                                                                                                                                        • Basic knowledge of data processing systems
                                                                                                                                                                        • Passion for learning
                                                                                                                                                                        • Basic knowledge about GCP

                                                                                                                                                                        The Google Cloud-professional Data Engineer training program can be completed in 35-45 days

                                                                                                                                                                        You will learn from a skilled Google Cloud Data Engineer

                                                                                                                                                                        You can earn around ₹14,00,000 – ₹23,00,00 PA after completing the Google Cloud-Professional Data Engineer training program

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                                                                                                                                                                        Google Professional Data Engineer (GCP)

                                                                                                                                                                        Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. Candidate preparing for 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.

                                                                                                                                                                        formate
                                                                                                                                                                        Format

                                                                                                                                                                        Multiple Choice and Multi-Response Questions

                                                                                                                                                                        growth
                                                                                                                                                                        Type

                                                                                                                                                                        Data Engineer

                                                                                                                                                                        cost
                                                                                                                                                                        Cost

                                                                                                                                                                        $200 (Plus taxes as applicable)

                                                                                                                                                                        time
                                                                                                                                                                        Time

                                                                                                                                                                        120 Minutes

                                                                                                                                                                        delivery
                                                                                                                                                                        No of Questions

                                                                                                                                                                        15 Questions (Case Study)

                                                                                                                                                                        language
                                                                                                                                                                        Passing Score

                                                                                                                                                                        65% or above

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