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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

Speciality

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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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100+

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

Track Week Days Weekends Fast Track
Course Duration 40-45 Days 7 Weekends 8 Days
Hours 1 Hrs. Per Day 2 Hrs. Per Day 6+ Hrs. Per Day
Training Mode Classroom/Online Classroom/Online Classroom/Online
Course Price at :
24,75027,500
10% OFF Expires in01D 08H 53M 23S

Program fees are indicative only* Know more

Program Core Credentials

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Trainer Profiles

Industry Experts

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Trained Students

10000+

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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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27-May-2024*

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04-Jun-2024*

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29-May-2024*

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WEEKDAY

06-Jun-2024

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01-Jun-2024

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CURRICULUM & PROJECTS

Google Cloud-Professional Data Engineer

  • 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

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  • 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
    • 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

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  • 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

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  • 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

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  • 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)

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  • 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

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+ More Lessons

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Projects

Validate your skills and knowledge by working on industry-based projects that includes significant real-time use cases.Gain hands-on expertize in Top IT skills and become industry-ready after completing our project works and assessments.Our projects are perfectly aligned with the modules given in the curriculum and they are picked up based on latest industry standards. Add some meaningful project works in your resume, get noticed by top industries and start earning huge salary lumps right away.
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Learn, Grow & Test your skill with Online Assessment Exam to achieve your Certification Goals

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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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  • - Build an Impressive Resume
  • - Get Tips from Trainer to Clear Interviews
  • - Attend Mock-Up Interviews with Experts
  • - Get Interviews & Get Hired
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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.

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Format

Multiple Choice and Multi-Response Questions

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Type

Data Engineer

cost
Cost

$200 (Plus taxes as applicable)

time
Time

120 Minutes

delivery
No of Questions

15 Questions (Case Study)

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Passing Score

65% or above

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Adding the scope of improvement and fostering the analytical abilities and skills through the perfect piece of academic work.

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