Learn to develop reliable data processing systems. Join today to learn from an expert Google Data engineer.
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4.5/5Course Duration
30 Hrs.Live Project
2 ProjectCertification Pass
GuaranteedTraining Format
Live Online /Self-Paced/ClassroomWatch Live Classes
Speciality
250+
Professionals Trained
3+
Batches every month
20+
Countries & Counting
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:
- Prerequisites:
- This course is for:
Data processing fundamentals
Data storage fundamentals
Mapping storage systems
Data modeling
Streaming data
Dataflow pipelining
How to operationalize data processing systems
Basic computer knowledge
Basic knowledge of data processing systems
Passion for learning
Basic knowledge about GCP
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
- 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.
- Our top-notch faculty will help you:
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
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:
- 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.
Google Cloud Data Engineer
Train for roles like data architect, data solutions architect, etc.
Google Cloud professional
Google Cloud trainer
Contributor to the community
- 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:
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:
- Advantages of getting certification:
100% placement support
Interview grooming sessions
Resume preparation
Access to the job portal of Croma Campus
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 | 8Days |
| Hours | 1 Hrs. Per Day | 2Hrs. Per Day | 6+ Hrs. Per Day |
| Training Mode | Classroom/Online | Classroom/Online | Classroom/Online |
Course Price at :
Program fees are indicative only* Know more
Program Core Credentials
Trainer Profiles
Industry Experts
Trained Students
10000+
Success Ratio
100%
Corporate Training
For India & Abroad
Job Assistance
100%
BATCH TIMINGS
Google Cloud-Professional Data Engineer Upcoming Batches
WEEKDAY
27-Mar-2023*
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04-Apr-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
- Data Processing and Data Storage Fundamentals in GCP
- Data Processing Concepts
- Data Processing Pipelines
- 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
- Compare storage options
- Mapping storage systems to business requirements
- Data modeling
- Trade-offs involving latency, throughput, transactions
- Distributed systems
- Schema design
Data processing Fundamentals
Data Storage Fundamentals
Selecting the best memory storage
- Designing Data pipelines
- MapReduce
- Hadoop & HDFS
- Apache Pig
- Apache Spark
- Apache Kafka
- Pub/sub basics
- pub/Sub Terminologies
- Advanced Pub/Sub Concepts
- Working with Pub/Sub
- Introduction to Data flow
- Pipeline Lifecycle
- Dataflow pipeline concepts
- Advanced Dataflow concepts
- Dataflow security and access
- Using Dataflow
- Dataproc Basics
- Working with Dataproc
- Advanced Dataproc
- Big Table Concepts
- Big Table Architecture
- Big Table Data Model
- Big Table Schema Design
- Big Table Advanced Concepts
- 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
- Datalab Concepts
- Working with Datalab
- Reporting & Business intelligence
- Data Distribution
- Introduction to Cloud Data Studio
- Charts and Filters
- Orchestration with Cloud Composer
- Cloud Composer Overview
- Cloud Composer Architecture
- Working with Cloud Composer
- Advanced Cloud Composer Concepts
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
Real-time Messaging with Pub/Sub
Cloud Data Flow Pipelining
Cloud Dataproc
NoSQL Data with Cloud Big Table
Data Analytics using BigQuery
Data Exploration with Cloud Datalab
Visualization with Cloud Data Studio
Job automation and orchestration (e.g., Cloud Composer)
- Design 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 (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
- At least once, in-order, and exactly once, etc., event 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
Steps for Designing
Migrating data warehousing and data processing
- Building and operationalizing data processing systems
- Cloud Managed Services
- Effectives Use of Managed Services
- Storage Cost and performance
- Lifecycle Management of Data
- Data cleansing
- Batch and streaming
- Transformation
- Data acquisition and import
- Integrating with new data sources
- Provisioning resources
- Monitoring pipelines
- Adjusting pipelines
- Testing and quality control
Building and operationalizing Storage Solutions
Building and operationalizing Pipelines
Building and operationalizing processing infrastructure
- Operationalizing machine learning models
- Machine Learning Introduction
- Machine Learning Basics
- Machine Learning Types and Models
- Overfitting
- Hyperparameters
- Feature Engineering
- Deep Learning with TensorFlow
- Introduction to Artificial Neural Networks
- Neural Network Architectures
- Building a Neural Network
- ML APIs (e.g., Vision API, Speech API)
- Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
- Conversational experiences (e.g., Dialogflow)
- Ingesting appropriate data
- Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
- Continuous evaluation
- Distributed vs. single machine
- Use of edge compute
- Hardware accelerators (e.g., GPU, TPU)
- 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)
Introduction to Machine Learning
Machine Learning with TesnorFlow
Leveraging pre-built ML models as a service. Considerations include:
Deploying an ML pipeline
Choosing the appropriate training and serving infrastructure
Measuring, monitoring, and troubleshooting machine learning models
- Ensuring Solution Quality
- 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))
- 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
- 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
- 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
Designing for security and compliance
Ensuring scalability and efficiency
Ensuring reliability and fidelity
Ensuring flexibility and portability
- 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
+ More Lessons
Mock Interviews
Projects
Phone (For Voice Call):
+91-971 152 6942WhatsApp (For Call & Chat):
+918287060032self assessment
Learn, Grow & Test your skill with Online Assessment Exam to achieve your Certification Goals
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
- - Build an Impressive Resume
- - Get Tips from Trainer to Clear Interviews
- - Attend Mock-Up Interviews with Experts
- - Get Interviews & Get Hired
If yes, Register today and get impeccable Learning Solutions!
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.
Multiple Choice and Multi-Response Questions
Data Engineer
$200 (Plus taxes as applicable)
120 Minutes
15 Questions (Case Study)
65% or above
Training Features
Instructor-led Sessions
The most traditional way to learn with increased visibility,monitoring and control over learners with ease to learn at any time from internet-connected devices.
Real-life Case Studies
Case studies based on top industry frameworks help you to relate your learning with real-time based industry solutions.
Assignment
Adding the scope of improvement and fostering the analytical abilities and skills through the perfect piece of academic work.
Lifetime Access
Get Unlimited access of the course throughout the life providing the freedom to learn at your own pace.
24 x 7 Expert Support
With no limits to learn and in-depth vision from all-time available support to resolve all your queries related to the course.
Certification
Each certification associated with the program is affiliated with the top universities providing edge to gain epitome in the course.
Training Certification
Your certificate and skills are vital to the extent of jump-starting your career and giving you a chance to compete in a global space.
Talk about it on Linkedin, Twitter, Facebook, boost your resume or frame it- tell your friend and colleagues about it.
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Testimonials & Reviews
Hi, my name is Vivek Aggrawal, Currently, I am associated with Aptron Technologies. I got out of this job opportunity after completing my Google Cloud Certification Course from Croma Campus Pvt. Ltd. In the training period, I have got
Read More...Vivek Aggrawal
Aptron Technologies
I have completed my Google Cloud-Associate Cloud Engineer training from Croma Campus. And I can assure you that Croma Campus is the best training institute for Google Cloud training. They provide high-quality training which will help y
Read More...Inaya
I completed the Google Cloud-Professional data engineering training program from Croma Campus last month. My experience has been very positive with these people. With their help and support, I have got placed as a cloud computing exper
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Hi, I am Mayank Sharma from Jaipur. This course is most demanded in the industry. Croma Campus has structured the course very well. This course helps me in making me knowledgeable and skillable. The trainer is very upgraded with the te
Read More...Mayank Sharma
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