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 Certification.
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 Course 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.
- Heres 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 | 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 :
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
25-Nov-2024*
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FASTRACK
03-Dec-2024*
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27-Nov-2024*
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05-Dec-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
- 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
- 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
- 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
- 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)
- 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
+ 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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