Google Cloud [GCP] Professional Data Engineer Training Institute & Certification Exam Center

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The Google Cloud Professional Data Engineer training program provides comprehensive instruction on the tools and techniques needed to design and manage data processing systems on the Google Cloud Platform (GCP). Participants learn to develop and deploy data pipelines, build and optimize data models, perform data analysis, and ensure data security and compliance. The course covers a range of GCP services such as BigQuery, Dataflow, Dataprep, and Pub/Sub. Through hands-on labs and real-world scenarios, students gain practical experience in designing scalable and efficient data solutions. This training prepares individuals for the Professional Data Engineer certification exam, validating their expertise in handling data on GCP.

Google Cloud [GCP] Professional Data Engineer Training Key Features

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Google Cloud [GCP] Professional Data Engineer Training Calender

Start Date Training Mode Enroll Status
May 28, 2024
10:00 - 13:00 (IST)
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Jun 05, 2024
13:00 - 16:00 (IST)
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May 18, 2024
14:00 - 17:00 (IST)
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Google Cloud [GCP] Professional Data Engineer Overview

Google Cloud Certified Professional Data Engineer is a globally valued certification that hones your skills to make data-driven decisions by optimizing the captured data. The certification training expertly demonstrate how to efficiently collect, transform, and visualize data to generate useful insights. This comprehensive training aims to render practical understanding to design, build, maintain, and troubleshoot the data processing systems while emphasizing on the crucial aspects of the system including reliability, scalability, fault-tolerance, fidelity, security, and efficiency.
A Data Engineer is accountable to perform data analysis to anticipate prospected business outcomes, develop statistical models for supporting the decision-making, and builds machine learning models for simplifying and automating primary processes of the business.

Prerequisites

  • Basic understanding of cloud,databases and data analysis

Who is it for?

This Google cloud Professional Data Engineer certification training helps advancing the career options for:
  • Data architects
  • Data engineers
  • Developers managing big data transformations
  • Data analysts

Section 1: Designing data processing systems (~22% of the exam)

1.1 Designing for security and compliance. Considerations include: 
    ●  Identity and Access Management (e.g., Cloud IAM and organization policies)
    ●  Data security (encryption and key management)
    ●  Privacy (e.g., personally identifiable information, and Cloud Data Loss Prevention API)
    ●  Regional considerations (data sovereignty) for data access and storage
    ●  Legal and regulatory compliance
1.2 Designing for reliability and fidelity. Considerations include:
    ●  Preparing and cleaning data (e.g., Dataprep, Dataflow, and Cloud Data Fusion)
    ●  Monitoring and orchestration of data pipelines
    ●  Disaster recovery and fault tolerance
    ●  Making decisions related to ACID (atomicity, consistency, isolation, and durability) compliance and availability
    ●  Data validation
1.3 Designing for flexibility and portability. Considerations include:
    ●  Mapping current and future business requirements to the architecture
    ●  Designing for data and application portability (e.g., multi-cloud and data residency requirements)
    ●  Data staging, cataloging, and discovery (data governance)
1.4 Designing data migrations. Considerations include:
    ●  Analyzing current stakeholder needs, users, processes, and technologies and creating a plan to get to desired state
    ●  Planning migration to Google Cloud (e.g., BigQuery Data Transfer Service, Database Migration Service, Transfer Appliance, Google Cloud networking, Datastream)
    ●  Designing the migration validation strategy
    ●  Designing the project, dataset, and table architecture to ensure proper data governance 

Section 2: Ingesting and processing the data (~25% of the exam)

2.1 Planning the data pipelines. Considerations include:
    ●  Defining data sources and sinks
    ●  Defining data transformation logic
    ●  Networking fundamentals
    ●  Data encryption
2.2 Building the pipelines. Considerations include:
    ●  Data cleansing
    ●  Identifying the services (e.g., Dataflow, Apache Beam, Dataproc, Cloud Data Fusion, BigQuery, Pub/Sub, Apache Spark, Hadoop ecosystem, and Apache Kafka)
    ●  Transformations
        ○  Batch
        ○  Streaming (e.g., windowing, late arriving data)
        ○  Language
        ○  Ad hoc data ingestion (one-time or automated pipeline)
    ●  Data acquisition and import
    ●  Integrating with new data sources 
2.3 Deploying and operationalizing the pipelines. Considerations include:
    ●  Job automation and orchestration (e.g., Cloud Composer and Workflows)
    ●  CI/CD (Continuous Integration and Continuous Deployment)

Section 3: Storing the data (~20% of the exam)

3.1 Selecting storage systems. Considerations include:
    ●  Analyzing data access patterns
    ●  Choosing managed services (e.g., Bigtable, Cloud Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore)
    ●  Planning for storage costs and performance
    ●  Lifecycle management of data
3.2 Planning for using a data warehouse. Considerations include:
    ●  Designing the data model
    ●  Deciding the degree of data normalization
    ●  Mapping business requirements
    ●  Defining architecture to support data access patterns
3.3 Using a data lake. Considerations include:
    ●  Managing the lake (configuring data discovery, access, and cost controls)
    ●  Processing data
    ●  Monitoring the data lake
3.4 Designing for a data mesh. Considerations include:
    ●  Building a data mesh based on requirements by using Google Cloud tools (e.g., Dataplex, Data Catalog, BigQuery, Cloud Storage)
    ●  Segmenting data for distributed team usage
    ●  Building a federated governance model for distributed data systems

Section 4: Preparing and using data for analysis (~15% of the exam)

4.1 Preparing data for visualization. Considerations include:
    ●  Connecting to tools
    ●  Precalculating fields
    ●  BigQuery materialized views (view logic)
    ●  Determining granularity of time data
    ●  Troubleshooting poor performing queries
    ●  Identity and Access Management (IAM) and Cloud Data Loss Prevention (Cloud DLP)
4.2 Sharing data. Considerations include:
    ●  Defining rules to share data
    ●  Publishing datasets
    ●  Publishing reports and visualizations
    ●  Analytics Hub
4.3 Exploring and analyzing data. Considerations include:
    ●  Preparing data for feature engineering (training and serving machine learning models)
    ●  Conducting data discovery

Section 5: Maintaining and automating data workloads (~18% of the exam)

5.1 Optimizing resources. Considerations include:
    ●  Minimizing costs per required business need for data
    ●  Ensuring that enough resources are available for business-critical data processes
    ●  Deciding between persistent or job-based data clusters (e.g., Dataproc)
5.2 Designing automation and repeatability. Considerations include:
    ●  Creating directed acyclic graphs (DAGs) for Cloud Composer
    ●  Scheduling jobs in a repeatable way 
5.3 Organizing workloads based on business requirements. Considerations include:
    ●  Flex, on-demand, and flat rate slot pricing (index on flexibility or fixed capacity)
    ●  Interactive or batch query jobs
5.4 Monitoring and troubleshooting processes. Considerations include:
    ●  Observability of data processes (e.g., Cloud Monitoring, Cloud Logging, BigQuery admin panel)
    ●  Monitoring planned usage
    ●  Troubleshooting error messages, billing issues, and quotas
    ●  Manage workloads, such as jobs, queries, and compute capacity (reservations)
5.5 Maintaining awareness of failures and mitigating impact. Considerations include:
    ●  Designing system for fault tolerance and managing restarts
    ●  Running jobs in multiple regions or zones
    ●  Preparing for data corruption and missing data
    ●  Data replication and failover (e.g., Cloud SQL, Redis clusters)
Note: **The topics said above are only the short blueprint of the syllabus. On the off chance that you feel that we have missed any subject, you can simply come to us and learn it, or simply call us to affirm

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Companies who have been benefited with his experience & knowledge Atos, Cloud reach, IBM, Samsung R&D, Wipro, Dell, HPE, GE, JP Morgan, Wells Fargo, RBS, Vodafone, Airtel, Nokia, Ericsson, Accenture, Capgemini and many more
 They have More than 10+ years of experience in Linux and related technologies.
 Our Trainers are expert level and completely up-to- date in the subjects they teach because they continue to spend time working on real- world industry applications.
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Google Cloud [GCP] Professional Data Engineer Certification Bootcamp

Google cloud Professional Data Engineer Training & certification Center

The Google Cloud Professional Data Engineer Certification Bootcamp is an intensive training program tailored to individuals seeking to prepare for the Google Cloud Platform (GCP) Professional Data Engineer certification exam. This bootcamp equips participants with the knowledge and skills necessary to design, build, operationalize, and secure data processing systems on Google Cloud.


Key Features of the Bootcamp:


1. Comprehensive Curriculum:
The bootcamp covers all topics outlined in the Professional Data Engineer exam guide provided by Google Cloud. This includes data processing, data storage, data analysis, machine learning, and data visualization on GCP.

2. Expert Instructors:
Participants are guided by experienced data engineers or trainers who possess deep expertise in Google Cloud technologies and have practical experience in building data solutions on GCP.

3. Hands-on Labs:
The bootcamp includes hands-on labs and practical exercises that allow participants to apply theoretical concepts in real-world scenarios. These labs help reinforce learning and provide valuable hands-on experience with GCP data services and tools.

4. Exam Preparation:
The bootcamp is structured to thoroughly prepare participants for the Professional Data Engineer certification exam. It covers exam topics in detail, provides exam-taking strategies, and offers practice exams to assess readiness.

5. Interactive Learning:
The bootcamp may include interactive sessions, group discussions, and Q&A sessions to facilitate active engagement and enhance learning outcomes. Participants have the opportunity to ask questions, seek clarification, and engage in peer-to-peer learning.

6. Networking Opportunities:
Participants can network with peers, industry experts, and instructors, fostering collaboration and knowledge sharing within the data engineering community. This networking can lead to valuable connections and career opportunities.

7. Flexible Delivery:
Bootcamps may be delivered in various formats, including in-person workshops, virtual classrooms, or self-paced online courses, to accommodate different learning preferences and schedules. Participants can choose the format that best suits their needs.


Prerequisites for the Bootcamp:


While prerequisites may vary depending on the specific bootcamp provider, participants are typically expected to have:

- Basic knowledge of data engineering concepts and principles.
- Familiarity with Google Cloud Platform services and products, particularly those related to data processing, storage, and analysis.
- Experience working with data engineering tools and technologies.


Benefits of the Bootcamp:


- Accelerated Learning:
The bootcamp offers a condensed and focused learning experience, enabling participants to acquire the necessary skills and knowledge efficiently.
- Certification Achievement
: Successful completion of the bootcamp prepares participants to confidently sit for the Google Cloud Professional Data Engineer exam and earn the certification.
- Career Advancement:
The certification validates participants' expertise in data engineering on Google Cloud Platform, opening up new career opportunities in the data engineering field.

Overall, the Google Cloud Professional Data Engineer Certification Bootcamp is an intensive and immersive training program designed to equip individuals with the skills and knowledge needed to excel in designing and building data solutions on Google Cloud Platform.

Our Recent Certified Candidates

Real Exam Format and Information

Exam Name
Google Cloud Professional Data Engineer
Exam Duration :
120 Minutes
Number of Questions :
15 To 20
Exam Fee :
$200
Validity :
2 years
Availability :
Pearson VUE
Types Of Questions :
Multiple Choice And Multi- Responce Questions
Passing Score :
No Scoring Criteria
Eligibility/Prerequisite :
None
Exam Languages :
English, Japanese, Korean, and Simplified Chinese


Google Cloud [GCP] Professional Data Engineer Benefits & Job

Over the past ten years, cloud computing has transformed from a "nice-to-have" to a "must-have" technology requirement in today's business environment. The need for an IT specialist who can manage the migration process and perform cloud-related tasks has increased as a result of this seismic change.
An IT specialist known as a cloud engineer is in charge of all technical aspects of cloud computing, including design, planning, management, upkeep, and support of an organization's current infrastructure as well as exploring options for moving different functions (like database storage) to a cloud-based system. Then, on the new machine, this individual migrates and keeps the function running.
Cloud Engineers must be technically proficient, have the ability to bargain with vendors, manage data security, and more in order to complete the move.
The term “cloud engineer” refers to a variety of positions, including:
    Architect for the cloud
    Engineer for cloud software
    Engineer in charge of cloud security
    Engineer for cloud-based systems
    Engineer in charge of cloud networks
Instead of focusing on technology as a whole, each role focuses on a specific type of cloud computing.
Companies that want to deploy cloud services, scale up their use of cloud resources, or improve their cloud knowledge and technology frequently hire cloud engineers.
The average annual salary of a cloud engineer in India is around 7.5-8 lakh rupees, according to Indeed's January 2020 study.
This data came from a review of 228 salaries submitted to Indeed in the previous three years.
Given the rapidly increasing demand for cloud engineers and the scarcity of supply, this is expected to skyrocket in the coming years.

Looking at cloud engineer salaries in India by job title is the best way to figure out what you're capable of.
As you advance up the corporate ladder as a cloud engineer, your salary will rise.
The better the company you work for, the more likely it is that you will receive a significant pay raise.

Experience
Average Salary per Annum
0-3 years
₹12,41,000
4-6 years
₹17,44,817 – ₹19,00,369
You can expect to earn three times as much as a new employee in just four years.
However, not everything in life is straightforward.
You must hone your skills and stay current with industry developments.
Top Cloud Engineering Companies in India
The average salary of a cloud engineer varies greatly depending on that company where you work.
Examine the pay packages offered by some of India's most prestigious companies.
         
Company
Average Salary per annum
Microland
₹13,45,871
Adobe
₹7,60,000
Microsoft
₹14,00,000
HCL Technologies
₹7,00,000
Nivio Technologies
₹18,53,084 
Cloud Engineer Salary in Other Countries
Another important factor to consider is how much these salaries vary by region. The list of Cloud Engineer Salaries in other countries is organized by location.
    
Location
Average Salary per annum
Cloud Engineer in New York, NY
$139,440
Cloud Engineer in Boston, MA
$124,803
Cloud Engineer in Chicago, IL
$118,758
Cloud Engineer in Atlanta, GA
$113,709
Cloud Engineer in Austin, TX
$115,084

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Google Cloud [GCP] Professional Data Engineer Recent Reviews

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Google Cloud [GCP] Professional Data Engineer FAQ

  • What is the purpose of the Google Professional Cloud Data Engineer training and certification?

    The Google Professional Cloud Data Engineer certification is designed to provide individuals with the knowledge and skills necessary to design, build, and maintain data processing systems on Google Cloud Platform (GCP).
  • What are the prerequisites for the Google Professional Cloud Data Engineer certification?

    It is recommended to have prior experience with GCP and a working knowledge of data processing and the Google Certified Professional Cloud Architect or equivalent experience.
  • What are the key topics covered in the Google Professional Cloud Data Engineer training?

    The key topics covered in the Google Professional Cloud Data Engineer training include data modeling, data warehousing, data processing, and data governance on GCP.
  • How long does it take to prepare for the Google Professional Cloud Data Engineer certification?

    The time it takes to prepare for the Google Professional Cloud Data Engineer certification varies depending on the individual's prior knowledge and experience. It is recommended to allocate several weeks to study and prepare for the certification exam.
  • What are the best resources to prepare for the Google Professional Cloud Data Engineer certification?

    The best resources to prepare for the Google Professional Cloud Data Engineer certification include GCP documentation, GCP training courses, GCP whitepapers, practice tests, and the Google Cloud Certified - Professional Cloud Data Engineer Study Guide.
  • What is the format of the Google Professional Cloud Data Engineer certification exam?

    The format of the Google Professional Cloud Data Engineer certification exam is multiple-choice, with 48 questions to be completed within 120 minutes.
  • What is the passing score for the Google Professional Cloud Data Engineer certification exam?

    The passing score for the Google Professional Cloud Data Engineer certification exam is not publicly disclosed.
  • How often is the Google Professional Cloud Data Engineer certification exam offered?

    The Google Professional Cloud Data Engineer certification exam is offered continuously and can be taken at any time at a testing center or online through a proctoring service.
  • What are the benefits of the Google Professional Cloud Data Engineer certification?

    The benefits of the Google Professional Cloud Data Engineer certification include increased credibility and recognition as a GCP expert, improved job opportunities and career advancement, and increased knowledge and understanding of data processing on GCP
  • How long is the Google Professional Cloud Data Engineer certification valid for?

    Google Professional Cloud Data Engineer certification is valid for two years from the date of passing the certification exam. After two years, the individual will need to retake the certification exam to maintain their Google Professional Cloud Data Engineer status.
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