GCP Data Engineering Course in PCMC Pune

1. Why is GCP Data Engineering only for experienced professionals?

While entry-level positions help build basic skills, companies prefer hiring experienced GCP Data Engineers because they bring important knowledge, technical skills, and problem-solving abilities. These professionals can design efficient, scalable, and secure data solutions in GCP, which are vital for today’s data-driven businesses. Experience allows them to handle the complexities of cloud platforms, minimize risks, and deliver high-quality data systems, making them valuable to organizations.`

GCP Data Engineering is one of the most in-demand and rewarding skills in the tech industry today. As more businesses rely on cloud technologies and large amounts of data are created every day, knowing how to use Google Cloud Platform (GCP) is very important for companies that want to manage, process, and analyze data on a large scale.

Many professionals aren’t yet familiar with cloud technologies like Google Cloud Platform (GCP), so there aren’t enough good training materials available for learning it deeply. This has created a big opportunity for skilled professionals, as there is a shortage of qualified GCP Data Engineers.

To meet the growing demand, companies are offering unconditional salaries to attract and retain experienced candidates with expertise in GCP.. Some companies are even training their own teams to fill this skill gap. This creates a great chance for professionals with industry experience to enter a rapidly growing field, with, offering high job security, competitive salaries, and long-term career prospects

2. Pre-requisites:

• SQL & PL/SQL (Stored Functions, Stored Procedures, Indexes)
• Python
• Spark (PySpark, Spark SQL)

3. GCP Services: A Must for Data Engineers:

1. GCP Bucket
   • Structured Data: e.g., Table Format (CSV, Excel)
   • Unstructured Data: e.g., Videos, Images, Text, PDFs
   • Semi-Structured Data: e.g., JSON Files
2. Cloud SQL and Cloud Spanner
3. Google BigQuery
4. Dataflow
5. Apache Airflow
6. Cloud Composer
7. Dataprep
8. Dataproc
9. Cloud Data Fusion
10. Google Pub/Sub
11. Machine Learning (ML) on GCP
    • BigQuery ML
    • Vertex AI
12. Data Lake & Delta Lake on GCP
13. DataOps on GCP
14. Data Visualization
15. Building ETL Pipelines

End-to-End Projects (At least 2)

1. Data Ingestion End-to-End Pipeline: using Cloud Composer, Dataflow, BigQuery, Pub/Sub
2. Data Ingestion End-to-End Pipeline: using other GCP services

Comparing Coding Complexity: GCP Data Engineers vs Java Developers

GCP Data Engineers: Coding complexity is around 40% compared to Java Developers, where the complexity is considered 100%.

Why:  GCP doesn’t have as many drag-and-drop services compared to AWS and Azure

5. Career Opportunities

1. For Experienced Professionals:
   • GCP Data Engineer
   • Cloud Data Engineer
   • Big Data Engineer
   • Data Analyst
   • Cloud Solutions Architect

6. Job Openings:

GCP (Google Cloud Platform) is becoming very popular in the U.S. for cloud-based projects. Companies are moving their projects to GCP because it offers powerful tools for handling large data and machine learning. GCP’s popularity is growing every day as more businesses see its benefits for cost savings and scalability.

7. Eligibility Criteria for Learning GCP Data Engineering:

Freshers should not focus on learning GCP skill sets, as most job openings are available for professionals with a minimum of 2+ years of experience or more.

8. Why GCP is a Valuable Skill Set for Experienced Professionals:

Demand & Growth: Businesses are shifting to the cloud to improve efficiency, save costs, and scale easily. GCP helps with data processing, storage, and machine learning, making it essential in industries like healthcare, retail, and finance.The global cloud computing market is expected to grow from $600 billion in 2023 to $1.6 trillion by 2030. This growth creates a high demand for skilled professionals who can manage and optimize cloud infrastructures, especially those with GCP expertise.

Job Growth: Many job openings for GCP roles, such as Data Engineers and Cloud Architects, are appearing, especially in the U.S. and US-based clients These roles offer competitive salaries.

9. Annual CTC for Azure Data Engineers (Based on Experience):

1. 2-4 Years Experience (Junior/Mid-Level):
   • India: ₹6–14 LPA
   • US: $80,000–$110,000
   • Job Role: At this level, Data Engineers typically manage cloud services, build and maintain data pipelines, and integrate data across systems. They may also work with more complex data structures and perform data transformations and analysis.
2. 4-8 Years Experience (Mid-Level/Senior):
   • India: ₹10–25 LPA
   • US: $110,000–$140,000
   • Job Role: Mid-level engineers lead project execution, optimize cloud infrastructure, and build and maintain data pipeline. create scalable solutions for managing large data sets. They are expected to mentor junior engineers, manage cloud architecture, and ensure efficient data flow across platforms.
3. 8+ Years Experience (Senior/Lead):
   • India: ₹20–55+ LPA
   • US: $140,000–$160,000+
   • Job Role: Senior-level engineers take charge of strategic decisions, cloud architecture design, and managing teams. They ensure the implementation of cutting-edge solutions, including machine learning models, data analytics, and large-scale data processing, often involving business decision-making at the highest levels.

Pre-requisites:

• SQL & PL/SQL (Stored Functions, Stored Procedures, Indexes)
• Python
• Spark (PySpark, Spark SQL)

GCP Services: A Must for Data Engineers:

1. GCP Bucket
   • Structured Data: e.g., Table Format (CSV, Excel)
   • Unstructured Data: e.g., Videos, Images, Text, PDFs
   • Semi-Structured Data: e.g., JSON Files
2. Cloud SQL and Cloud Spanner
3. Google BigQuery
4. Dataflow
5. Apache Airflow
6. Cloud Composer
7. Dataprep
8. Dataproc
9. Cloud Data Fusion
10. Google Pub/Sub
11. Machine Learning (ML) on GCP
    • BigQuery ML
    • Vertex AI
12. Data Lake & Delta Lake on GCP
13. DataOps on GCP
14. Data Visualization
15. Building ETL Pipelines

End-to-End Projects (At least 2)

1. Data Ingestion End-to-End Pipeline: using Cloud Composer, Dataflow, BigQuery, Pub/Sub
2. Data Ingestion End-to-End Pipeline: using other GCP services