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GCP Data Engineering

Google Cloud Platform (GCP) Data Engineering encompasses the design, implementation, and management of data systems and workflows using GCP’s comprehensive suite of cloud-based tools. Key components include:

1. **Data Ingestion and ETL**: Using services like **Google Cloud Dataflow** for stream and batch data processing, and **Google Cloud Dataproc** for managed Apache Spark and Hadoop clusters. These tools help in transforming and preparing data for analysis.

2. **Data Storage**: Utilizing **Google Cloud Storage** for scalable object storage and **BigQuery** for serverless, highly scalable, and cost-effective data warehousing. This allows for efficient storage and querying of large datasets.

3. **Data Integration and Orchestration**: Employing **Cloud Composer**, based on Apache Airflow, to schedule and manage data workflows and dependencies, ensuring smooth operation of data pipelines.

4. **Data Security and Management**: Implementing **Google Cloud Identity and Access Management (IAM)** to control access, and using **Cloud Data Loss Prevention (DLP)** to protect sensitive information.

5. **Analytics and Visualization**: Leveraging **BigQuery** for advanced analytics and **Google Data Studio** for creating interactive dashboards and reports, enabling insightful data-driven decision-making.

Overall, GCP Data Engineering focuses on building robust, scalable, and efficient data systems that support various business needs, from real-time analytics to complex data processing.

Course Instructor Sateesh Pabbathi

Original price was: ₹30,000.00.Current price is: ₹24,999.00.

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Course Overview

Google Cloud Platform (GCP) Data Engineering involves using GCP’s suite of tools and services to design, build, and manage data pipelines and architectures. This includes extracting, transforming, and loading (ETL) data using services like Google Cloud Dataflow and Dataproc, storing and querying data in BigQuery or Cloud Storage, and orchestrating workflows with Cloud Composer. Data engineers on GCP focus on ensuring data is clean, reliable, and accessible for analytics and machine learning applications.

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Course Curriculum

MODULE 1: INTRODUCTION TO GCP AND DATA ENGINEERING

Overview of Google Cloud Platform


GCP Services and Offerings


GCP Console and Cloud Shell
Introduction to Data Engineering


Data Engineering Concepts and Principles


Data Storage Options on GCP


Data Security and Compliance on GCP


Best Practices for Data Engineering on GCP

MODULE 2: DATA INGESTION AND EXTRACTION

Data Ingestion Strategies


Batch vs. Real-time Data Ingestion


Pub/Sub: GCP Messaging Service


Setting Up Pub/Sub Topics and Subscriptions


Data Extraction from APIs


Web Scraping for Data Extraction


Data Ingestion Patterns and Best Practices


Monitoring and Managing Data Ingestion Pipelines

MODULE 3: DATA TRANSFORMATION AND PROCESSING

Data Transformation with Dataflow


Writing and Deploying Dataflow Pipelines


Windowing and Triggers in Dataflow


Data Processing with Dataproc

Introduction to Apache Spark and Hadoop


Running Spark Jobs on Dataproc


Performance Optimization in Data Transformation


Error Handling in Data Processing Pipelines

MODULE 4: DATA STORAGE AND WAREHOUSING

BigQuery: Serverless Data Warehouse


Creating Datasets and Tables in BigQuery


SQL Queries and Advanced Analytics in BigQuery


Data Storage Considerations in GCP


Choosing Between Storage Options (e.g., Cloud SQL, Cloud
Storage)


Data Partitioning Strategies for Performance


Data Warehousing Best Practices


Data Security in Data Warehousing on GCP

MODULE 5: DATA ORCHESTRATION AND WORKFLOW AUTOMATION

Composer: Managed Airflow


Setting Up and Scheduling Workflows in Composer

DAG (Directed Acyclic Graph) Creation and Execution


Error Handling and Retry Strategies in Workflows


Monitoring and Logging in Data Workflows


Scalability and Resource Management in Composer


Best Practices for Data Orchestration


Continuous Integration and Deployment (CI/CD) for Data Pipelines

MODULE 6: DATA GOVERNANCE, SECURITY, AND COMPLIANCE

Data Governance Best Practices


Data Security and Encryption on GCP


Compliance and Regulatory Considerations


Auditing and Monitoring Data Access


Managing Data Lifecycle and Retention Policies


Data Privacy and GDPR Compliance


Disaster Recovery Planning and Testing


Case Studies: Security and Compliance in Data Engineering

MODULE 7: ADVANCED TOPICS IN GCP DATA ENGINEERING

Data Streaming Architectures and Use Cases


Data Lake Architecture and Implementation in GCP


Real-time Data Analytics with BigQuery and Dataflow

Machine Learning Integration with GCP Data Pipelines


IoT Data Processing and Analytics on GCP


Data Pipeline Optimization and Cost Management


Data Quality and Data Governance in GCP


Emerging Trends in Data Engineering on GCP