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

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

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