GCP Data Engineering Training
Introduction to GCP for Data Engineering
Learn the basics of Google Cloud Platform (GCP) with a focus on data engineering. Understand the core GCP services and tools used for building and managing data pipelines and big data solutions.
Setting Up Your GCP Environment
Explore how to set up and configure a GCP account for data engineering tasks. Learn how to create and manage projects, set up billing, and navigate the Google Cloud Console.
Big Data Storage Solutions
Study the various storage options available in GCP, including Cloud Storage, BigQuery, and Cloud Bigtable. Learn how to choose the appropriate storage solution for different types of data and workloads.
Data Ingestion and ETL with Dataflow
Learn how to use Cloud Dataflow for data ingestion, transformation, and loading (ETL). Explore data pipelines, batch and stream processing, and how to build scalable and efficient data workflows.
Managing Data with BigQuery
Discover how to use BigQuery for large-scale data analytics. Learn how to query, analyze, and visualize data, and understand best practices for optimizing performance and cost.
Data Transformation and Processing with Dataproc
Explore Google Cloud Dataproc for managing Apache Hadoop and Spark clusters. Learn how to process and analyze large datasets, and how to integrate Dataproc with other GCP services.
Building and Managing Data Pipelines
Understand how to design, implement, and manage data pipelines using GCP tools. Learn about pipeline orchestration, data flow management, and error handling.
Data Security and Governance
Learn best practices for securing and managing data in GCP. Explore identity and access management (IAM), data encryption, and compliance considerations for data engineering.
Real-Time Data Processing with Pub/Sub
Discover how to use Google Cloud Pub/Sub for real-time data ingestion and messaging. Learn how to set up topics and subscriptions, and integrate Pub/Sub with other GCP services for real-time processing.
Data Visualization and Reporting
Learn how to create interactive and informative data visualizations using Google Data Studio and other GCP tools. Understand how to build dashboards and reports to gain insights from your data.
Preparing for GCP Data Engineering Certification
Discover the different certification paths for GCP Data Engineering. Learn tips and strategies for preparing for and passing the GCP Data Engineering certification exams.
Hands-On Labs and Projects
Engage in hands-on labs and projects to apply your knowledge of GCP data engineering tools and techniques. Work on real-world scenarios to develop practical skills in building and managing data solutions on GCP.
GCP Data Engineering Syllabus
Introduction to Google Cloud Platform (GCP)
- Overview of GCP Services and Products
- GCP Architecture and Regions
- Billing and Pricing Overview
GCP Fundamentals for Data Engineers
- Cloud Storage on GCP
- Cloud Storage
- Cloud SQL
- Bigtable
- Compute Engine and Kubernetes Engine
- Networking in GCP
- VPCs
- Load Balancing
Data Storage and Management on GCP
- Cloud Storage Options
- Cloud Storage
- Cloud Bigtable
- Cloud Spanner
- Datastore and Firestore for NoSQL Data
- Introduction to BigQuery for Data Warehousing
Data Processing and Transformation
- Introduction to Dataflow for Stream and Batch Processing
- Apache Beam and Dataflow Programming Model
- Transforming Data with Dataflow Pipelines
Data Integration and ETL on GCP
- Introduction to Cloud Pub/Sub for Messaging
- Using Cloud Dataflow for ETL Pipelines
- Data Transfer Service and Import/Export
Advanced Data Analysis and Machine Learning on GCP
- BigQuery ML for Machine Learning Models
- AI Platform for Model Training and Deployment
- Data Visualization with Data Studio
Monitoring, Logging, and Security in GCP
- Stackdriver for Monitoring and Logging
- Identity and Access Management (IAM)
- Data Security and Compliance on GCP
Data Engineering Best Practices
- Designing Scalable and Cost-efficient Architectures
- Performance Optimization Techniques
- CI/CD Pipelines for Data Engineering Workflows
Real-world Applications and Case Studies
- Implementing GCP Data Solutions in Different Industries
- Best Practices and Lessons Learned
- Project or Case Study Presentation
Training
Basic Level Training
Duration : 1 Month
Advanced Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
Please contact the office for details