GCP Data Engineer Training:
In modern technology, data is the foundation of business success, and companies are increasingly adopting cloud-based data solutions to handle massive datasets efficiently. Google Cloud Platform (GCP) provides powerful tools and services that allow organizations to store, process, analyze, and manage data at scale. A Google Cloud Platform (GCP) Data Engineer is specialized in designing, building, and managing scalable data infrastructure using Google Cloud services. They handle data ingestion, processing, storage, and analysis, enabling organizations to extract valuable insights and drive business decisions.GCP Data Engineers work with structured and unstructured data, optimizing real-time and batch data processing workflows to support analytics, machine learning, and AI applications. They leverage fully managed services in Google Cloud to ensure cost-efficient, secure, and high-performance data solutions. Our GCP Data Engineer Course is designed to help you master the skills needed to design, build, and manage scalable data pipelines using Google Cloud Platform. This GCP Data Engineer Training covers core GCP tools like BigQuery for data warehousing, Dataflow for stream and batch processing, Pub/Sub for real-time data ingestion, and Dataproc for big data processing. Through hands-on labs and real-world projects, this GCP Data Engineer Online Course provides practical experience in creating secure, efficient, and scalable data solutions. Whether you’re new to cloud data engineering or looking to enhance your skills, this course offers a clear path to turning raw data into actionable insights with GCP.What Will You Learn?
- 1. Google Cloud Fundamentals & Data Engineering Concepts:You will start with an introduction to Google Cloud Platform (GCP) and learn about its core data engineering services. You’ll understand cloud storage, computing, security, and IAM (Identity and Access Management) to efficiently manage data in a scalable, secure environment.
- 2. BigQuery – Cloud Data Warehousing & Analytics: BigQuery is Google’s fully managed, serverless data warehouse, designed for high-speed analytics on massive datasets. You will learn how to: Load, transform, and query structured and semi-structured data. Optimize queries and improve performance using partitioning and clustering. Integrate BigQuery with BI tools like Looker, Data Studio, and Tableau.
- 3. Dataflow – Real-Time & Batch Data Processing: Dataflow, powered by Apache Beam, allows you to process data streams and batch workloads efficiently. You will learn how to: Design and implement streaming and batch data pipelines. Work with real-time data processing for analytics and machine learning.Handle event-driven architectures and IoT data processing.
- 4. Pub/Sub – Real-Time Messaging & Event-Driven Processing: Google Cloud Pub/Sub is essential for real-time data ingestion and messaging. You will learn how to: Build event-driven architectures for data streaming applications. Process real-time messages from IoT devices, logs, and other sources. Integrate Pub/Sub with Dataflow and BigQuery for real-time analytics.
- 5. Dataproc – Big Data Processing with Apache Spark & Hadoop: Google Cloud Dataproc simplifies big data processing using Apache Spark, Hadoop, and Presto. You will learn how to: Run large-scale data processing and ETL tasks using Spark. Optimize Dataproc clusters for cost efficiency and performance. Manage and analyze big data using Google Cloud Storage and BigQuery.
- 6. Data Governance, Security & Best Practices: Understanding data security, compliance, and governance is crucial. You will learn how to: Implement role-based access control (RBAC) and encryption to secure data. Use Google Cloud’s security features to comply with regulations like GDPR and HIPAA. Set up monitoring and logging using Cloud Logging and Cloud Monitoring.
- 7. Workflow Automation with Cloud Composer (Apache Airflow): Orchestrate end-to-end data workflows using Cloud Composer (Google’s managed Apache Airflow service). You will learn how to: Automate ETL workflows across different services. Schedule and monitor data pipeline executions. Optimize workflows for scalability and reliability.
- 8.Hands-On Projects and Real-World Applications: Practical labs and projects to build end-to-end data pipelines. Real-world scenarios to apply your GCP data engineering skills.
Course Curriculum
Introduction to Data Engineering
Building a Data Lake
Building a Data Warehouse
Introduction to Building Batch Data Pipelines
Executing Spark on Cloud Dataproc
Serverless Data Processing with Cloud Dataflow
Manage Data Pipelines with Cloud Data Fusion and Cloud Composer
Introduction to Processing Streaming Data
Serverless Messaging with Cloud Pub/Sub
Cloud Dataflow Streaming Features
High-Throughput BigQuery and Bigtable Streaming Features
Advanced BigQuery Functionality and Performance
BigQuery: Advanced Features and Use Cases
Databricks
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