GCP for Data Engineering Course

Master data engineering with Google Cloud Platform, building scalable data pipelines and analytics solutions.

Master GCP for Data Engineering

Learn to design and deploy scalable data pipelines using Google Cloud Platform services for modern data engineering.

This Course Includes

  • 30 Hours of Hands-on Training
  • Tools: BigQuery, Dataflow, Dataproc, Pub/Sub
  • Online GCP Labs
  • Learn ETL and Data Pipeline Skills
  • Real-World Data Engineering Projects
  • Serverless Data Processing with Cloud Functions

Things You'll Learn

  • Building scalable data pipelines with Dataflow
  • ETL processes with Dataflow and Dataproc
  • Data warehousing with BigQuery
  • Real-time data streaming with Pub/Sub
  • Querying and analyzing data with BigQuery

Course Content

Introduction to GCP Data Engineering
  • Overview of data engineering on Google Cloud Platform.
  • Key GCP services: BigQuery, Dataflow, Cloud Storage.
  • Hands-on exercise: Setting up a GCP data engineering environment.
  • Understanding data lakes vs. data warehouses in GCP.
  • Introduction to GCP Free Tier for data services.
  • Real-world use case: Ingesting raw data into Cloud Storage.
  • Navigating Cloud Composer and Data Fusion.
  • Basic GCP CLI commands for data tasks.
Data Storage and Ingestion
  • Using Cloud Storage for scalable data storage.
  • Hands-on lab: Creating Cloud Storage buckets for raw and processed data.
  • Ingesting data with Cloud Pub/Sub.
  • Hands-on exercise: Streaming real-time data with Pub/Sub.
  • Configuring Cloud Data Fusion for data ingestion.
  • Real-world scenario: Ingesting IoT sensor data.
  • Optimizing Cloud Storage with lifecycle rules.
  • Best practices for data partitioning and compression.
ETL with Dataflow and Dataproc
  • Building ETL pipelines with Cloud Dataflow.
  • Hands-on lab: Creating a Dataflow pipeline for ETL.
  • Using Dataproc for big data processing with Spark/Hadoop.
  • Hands-on exercise: Transforming CSV to Parquet with Dataflow.
  • Integrating Dataflow with BigQuery and Cloud Storage.
  • Real-world case study: ETL for e-commerce analytics.
  • Automating Dataflow jobs with Cloud Scheduler.
  • Debugging and optimizing Dataflow performance.
Data Warehousing and Querying
  • Setting up and managing BigQuery datasets.
  • Hands-on lab: Loading data into BigQuery.
  • Querying data with BigQuery SQL.
  • Hands-on exercise: Running SQL queries on Cloud Storage data.
  • Optimizing BigQuery performance with partitioning and clustering.
  • Real-world example: Building a BI dashboard with BigQuery.
  • Using Looker Studio for data visualization.
  • Best practices for data warehouse design in BigQuery.
Advanced Data Engineering
  • Building serverless data pipelines with Cloud Functions.
  • Hands-on lab: Triggering Cloud Functions for data processing.
  • Real-time analytics with Dataflow Streaming.
  • Hands-on exercise: Analyzing streaming data with Pub/Sub and Dataflow.
  • Orchestrating pipelines with Cloud Composer.
  • Real-world scenario: End-to-end data pipeline for marketing data.
  • Monitoring pipelines with Cloud Monitoring.
  • Preparing for Google Cloud Data Engineer certification.

Why Choose This Course?

  • Led by GCP-certified data engineers
  • Hands-on labs with real-world datasets
  • Flexible online learning format
  • Projects to showcase data engineering skills
  • Prepares you for Google Cloud Data Engineer certification
Register Here