Practical Data Engineering in GCP: Beginner to Advanced

📚 Course Title: Practical Data Engineering in GCP: Beginner to Advanced

🚀 Headline: Step by Step Guide to Building Four Data Pipelines in Google Cloud Using DataStream, Data Fusion, DataPrep, DataFlow & More!

🔍 Course Description:

Embark on a transformative journey through the realm of data engineering with our comprehensive course on Google Cloud Platform (GCP). This course is meticulously designed to guide you from the basics to advanced levels, where you will learn to create a robust data lake using Google Cloud Storage and elevate it to a lakehouse architecture by integrating Google BigQuery.

Through hands-on experience, you'll build four no-code data pipelines using a suite of GCP services, including DataStream, Dataflow, DataPrep, Pub/Sub, and Data Fusion. Each chapter is tailored to simulate a real-world project implementation scenario, offering you an immersive learning environment that mirrors the demands of actual industry practices.

📕 Chapter 1: Project Setup & Storage Solutions

• Initialize your project in Google Cloud.
• Delve into the capabilities and features of Google Cloud Storage.
• Explore the potential of Google BigQuery for data analysis.

💧 Chapter 2 - Data Pipeline 1: Streaming with SQL and Dataflow

• Create a Cloud SQL database and populate it with your datasets.
• Set up streaming from Cloud SQL to Cloud Storage using DataStream Change Data Capture (CDC).
• Trigger a Pub/Sub notification to process data flow into Cloud Storage.
• Design and deploy a Dataflow pipeline for real-time streaming jobs feeding into BigQuery.

🔄 Chapter 3 - Data Pipeline 2: ETL with Google Data Fusion

• Introduce Google Data Fusion and its capabilities.
• Author and monitor ETL jobs within the data lake, transforming data as needed.
• Utilize Wrangler in Data Fusion for profiling your datasets.
• Cleanse, normalize, and govern your data using metadata in Data Fusion.

🐦 Chapter 4 - Data Pipeline 3: Real-time Message Processing with Pub/Sub

• Learn about Google Pub/Sub and its role in event-driven architectures.
• Build a .NET application to publish messages to a Pub/Sub topic.
• Create a data pipeline for streaming messages into BigQuery for real-time analytics.

📈 Chapter 5 - Data Pipeline 4: DataPrep for ETL Jobs

• Discover Cloud DataPrep, an intuitive UI for data preparation.
• Profile your datasets and author ETL jobs with Cloud DataPrep.
• Monitor and manage your ETL workflows with ease.

By the end of this course, you'll have a solid understanding of how to leverage GCP services to build scalable, efficient data pipelines that cater to a variety of use cases. Whether you're an aspiring data engineer or looking to upskill your current expertise, this course is your gateway to mastering data engineering on Google Cloud Platform.

Enroll now and take the first step towards becoming a data engineering expert! 🌟

Prerequisites: Basic understanding of cloud computing concepts and familiarity with data concepts. No prior experience with GCP is required, as the course will cover all necessary basics.

Target Audience: Aspiring Data Engineers, Data Analysts, Solution Architects, and anyone interested in learning about data engineering on Google Cloud Platform.

Skills Acquired:

• Setting up a Google Cloud project
• Working with Google Cloud Storage and BigQuery
• Implementing ETL (Extract, Transform, Load) processes using GCP services like DataStream, Dataflow, Data Fusion, and DataPrep
• Building real-time data pipelines with Pub/Sub
• Understanding the lakehouse architecture
• Utilizing no-code tools for data transformation and orchestration

Course Materials: Access to a Google Cloud Platform environment, step-by-step tutorials, video content, hands-on labs, and community support.

Join us on this data engineering adventure and unlock the full potential of your data! 🌟