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Data Science, AI & Machine Learning Information & Emerging Technologies

Course Features

Price

Original price was: £490.00.Current price is: £14.99.

Study Method

Online | Self-paced

Course Format

Interactive PDFs, Articles & Learning Resources

Duration

1 day, 2 hours

Qualification

Professional Skills Development Course

Assessment

Final MCQ Exam (included in price)

Certificate

Verifiable Digital Certificate - Free

Additional info

Lifetime Access | Start Instantly

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GCP Data Engineering Course showing Google Cloud pipelines, data lakes, Dataproc and Databricks workflows

Overview

GCP Data Engineering Course provides a comprehensive learning experience for individuals who want to develop professional skills in designing, building and managing modern cloud-based data solutions using Google Cloud Platform. This course is designed to help learners understand how data engineers collect, process, store and transform large-scale data while working with industry-relevant cloud technologies.

The course begins by introducing the fundamentals of data engineering on GCP, including cloud concepts, development environment setup and essential tools used by modern data professionals. Learners explore how to configure their workspace, work with Python environments and prepare the foundation required for building scalable data workflows.

Students then gain practical experience with Google Cloud services, including Google Cloud Storage, Cloud SQL, Dataproc and Databricks. They learn how to design data lakes, manage cloud storage resources, upload and process datasets, handle different file formats and work with structured data using Python and analytics libraries.

A major focus of the course is building reliable data pipelines. Learners discover how to create ELT workflows, process large datasets with Spark technologies and automate data transformation tasks. Through practical exercises, they develop experience with Spark SQL, PySpark, workflow management, job execution and pipeline validation in cloud environments.

The course also covers database integration and secure cloud practices. Learners explore PostgreSQL databases on Cloud SQL, data processing with Pandas, secure credential management using Secret Manager and methods for connecting applications with cloud services safely. These skills help students understand how real-world data engineering systems are designed and maintained.

By completing this course, learners can develop practical knowledge required for modern cloud data engineering roles. The skills gained can support career growth in data engineering, cloud computing, analytics engineering and big data processing. The hands-on approach helps learners build confidence in working with scalable data platforms and professional cloud workflows.

Upon successful completion, learners will receive a free course completion certificate. Multiple premium certificate and transcript options are also available for purchase if they wish to obtain additional documentation. Students also have access to 5-star-rated support available 24/7 through email, ensuring guidance throughout their learning journey.
This course is suitable for aspiring data engineers, software developers, cloud professionals, analysts and technology graduates who want to build expertise in Google Cloud data solutions. It is also valuable for professionals looking to transition into cloud-based data roles or expand their knowledge of modern big data platforms.
Learners should have basic programming knowledge, preferably with Python, along with general understanding of databases and data concepts. Familiarity with command-line tools and cloud computing concepts can be helpful, but the course introduces essential topics progressively for motivated learners.
After completing this course, learners can pursue opportunities as Data Engineers, Cloud Data Engineers, Analytics Engineers or Big Data Developers. The knowledge gained can also support preparation for cloud certifications, advanced data studies and professional projects involving scalable data platforms and analytics systems.

Who is this course for?

GCP Data Engineering Course provides a comprehensive learning experience for individuals who want to develop professional skills in designing, building and managing modern cloud-based data solutions using Google Cloud Platform. This course is designed to help learners understand how data engineers collect, process, store and transform large-scale data while working with industry-relevant cloud technologies.

The course begins by introducing the fundamentals of data engineering on GCP, including cloud concepts, development environment setup and essential tools used by modern data professionals. Learners explore how to configure their workspace, work with Python environments and prepare the foundation required for building scalable data workflows.

Students then gain practical experience with Google Cloud services, including Google Cloud Storage, Cloud SQL, Dataproc and Databricks. They learn how to design data lakes, manage cloud storage resources, upload and process datasets, handle different file formats and work with structured data using Python and analytics libraries.

A major focus of the course is building reliable data pipelines. Learners discover how to create ELT workflows, process large datasets with Spark technologies and automate data transformation tasks. Through practical exercises, they develop experience with Spark SQL, PySpark, workflow management, job execution and pipeline validation in cloud environments.

The course also covers database integration and secure cloud practices. Learners explore PostgreSQL databases on Cloud SQL, data processing with Pandas, secure credential management using Secret Manager and methods for connecting applications with cloud services safely. These skills help students understand how real-world data engineering systems are designed and maintained.

By completing this course, learners can develop practical knowledge required for modern cloud data engineering roles. The skills gained can support career growth in data engineering, cloud computing, analytics engineering and big data processing. The hands-on approach helps learners build confidence in working with scalable data platforms and professional cloud workflows.

Upon successful completion, learners will receive a free course completion certificate. Multiple premium certificate and transcript options are also available for purchase if they wish to obtain additional documentation. Students also have access to 5-star-rated support available 24/7 through email, ensuring guidance throughout their learning journey.
This course is suitable for aspiring data engineers, software developers, cloud professionals, analysts and technology graduates who want to build expertise in Google Cloud data solutions. It is also valuable for professionals looking to transition into cloud-based data roles or expand their knowledge of modern big data platforms.
Learners should have basic programming knowledge, preferably with Python, along with general understanding of databases and data concepts. Familiarity with command-line tools and cloud computing concepts can be helpful, but the course introduces essential topics progressively for motivated learners.
After completing this course, learners can pursue opportunities as Data Engineers, Cloud Data Engineers, Analytics Engineers or Big Data Developers. The knowledge gained can also support preparation for cloud certifications, advanced data studies and professional projects involving scalable data platforms and analytics systems.

Requirements

GCP Data Engineering Course provides a comprehensive learning experience for individuals who want to develop professional skills in designing, building and managing modern cloud-based data solutions using Google Cloud Platform. This course is designed to help learners understand how data engineers collect, process, store and transform large-scale data while working with industry-relevant cloud technologies.

The course begins by introducing the fundamentals of data engineering on GCP, including cloud concepts, development environment setup and essential tools used by modern data professionals. Learners explore how to configure their workspace, work with Python environments and prepare the foundation required for building scalable data workflows.

Students then gain practical experience with Google Cloud services, including Google Cloud Storage, Cloud SQL, Dataproc and Databricks. They learn how to design data lakes, manage cloud storage resources, upload and process datasets, handle different file formats and work with structured data using Python and analytics libraries.

A major focus of the course is building reliable data pipelines. Learners discover how to create ELT workflows, process large datasets with Spark technologies and automate data transformation tasks. Through practical exercises, they develop experience with Spark SQL, PySpark, workflow management, job execution and pipeline validation in cloud environments.

The course also covers database integration and secure cloud practices. Learners explore PostgreSQL databases on Cloud SQL, data processing with Pandas, secure credential management using Secret Manager and methods for connecting applications with cloud services safely. These skills help students understand how real-world data engineering systems are designed and maintained.

By completing this course, learners can develop practical knowledge required for modern cloud data engineering roles. The skills gained can support career growth in data engineering, cloud computing, analytics engineering and big data processing. The hands-on approach helps learners build confidence in working with scalable data platforms and professional cloud workflows.

Upon successful completion, learners will receive a free course completion certificate. Multiple premium certificate and transcript options are also available for purchase if they wish to obtain additional documentation. Students also have access to 5-star-rated support available 24/7 through email, ensuring guidance throughout their learning journey.
This course is suitable for aspiring data engineers, software developers, cloud professionals, analysts and technology graduates who want to build expertise in Google Cloud data solutions. It is also valuable for professionals looking to transition into cloud-based data roles or expand their knowledge of modern big data platforms.
Learners should have basic programming knowledge, preferably with Python, along with general understanding of databases and data concepts. Familiarity with command-line tools and cloud computing concepts can be helpful, but the course introduces essential topics progressively for motivated learners.
After completing this course, learners can pursue opportunities as Data Engineers, Cloud Data Engineers, Analytics Engineers or Big Data Developers. The knowledge gained can also support preparation for cloud certifications, advanced data studies and professional projects involving scalable data platforms and analytics systems.

Career path

GCP Data Engineering Course provides a comprehensive learning experience for individuals who want to develop professional skills in designing, building and managing modern cloud-based data solutions using Google Cloud Platform. This course is designed to help learners understand how data engineers collect, process, store and transform large-scale data while working with industry-relevant cloud technologies.

The course begins by introducing the fundamentals of data engineering on GCP, including cloud concepts, development environment setup and essential tools used by modern data professionals. Learners explore how to configure their workspace, work with Python environments and prepare the foundation required for building scalable data workflows.

Students then gain practical experience with Google Cloud services, including Google Cloud Storage, Cloud SQL, Dataproc and Databricks. They learn how to design data lakes, manage cloud storage resources, upload and process datasets, handle different file formats and work with structured data using Python and analytics libraries.

A major focus of the course is building reliable data pipelines. Learners discover how to create ELT workflows, process large datasets with Spark technologies and automate data transformation tasks. Through practical exercises, they develop experience with Spark SQL, PySpark, workflow management, job execution and pipeline validation in cloud environments.

The course also covers database integration and secure cloud practices. Learners explore PostgreSQL databases on Cloud SQL, data processing with Pandas, secure credential management using Secret Manager and methods for connecting applications with cloud services safely. These skills help students understand how real-world data engineering systems are designed and maintained.

By completing this course, learners can develop practical knowledge required for modern cloud data engineering roles. The skills gained can support career growth in data engineering, cloud computing, analytics engineering and big data processing. The hands-on approach helps learners build confidence in working with scalable data platforms and professional cloud workflows.

Upon successful completion, learners will receive a free course completion certificate. Multiple premium certificate and transcript options are also available for purchase if they wish to obtain additional documentation. Students also have access to 5-star-rated support available 24/7 through email, ensuring guidance throughout their learning journey.
This course is suitable for aspiring data engineers, software developers, cloud professionals, analysts and technology graduates who want to build expertise in Google Cloud data solutions. It is also valuable for professionals looking to transition into cloud-based data roles or expand their knowledge of modern big data platforms.
Learners should have basic programming knowledge, preferably with Python, along with general understanding of databases and data concepts. Familiarity with command-line tools and cloud computing concepts can be helpful, but the course introduces essential topics progressively for motivated learners.
After completing this course, learners can pursue opportunities as Data Engineers, Cloud Data Engineers, Analytics Engineers or Big Data Developers. The knowledge gained can also support preparation for cloud certifications, advanced data studies and professional projects involving scalable data platforms and analytics systems.

    • What is Data Engineering on Google Cloud Platform? 00:10:00
    • Prerequisites for Learning GCP Data Engineering 00:10:00
    • Key Highlights of This Data Engineering Course 00:10:00
    • How to Use the Udemy Platform Effectively 00:10:00
    • Course Policies: Refunds & Feedback 00:10:00
    • Overview of GCP Data Engineering Folder Structure 00:10:00
    • Configuring VS Code Workspace for Data Engineering 00:10:00
    • Setting Up Python 3.9 Virtual Environment with VS Code 00:10:00
    • Introduction to GCP for Data Engineers 00:10:00
    • Essential Skills for GCP Account Setup 00:10:00
    • Overview of Major Cloud Platforms 00:10:00
    • Deep Dive: What is Google Cloud Platform (GCP)? 00:10:00
    • Step-by-Step Guide to Creating a GCP Account 00:10:00
    • Creating a New Google Account (if needed) 00:10:00
    • How to Sign Up for GCP Using Your Google Account 00:10:00
    • Understanding GCP Credits and Free Tier 00:10:00
    • Creating and Managing GCP Projects 00:10:00
    • Introduction to Google Cloud Shell 00:10:00
    • Installing Google Cloud SDK Locally 00:10:00
    • Initializing gcloud CLI with Your GCP Project 00:10:00
    • Reinitializing Cloud Shell for Updates 00:10:00
    • Overview of GCP Analytics Services 00:10:00
    • Summary: Getting Started with GCP 00:10:00
    • Introduction to Google Cloud Storage (GCS) 00:10:00
    • Navigating the GCS Web User Interface 00:10:00
    • Uploading Files and Folders to GCS Buckets 00:10:00
    • Managing GCS Buckets & Objects with gsutil Commands 00:10:00
    • Deleting Buckets Using GCS Web UI 00:10:00
    • Setting Up Data Repositories with Google Cloud Shell 00:10:00
    • Understanding Datasets in GCS 00:10:00
    • Advanced Bucket Management with gsutil 00:10:00
    • Copying Datasets into GCS Using gsutil 00:10:00
    • Cleaning Up Buckets and Objects in GCS 00:10:00
    • Hands-On Exercise: Managing Buckets and Files 00:10:00
    • Designing a Data Lake Architecture on GCS 00:10:00
    • Installing and Using Google Cloud Libraries in Python 00:10:00
    • Creating Buckets and Uploading Files via Python & gsutil 00:10:00
    • Managing Files in GCS with Python Libraries 00:10:00
    • Review of Key Google Cloud Storage Python Methods 00:10:00
    • Retrieving Bucket Details Using Python 00:10:00
    • Handling Blobs (Files) in GCS with Python 00:10:00
    • Project: Problem Statement for File Management in GCS 00:10:00
    • Designing Multi-file Upload Systems Using Python 00:10:00
    • Getting File Lists for Uploading to GCS 00:10:00
    • Uploading Files as Blobs to GCS Using Python 00:10:00
    • Validating Uploaded Files in GCS Programmatically 00:10:00
    • Data Processing in GCS Using Pandas Library 00:10:00
    • Converting Data to Parquet Format and Writing to GCS 00:10:00
    • Uploading Multiple Files to GCS Using Pandas 00:10:00
    • File Listing and Management Using Python & Pandas 00:10:00
    • Introduction to Parquet File Format & JSON Schema Files 00:10:00
    • Extracting Column Names from JSON Schema Files 00:10:00
    • Uploading Parquet Files to GCS Using Pandas 00:10:00
    • Validating File Integrity Post-Upload Using Pandas 00:10:00
    • Introduction to GCP Cloud SQL Service 00:10:00
    • Provisioning a Postgres Database Server on GCP Cloud SQL 00:10:00
    • Configuring Network Access and Security for Cloud SQL 00:10:00
    • Installing and Validating Postgres Client Tools on Mac/PC 00:10:00
    • Creating Databases in Cloud SQL Postgres 00:10:00
    • Setting Up Tables and Schemas in Cloud SQL 00:10:00
    • Validating Database Tables and Data 00:10:00
    • Integrating Cloud SQL Postgres with Python Applications 00:10:00
    • Using Pandas to Read and Write Data with Cloud SQL 00:10:00
    • Loading Files into Pandas DataFrames for Processing 00:10:00
    • Data Transformation with Pandas APIs 00:10:00
    • Writing Processed Data from Pandas to Postgres Tables 00:10:00
    • Verifying Database Updates with Pandas Queries 00:10:00
    • Introduction to GCP Secret Manager for Secure Credentials 00:10:00
    • Assigning IAM Roles for Secret Manager Access 00:10:00
    • Installing Secret Manager Python Client Library 00:10:00
    • Fetching Secrets Programmatically Using Python 00:10:00
    • Secure Database Connections Using Secrets from Secret Manager 00:10:00
    • Properly Stopping Cloud SQL Postgres Instances 00:10:00
    • Introduction to Google Dataproc for Big Data 00:10:00
    • Deploying Single-Node Dataproc Clusters for Development 00:10:00
    • Verifying SSH Connectivity to Dataproc Master Node 00:10:00
    • Assigning Static IP Addresses to Dataproc VM Instances 00:10:00
    • Configuring VS Code Remote Development with Dataproc 00:10:00
    • Setting Up Development Workspace on Dataproc 00:10:00
    • Using Hadoop HDFS Commands on Dataproc Clusters 00:10:00
    • Revisiting gsutil for GCS File Management 00:10:00
    • Preparing Datasets on Dataproc Master Node 00:10:00
    • Copying Local Files into HDFS Storage 00:10:00
    • Transferring Data from GCS to HDFS 00:10:00
    • Validating PySpark CLI on Dataproc Clusters 00:10:00
    • Testing Spark Scala CLI on Dataproc 00:10:00
    • Running Spark SQL CLI in Dataproc Environment 00:10:00
    • Introduction to Dataproc Jobs and Workflow Management 00:10:00
    • Setting Up JSON Datasets in GCS for Pipeline Jobs 00:10:00
    • Writing and Reviewing Spark SQL Commands for Dataproc 00:10:00
    • Executing Dataproc Jobs via Spark SQL Queries 00:10:00
    • Modularizing Spark SQL Scripts for Scalability 00:10:00
    • Reviewing Spark SQL Script Files for Pipelines 00:10:00
    • Validating Spark SQL Script for File Format Conversion 00:10:00
    • Practical Exercise: File Format Conversion Using Spark SQL 00:10:00
    • Testing Spark SQL Script for Daily Revenue Calculations 00:10:00
    • Creating Spark SQL Scripts for Database Cleanup 00:10:00
    • Uploading Spark SQL Scripts to GCS 00:10:00
    • Running and Validating Spark SQL Scripts from GCS 00:10:00
    • Limitations of Running Spark SQL via Dataproc Jobs 00:10:00
    • Managing Dataproc Clusters with gcloud CLI Commands 00:10:00
    • Running Dataproc Jobs Using Spark SQL Commands 00:10:00
    • Executing Dataproc Jobs with Spark SQL Scripts 00:10:00
    • Practice Exercises: Running Spark SQL Scripts on Dataproc 00:10:00
    • Deleting Dataproc Jobs Using gcloud CLI 00:10:00
    • Best Practices for Managing Dataproc Jobs with CLI 00:10:00
    • Introduction to Dataproc Workflow Templates via UI 00:10:00
    • Designing and Reviewing Dataproc Workflow Templates 00:10:00
    • Creating Dataproc Workflow Templates with Cluster Definitions 00:10:00
    • Adding Jobs to Workflow Templates Using gcloud Commands 00:10:00
    • Adding Jobs to Workflow Templates via CLI Automation 00:10:00
    • Instantiating and Running Dataproc Workflow Templates 00:10:00
    • Monitoring Dataproc Operations and Cleanup 00:10:00
    • Executing and Validating ELT Data Pipelines on Dataproc 00:10:00
    • Shutting Down Dataproc Clusters to Save Costs 00:10:00
    • Signing Up and Setting Up Databricks on GCP 00:10:00
    • Creating Databricks Workspaces 00:10:00
    • Introduction to Databricks Clusters on GCP 00:10:00
    • Getting Started with Databricks Notebooks 00:10:00
    • Overview of Databricks Platform Features 00:10:00
    • Using Databricks CLI Commands 00:10:00
    • Limitations and Workarounds Managing DBFS with CLI 00:10:00
    • Copying Datasets into DBFS from GCS 00:10:00
    • Creating Folders in GCS via DBFS Commands 00:10:00
    • Uploading Data into DBFS Using GCS Web UI 00:10:00
    • Using gsutil to Copy Data to DBFS 00:10:00
    • Processing Data in DBFS with Spark SQL 00:10:00
    • Spark SQL Basics in Databricks Notebooks 00:10:00
    • Creating Temporary Views with Spark SQL 00:10:00
    • Exercises: Building Temporary Views 00:10:00
    • Running Spark SQL Queries for Product Revenue 00:10:00
    • Saving Spark SQL Query Results in DBFS 00:10:00
    • Introduction to PySpark Examples in Databricks 00:10:00
    • Handling JSON Schema in PySpark DataFrames 00:10:00
    • Creating DataFrames from JSON Schemas 00:10:00
    • Data Transformation Using Spark APIs 00:10:00
    • Retrieving Schema Details Across Datasets 00:10:00
    • Converting CSV to Parquet with Schema in PySpark 00:10:00
    • Overview of Databricks Workflow Management 00:10:00
    • Passing Arguments to Python Notebooks in Databricks 00:10:00
    • Passing Arguments to SQL Notebooks in Databricks 00:10:00
    • Creating and Running Your First Databricks Job 00:10:00
    • Executing Jobs with Task Parameters 00:10:00
    • Building Orchestrated Pipelines with Databricks Jobs 00:10:00
    • Importing ELT Pipeline Applications to Databricks 00:10:00
    • Cleaning Up Databases and Datasets with Spark SQL 00:10:00
    • Reviewing File Format Conversion Scripts 00:10:00
    • Reviewing SQL Notebooks for Table Creation and Results 00:10:00
    • Validating ELT Pipeline Applications 00:10:00
    • Build ELT Pipeline using Databricks Job in Workflows 00:10:00
    • Building and Running ELT Pipelines in Databricks Workflows 00:10:00
    • Exam of Master Data Engineering & Analytics on Google Cloud Platform (GCP) 00:50:00
    • Premium Certificate 00:15:00
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Yes, our premium certificate and transcript are widely recognized and accepted by embassies worldwide, particularly by the UK embassy. This adds credibility to your qualification and enhances its value for professional and academic purposes.

Yes, this course is designed for learners of all levels, including beginners. The content is structured to provide step-by-step guidance, ensuring that even those with no prior experience can follow along and gain valuable knowledge.

Yes, professionals will also benefit from this course. It covers advanced concepts, practical applications, and industry insights that can help enhance existing skills and knowledge. Whether you are looking to refine your expertise or expand your qualifications, this course provides valuable learning.

No, you have lifetime access to the course. Once enrolled, you can revisit the materials at any time as long as the course remains available. Additionally, we regularly update our content to ensure it stays relevant and up to date.

I trust you’re in good health. Your free certificate can be located in the Achievement section. The option to purchase a CPD certificate is available but entirely optional, and you may choose to skip it. Please be aware that it’s crucial to click the “Complete” button to ensure the certificate is generated, as this process is entirely automated.

Yes, the course includes both assessments and assignments. Your final marks will be determined by a combination of 20% from assignments and 80% from assessments. These evaluations are designed to test your understanding and ensure you have grasped the key concepts effectively.

We are a recognized course provider with CPD, UKRLP, and AOHT membership. The logos of these accreditation bodies will be featured on your premium certificate and transcript, ensuring credibility and professional recognition.

Yes, you will receive a free digital certificate automatically once you complete the course. If you would like a premium CPD-accredited certificate, either in digital or physical format, you can upgrade for a small fee.

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Course Features

Price

Original price was: £490.00.Current price is: £14.99.

Study Method

Online | Self-paced

Course Format

Interactive PDFs, Articles & Learning Resources

Duration

1 day, 2 hours

Qualification

Professional Skills Development Course

Assessment

Final MCQ Exam (included in price)

Certificate

Verifiable Digital Certificate - Free

Additional info

Lifetime Access | Start Instantly

  • Share

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