Big Data Engineering Bootcamp with GCP, and Azure Cloud from Udemy

Course Description

In today’s data-driven world, organizations are dealing with massive amounts of data generated every second. Big Data technologies have become essential for efficiently processing, storing, and analyzing this data to drive business insights. Whether you are a beginner, fresher, or an experienced professional looking to transition into Big Data Engineering, this course is designed to take you from zero to expert level with real-world, end-to-end projects.

This comprehensive Big Data Bootcamp will help you master the most in-demand technologies like Hadoop, Apache Spark, Kafka, Flink, and cloud platforms like AWS, Azure, and GCP. You will learn how to build scalable data pipelines, perform batch and real-time data processing, and work with distributed computing frameworks.

We will start from the basics, explaining the fundamental concepts of Big Data and its ecosystem, and gradually move toward advanced topics, ensuring you gain practical experience through hands-on projects.

What You Will Learn?

Big Data Foundations – Understand the 3Vs (Volume, Velocity, Variety) and how Big Data technologies solve real-world problems.
Data Engineering & Pipelines – Learn how to design ETL workflows, ingest data from multiple sources, transform it, and store it efficiently.
Big Data Processing – Gain expertise in batch processing with Apache Spark and real-time streaming with Kafka and Flink.
Cloud-Based Big Data Solutions – Deploy and manage Big Data solutions on Azure, and GCP using services
End-to-End Projects – Work on industry-relevant projects, implementing scalable architectures, data pipelines, and analytics.
Performance Optimization – Understand best practices for optimizing Big Data workflows for efficiency and scalability.

Who is This Course For?

Beginners & Freshers – No prior experience needed. Start your journey in Big Data Engineering from scratch.
Software Developers – Expand your skills into Big Data technologies like Hadoop, Spark, and Kafka.
Data Analysts & Scientists – Work with large datasets, ETL pipelines, and real-time processing.
Cloud & DevOps Engineers – Learn how to deploy and manage Big Data applications in cloud environments.
IT Professionals – Transition into Big Data Engineering with hands-on experience and industry-relevant projects.

Prerequisites

Basic Computer Knowledge – No prior Big Data experience required.
Python or SQL (Optional) – Helps but is not mandatory.
Laptop with 8GB RAM & Internet Access – To run Big Data tools locally or on the cloud.

By the end of this course, you will be job-ready, equipped with practical skills, and confident in working with Big Data technologies used by top companies worldwide.

Enroll now and take your career to the next level with Big Data.

What's inside

Learning objectives

Learn hadoop, spark, and kafka from scratch, understanding the 3vs (volume, velocity, variety) and their real-world applications.
Master etl workflows, data ingestion, transformation, and storage using apache spark, airflow,kafka, and distributed systems.

Deploy & manage big data solutions on azure, and gcp
Work on real-world big data projects, implementing scalable architectures, data pipelines, and analytics using industry tools.

Learn hadoop, spark, and kafka from scratch, understanding the 3vs (volume, velocity, variety) and their real-world applications.
Master etl workflows, data ingestion, transformation, and storage using apache spark, airflow,kafka, and distributed systems.
Deploy & manage big data solutions on azure, and gcp
Work on real-world big data projects, implementing scalable architectures, data pipelines, and analytics using industry tools.

Syllabus

Introduction

Course Overview, what does a Big Data Engineer do & the roadmap

How to Complete Course and Udemy Platform Overview

Python Fundamentals

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Starts with the fundamentals of Big Data, explaining the 3Vs and how Big Data technologies address real-world challenges, which is helpful for those with no prior experience

Expands skills into Big Data technologies like Hadoop, Spark, and Kafka, which are essential tools for modern data engineering and distributed systems

Provides hands-on experience with industry-relevant projects, enabling a transition into Big Data Engineering, which is a growing field with high demand

Enables working with large datasets, ETL pipelines, and real-time processing, which are crucial for deriving insights and building data-driven applications

Focuses on deploying and managing Big Data applications in cloud environments like Azure and GCP, which are widely adopted platforms in the industry

Requires a laptop with 8GB RAM, which may pose a barrier for some learners who do not have access to such resources, limiting accessibility

Reviews summary

Comprehensive big data engineering bootcamp

According to learners, this course offers a comprehensive and hands-on introduction to Big Data Engineering. Students particularly praise the practical projects and step-by-step explanations, which many found helped solidify their understanding. The course covers key technologies like Spark, Kafka, and Hadoop, along with deployment on GCP and Azure, providing a solid foundation for the field. While a few felt certain sections could be more in-depth or updated, the overall consensus is that it's a highly valuable and relevant course for those looking to enter or advance in data engineering roles.

Builds a strong foundational understanding of BDE.

"This course provided me with a solid foundation in Big Data Engineering."

"I gained a strong grasp of the core principles of distributed systems."

"It's a great starting point for anyone new to the field."

Concepts and steps are explained clearly for learners.

"The explanations are very clear and easy to follow, even for complex topics."

"I found the step-by-step approach helpful in understanding the material."

"The instructor breaks down complicated subjects into manageable parts."

Highly relevant for job seekers and career transitions.

"I feel much more prepared for data engineering roles after this course."

"The skills learned are directly applicable to industry requirements."

"This bootcamp is practical and aligned with current job market needs."

Covers a wide range of essential Big Data technologies.

"The course covers major big data technologies like Spark, Kafka, and Hadoop."

"It provided a good overview of GCP and Azure services relevant to big data."

"I got a broad introduction to the Big Data ecosystem and its components."

Provides valuable practical experience through projects.

"The hands-on coding and projects are the strongest part of the course for me."

"The projects helped me apply the concepts learned and feel more confident."

"I especially appreciated the practical, real-world examples and projects provided."

Some advanced topics could benefit from more depth.

"I wish there were more advanced examples for Spark."

"Could use more in-depth coverage on optimization techniques."

"Some technologies were covered but not in enough detail for expertise."

Some sections may need updating due to tech evolution.

"Could use some updates to reflect the latest versions of tools."

"A few parts felt slightly outdated based on current industry practices."

"I hope the course is regularly updated to keep up with changes."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Big Data Engineering Bootcamp with GCP, and Azure Cloud with these activities:

Review Python Fundamentals

Show steps

Solidify your understanding of Python fundamentals to better grasp the data manipulation and scripting aspects of Big Data engineering.

Browse courses on Python Basics

Show steps

Review basic syntax, data types, and control flow in Python.
Practice writing simple Python scripts to manipulate data.
Complete online Python tutorials or exercises.

Brush Up on SQL

Show steps

Strengthen your SQL skills to effectively query and manipulate data within Big Data environments.

Browse courses on SQL

Show steps

Review SQL syntax for querying, inserting, updating, and deleting data.
Practice writing SQL queries on sample datasets.
Explore advanced SQL concepts like joins and subqueries.

Review 'Hadoop: The Definitive Guide'

Show steps

Gain a comprehensive understanding of Hadoop architecture and its components by studying a definitive guide.

View Hadoop: The Definitive Guide: Storage and... on Amazon

Show steps

Read chapters related to HDFS, MapReduce, and YARN.
Take notes on key concepts and architecture diagrams.
Attempt the exercises at the end of each chapter.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Follow Spark Tutorials on Databricks

Show steps

Enhance your Spark skills by working through practical tutorials on the Databricks platform.

Show steps

Sign up for a Databricks Community Edition account.
Work through tutorials on Spark DataFrames and Spark SQL.
Experiment with different data transformations and aggregations.

Build a Simple Data Pipeline with Kafka and Spark Streaming

Show steps

Solidify your understanding of real-time data processing by building a data pipeline using Kafka and Spark Streaming.

Show steps

Set up a Kafka cluster and a Spark Streaming application.
Configure Kafka to ingest data from a sample source.
Use Spark Streaming to process and analyze the data in real-time.
Visualize the results using a dashboard.

Create a Data Visualization Dashboard

Show steps

Practice presenting insights from Big Data by creating an interactive dashboard using tools like Tableau or Power BI.

Show steps

Choose a dataset related to Big Data (e.g., public datasets on Kaggle).
Clean and transform the data using Python or Spark.
Design and build an interactive dashboard to visualize key metrics.
Present your dashboard to peers or colleagues.

Contribute to an Open Source Big Data Project

Show steps

Deepen your understanding of Big Data technologies by contributing to an open-source project.

Show steps

Identify an open-source Big Data project on GitHub (e.g., Apache Spark, Apache Kafka).
Review the project's documentation and contribution guidelines.
Identify a bug or feature to work on.
Submit a pull request with your changes.

Career center

Learners who complete Big Data Engineering Bootcamp with GCP, and Azure Cloud will develop knowledge and skills that may be useful to these careers:

Data Engineer

A data engineer designs, builds, and manages the infrastructure that allows organizations to use big data effectively. This career involves developing data pipelines, ensuring data quality, and optimizing data storage and retrieval. This Big Data Engineering Bootcamp with GCP and Azure Cloud directly aligns with the core responsibilities of a data engineer. The course covers essential technologies like Hadoop, Apache Spark, Kafka, and Flink, which are critical tools for any data engineer to build scalable and robust data solutions. The practical, hands-on projects provide invaluable experience in implementing end-to-end big data architectures and optimizing performance, preparing you for real-world challenges a data engineer encounters daily.

See salaries and explore the career path for Data Engineer

Analytics Engineer

Analytics engineers are responsible for transforming raw data into usable datasets for analysis. They build and maintain data models, develop ETL pipelines, and ensure data quality. The Big Data Engineering Bootcamp with GCP and Azure Cloud directly prepares individuals for this role. The course teaches how to design ETL workflows, ingest data from multiple sources, transform it, and store it efficiently. The focus on performance optimization and scalability also aligns with the analytics engineer's responsibilities of ensuring efficient and reliable data delivery for analytics purposes.

See salaries and explore the career path for Analytics Engineer

Data Pipeline Developer

A data pipeline developer specializes in building and maintaining the data pipelines that move and transform data from various sources to destinations. This career requires expertise in ETL processes, data integration, and workflow automation. The Big Data Engineering Bootcamp with GCP and Azure Cloud directly supports the skills needed. The course specifically focuses on designing ETL workflows, ingesting data from multiple sources, transforming it, and storing it efficiently. Additionally, the hands-on projects give extensive experience in implementing scalable architectures and building robust data pipelines, making this bootcamp perfect for aspiring data pipeline developers.

See salaries and explore the career path for Data Pipeline Developer

ETL Developer

An ETL developer builds and manages the Extract, Transform, Load processes that move data from source systems into data warehouses or other data storage solutions. ETL developers need to be proficient in data integration, data cleansing, and data transformation techniques. The Big Data Engineering Bootcamp with GCP and Azure Cloud directly addresses the key skills needed for this role. The course teaches how to design ETL workflows, ingest data from various sources, transform it, and store it efficiently. The hands-on projects in implementing scalable architectures and data pipelines provide practical experience, which is invaluable for any aspiring ETL developer.

See salaries and explore the career path for ETL Developer

Cloud Engineer

Cloud engineers are responsible for designing, implementing, and managing cloud computing solutions. They work with platforms like Azure, and GCP to deploy and maintain applications and services. This Big Data Engineering Bootcamp with GCP and Azure Cloud directly addresses the need for cloud expertise in big data environments. A cloud engineer learns how to deploy and manage big data solutions on these cloud platforms, gaining practical experience with cloud services. The course's focus on scalable architectures and data pipelines helps a cloud engineer understand how to optimize cloud resources for big data processing and storage. The cloud engineer's knowledge will allow for the utilization of industry-relevant projects, and implementing scalable architectures, data pipelines, and analytics.

See salaries and explore the career path for Cloud Engineer

Big Data Architect

A big data architect designs the overall framework for managing and processing large volumes of data within an organization. This involves selecting appropriate technologies, designing data storage solutions, and ensuring scalability and performance. The Big Data Engineering Bootcamp with GCP and Azure Cloud strongly supports the skills needed for this role. The course covers Hadoop, Apache Spark, Kafka, and Flink. These tools are essential for building robust and scalable big data systems. The course emphasizes designing ETL workflows and implementing scalable architectures, which are critical for a big data architect. This also supports the need for data pipelines and analytics using industry tools.

See salaries and explore the career path for Big Data Architect

Data Warehouse Architect

A data warehouse architect designs and oversees the implementation of data warehousing solutions for an organization. Data warehouse architects must be able to model data, design ETL processes, and optimize data storage for efficient reporting and analysis. The Big Data Engineering Bootcamp with GCP and Azure Cloud helps build a foundation for this role. The course covers ETL workflows, data ingestion, and data transformation, all critical aspects of data warehousing. The course's focus on cloud-based big data solutions using Azure and GCP also aligns with the modern trends in data warehousing, where cloud platforms are increasingly used to host data warehouses.

See salaries and explore the career path for Data Warehouse Architect

DevOps Engineer

DevOps engineers automate and streamline the software development and deployment process. They work with various tools and technologies to ensure continuous integration and continuous delivery (CI/CD). The Big Data Engineering Bootcamp with GCP and Azure Cloud helps DevOps engineers deploy and manage big data applications in cloud environments. The course covers cloud-based big data solutions on Azure and GCP, teaching DevOps engineers how to provision and manage big data infrastructure in the cloud. DevOps engineers can also learn how to automate the deployment of data pipelines and big data processing workflows.

See salaries and explore the career path for DevOps Engineer

Solutions Architect

A solutions architect designs and implements comprehensive technology solutions that meet business needs. Solutions architects require a broad understanding of various technologies, including data processing, cloud computing, and system integration. The Big Data Engineering Bootcamp with GCP and Azure Cloud may be useful as it provides exposure to various big data technologies and cloud platforms. The course covers Hadoop, Apache Spark, Kafka, and Flink, as well as cloud services on Azure and GCP. The hands-on projects in implementing scalable architectures and data pipelines provide practical experience in designing end-to-end solutions.

See salaries and explore the career path for Solutions Architect

Machine Learning Engineer

Machine learning engineers develop and deploy machine learning models for various applications. They require expertise in data processing, feature engineering, model training, and deployment. The Big Data Engineering Bootcamp with GCP and Azure Cloud provides essential skills for machine learning engineers, particularly in the area of data processing. The course's focus on big data technologies like Hadoop, Apache Spark, and Kafka helps machine learning engineers manage and process the large datasets required for training machine learning models. The course would also expose machine learning engineers to ETL workflows, data ingestion, transformation, and storage, which are critical steps in preparing data for machine learning.

See salaries and explore the career path for Machine Learning Engineer

Data Science Manager

A data science manager leads a team of data scientists, providing guidance, mentorship, and strategic direction. While this role is managerial, a strong understanding of data engineering is crucial for managing data science projects effectively. The Big Data Engineering Bootcamp with GCP and Azure Cloud helps data science managers understand the underlying infrastructure and processes involved in big data processing. The course's coverage of ETL workflows, data pipelines, and cloud-based big data solutions provides valuable insights for managing data science teams and ensuring that data science projects are built on a solid foundation.

See salaries and explore the career path for Data Science Manager

Data Analyst

Data analysts examine data to identify trends, answer questions, and make recommendations to organizations. Although this position is not focused on building models, data analysis requires data extraction, data cleaning, and data preparation. The Big Data Engineering Bootcamp with GCP and Azure Cloud helps with these aspects of analysis due to its coverage of data pipelines. Data analysts can learn to work with large datasets and ETL pipelines, which are becoming increasingly important for data analysts as data volumes grow. The course will expose data analysts to industry tools.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

A business intelligence analyst analyzes data to identify trends and provide actionable insights to improve the company’s decision-making. This role is very similar to data analyst, therefore, the Big Data Engineering Bootcamp with GCP and Azure Cloud may be useful as it helps support with these aspects of analysis due to its coverage of data pipelines. The course will expose analysts to industry tools, and the need to work with large datasets and ETL pipelines, which is becoming increasingly important for business intelligence as data volumes grow.

See salaries and explore the career path for Business Intelligence Analyst

Database Administrator

A database administrator manages and maintains databases, ensuring data integrity, performance, and security. While this career traditionally focuses on relational databases, modern database administrators often work with big data technologies. The Big Data Engineering Bootcamp with GCP and Azure Cloud may be useful as it provides exposure to distributed data storage and processing. Database administrators can learn how to manage and optimize big data clusters using Hadoop, Apache Spark, and cloud-based solutions on Azure and GCP. This expands their skill set to handle the growing volume and variety of data in modern organizations.

See salaries and explore the career path for Database Administrator

Software Engineer

Software engineers design, develop, and test software applications. This career may not directly involve big data, but understanding big data technologies can be valuable for building scalable and data-intensive applications. The Big Data Engineering Bootcamp with GCP and Azure Cloud may be useful as it expands a software engineer's skill set into big data technologies like Hadoop, Spark, and Kafka. Software engineers can learn how to integrate their applications with big data systems and how to process large volumes of data efficiently. This course would also support the need for software developers to work with large datasets, ETL pipelines, and real-time processing.

See salaries and explore the career path for Software Engineer

Big Data Engineering Bootcamp with GCP, and Azure Cloud

What's inside

Learning objectives

Syllabus

Traffic lights

Save this course

Reviews summary

Comprehensive big data engineering bootcamp

Activities

Career center

Reading list

Share

Similar courses