A Big Data Hadoop and Spark project for absolute beginners from Udemy

This course will prepare you for a real world Data Engineer role .

Data Engineering is a crucial component of data-driven organizations, as it encompasses the processing, management, and analysis of large-scale data sets, which is essential for staying competitive.

This course provides an opportunity to quickly get started with Big Data through the use of a free cloud clusters, and solve a practical use case.

You will learn the fundamental concepts of Hadoop, Hive, and Spark, using both Python and Scala. The course aims to develop your Spark Scala and PySpark coding abilities to that of a professional developer, by introducing you to industry-standard coding practices such as logging, error handling and configuration management.

Additionally, you will understand the Databricks Lakehouse Platform and learn how to conduct analytics using Python and Scala with Spark, apply Spark SQL and Databricks SQL for analytics, develop a data pipeline with Apache Spark, and manage a Delta table by accessing version history, restoring data, and utilizing time travel features. You will also learn how to optimize query performance using Delta Cache, work with Delta Tables and Databricks File System, and gain insights into real-world scenarios from our experienced instructor.

What you will learn :

Big Data, Hadoop concepts
How to create a free Hadoop and Spark cluster using Google Dataproc
Hadoop hands-on - HDFS, Hive
Python basics
PySpark RDD - hands-on
PySpark SQL, DataFrame - hands-on
Project work using PySpark and Hive
Scala basics
Spark Scala DataFrame
Project work using Spark Scala
Developing a practical comprehension of Databricks Delta Lake Lakehouse concepts through hands-on experience.
Learning to operate a Delta table by accessing its version history, recovering data, and utilizing time travel functionality
Spark Scala Real world coding framework and development using Winutil, Maven and IntelliJ.
Python Spark Hadoop Hive coding framework and development using PyCharm
Building a data pipeline using Hive , PostgreSQL, Spark
Logging , error handling and unit testing of PySpark and Spark Scala applications
Spark Scala Structured Streaming
Applying spark transformation on data stored in AWS S3 using Glue and viewing data using Athena
How to become a productive data engineer leveraging ChatGPT

Prerequisites :

This course is designed for Data Engineering beginners with no prior knowledge of Python and Scala required. However, some familiarity with databases and SQL is necessary to succeed in this course. Upon completion, you will have the skills and knowledge required to succeed in a real-world Data Engineer role.

What's inside

Syllabus

Introduction

New addition - Databricks Delta Lake Lakehouse

Understand Big Data Hadoop Spark Concepts

Big Data concepts

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Uses Databricks, a popular platform in the data engineering field, for hands-on experience with Spark and Delta Lake, which are essential for modern data pipelines

Covers Hadoop, Hive, and Spark, which are foundational technologies for processing and analyzing large datasets, providing a solid base for a data engineering role

Includes real-world coding practices such as logging, error handling, and configuration management, which are crucial for developing robust and maintainable data engineering solutions

Teaches how to build a data pipeline using Hive, PostgreSQL, and Spark, which are common components in data engineering architectures for data ingestion, storage, and processing

Requires familiarity with databases and SQL, which suggests that learners without this background may need to acquire these skills separately before or during the course

Introduces using ChatGPT for faster development and Spark performance tuning, which may be useful, but learners should also develop a strong understanding of the underlying concepts

Reviews summary

Comprehensive big data for beginners

According to learners, this course offers a comprehensive introduction to Big Data technologies like Hadoop, Spark (PySpark and Scala), Hive, and Databricks Delta Lake. Many students appreciate the extensive hands-on exercises and practical projects that provide valuable real-world experience for aspiring data engineers. The inclusion of industry-standard coding practices and how to leverage tools like ChatGPT is frequently highlighted as a unique and valuable aspect. While largely seen as positive, some beginners found the sheer volume of material and the pace challenging, noting that familiarity with SQL and databases is indeed necessary despite the 'absolute beginner' title.

Includes practical skills beyond just theory.

"Learning about logging, error handling, and build tools felt essential for a real data engineering role."

"The section on leveraging ChatGPT was a modern and very useful addition."

"This course doesn't just teach syntax, it teaches you how to code like a professional data engineer."

Covers a wide array of key Big Data technologies.

"I was impressed by how many different tools and concepts were covered, from Hadoop and Hive to Spark, Databricks, and Delta Lake."

"The course provides a really broad overview of the Big Data ecosystem, hitting all the major components you need to know."

"This course touches upon so many important areas like PySpark, Spark Scala, and Databricks, giving a solid foundation."

Strong focus on practical application and coding.

"The projects using PySpark and Spark Scala were incredibly helpful for applying what I learned in a practical setting."

"I particularly liked the hands-on labs with Dataproc and Databricks; they made the concepts much clearer."

"Getting to build a data pipeline was the most valuable part for me; it felt like real work."

Some users encountered environment setup issues.

"Getting the local setup with Winutils, Maven, and IntelliJ working correctly was a frustrating hurdle for me."

"Encountered some issues with environment configuration, which took time away from learning the core concepts."

"While cloud labs worked well, setting up the development framework locally had a few bumps."

Can be challenging for some beginners.

"While it says absolute beginners, I felt a bit overwhelmed by the speed and the sheer number of new things introduced."

"You definitely need the mentioned SQL and database knowledge; without it, parts are quite challenging."

"The course covers so much ground that some topics felt a bit rushed, and I had to supplement with other resources."

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 A Big Data Hadoop and Spark project for absolute beginners with these activities:

Review SQL Fundamentals

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Strengthen your SQL foundation to better understand Hive and Spark SQL concepts covered in the course.

Browse courses on SQL

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Review basic SQL syntax and commands.
Practice writing SQL queries for data retrieval and manipulation.
Familiarize yourself with database concepts like tables, schemas, and relationships.

Review: Hadoop: The Definitive Guide

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Gain a deeper understanding of Hadoop concepts by reading a comprehensive guide.

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

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Read the chapters related to HDFS, MapReduce, and YARN.
Take notes on key concepts and architecture details.
Relate the book's content to the course's Hadoop hands-on exercises.

PySpark DataFrame Exercises

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Reinforce your PySpark DataFrame skills through targeted practice exercises.

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Find a dataset online (e.g., Kaggle) suitable for DataFrame manipulation.
Practice common DataFrame operations like filtering, grouping, and aggregation.
Implement solutions using PySpark on Databricks.

Four other activities

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Show all seven activities

Review: Learning Spark

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Enhance your Spark skills by working through the examples and exercises in a dedicated Spark book.

View Learning Spark: Lightning-Fast Big Data Analysis on Amazon

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Read the chapters related to RDDs, DataFrames, and Spark SQL.
Run the code examples in the book using Databricks.
Complete the exercises at the end of each chapter.

Blog Post: Comparing Spark and Hadoop

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Solidify your understanding of Spark and Hadoop by creating a blog post that compares their features, use cases, and performance characteristics.

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Research the key differences between Spark and Hadoop.
Outline the blog post with sections on architecture, performance, and use cases.
Write the blog post, including code examples and diagrams.
Publish the blog post on a platform like Medium or your personal website.

Build a Data Pipeline with Spark and Hive

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Apply your knowledge by building a complete data pipeline using Spark and Hive to process and analyze a real-world dataset.

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Choose a dataset (e.g., from a public API or Kaggle).
Design a data pipeline architecture using Spark and Hive.
Implement the pipeline, including data ingestion, transformation, and storage.
Test and optimize the pipeline for performance.

Contribute to an Open Source Spark Project

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Deepen your understanding of Spark by contributing to an open-source project, such as Apache Spark itself or a related library.

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Find an open-source Spark project on GitHub or a similar platform.
Identify a bug or feature request that you can contribute to.
Fork the repository, implement the fix or feature, and submit a pull request.
Respond to feedback from the project maintainers and revise your contribution as needed.

Career center

Learners who complete A Big Data Hadoop and Spark project for absolute beginners will develop knowledge and skills that may be useful to these careers:

Data Engineer

A Data Engineer is responsible for designing, building, and maintaining the infrastructure that enables data generation, processing, and storage. This course provides an opportunity to quickly get started with Big Data, which helps build a foundation for a Data Engineer. Learning the fundamental concepts of Hadoop, Hive, and Spark using both Python and Scala prepares you to be successful as a Data Engineer. The course aims to develop your coding abilities to that of a professional developer by introducing you to industry-standard coding practices such as logging, error handling, and configuration management. Specifically, the hands-on experience with Databricks Lakehouse Platform, Spark SQL, and data pipeline development helps a Data Engineer understand real-world data challenges.

See salaries and explore the career path for Data Engineer

Spark Developer

A Spark Developer focuses on creating and maintaining applications using Apache Spark for large-scale data processing. This course helps build a foundation for anyone wishing to become a Spark Developer. The practical exercises, including developing a data pipeline with Apache Spark, directly translate to the daily tasks of a Spark Developer. Moreover, the course's focus on coding best practices, such as logging and error handling, ensures that a Spark Developer can produce high-quality, maintainable code. The lessons on optimizing query performance using Delta Cache and working with Delta Tables are directly applicable to improving the efficiency of Spark applications.

See salaries and explore the career path for Spark Developer

Big Data Architect

A Big Data Architect designs and oversees the implementation of an organization's big data strategy. The concepts of Hadoop, Hive, and Spark, using both Python and Scala, are essential tools that help build a foundation for any Big Data Architect. Diving into the Databricks Lakehouse Platform and learning how to conduct analytics using Python and Scala with Spark will help a Big Data Architect manage and optimize data workflows. The course's emphasis on developing a data pipeline with Apache Spark and managing Delta tables by accessing version history demonstrates the ability to build robust and scalable data architectures, which is highly beneficial to a Big Data Architect.

See salaries and explore the career path for Big Data Architect

ETL Developer

An Extract, Transform, Load (ETL) Developer designs and implements data pipelines to move data between different systems. This course can help build a foundation for a successful career as an ETL Developer. The focus on Hadoop, Hive, and Spark provides the tools necessary to handle large-scale data integration tasks. In particular, developing a data pipeline with Apache Spark and understanding the Databricks Lakehouse Platform can substantially enhance an ETL Developer's ability to build efficient and reliable data pipelines.

See salaries and explore the career path for ETL Developer

Hadoop Developer

A Hadoop Developer works on developing and maintaining applications within the Hadoop ecosystem. Gaining familiarity with the fundamental concepts of Hadoop, as this course provides, helps prepare individuals for this role. The hands-on experience with HDFS and Hive allows a Hadoop Developer to effectively store, process, and analyze large datasets. The course provides familiarity on how to create free Hadoop and Spark clusters using Google Dataproc. A Hadoop Developer must be familiar with the fundamentals of Google Dataproc.

See salaries and explore the career path for Hadoop Developer

Data Warehouse Architect

A Data Warehouse Architect designs, implements, and manages an organization's data warehouse. This course helps build a foundation for implementing and managing efficient data warehouses. Learning about the Databricks Lakehouse Platform and Delta Lake concepts provides a Data Warehouse Architect with modern techniques for data storage and retrieval. Furthermore, understanding how to optimize query performance using Delta Cache and work with Delta Tables helps a Data Warehouse Architect ensure the data warehouse is performant and reliable.

See salaries and explore the career path for Data Warehouse Architect

Technical Lead

A Technical Lead manages and guides a team of developers in the design, development, and implementation of technical projects. This course may provide a Technical Lead with the skills to oversee big data initiatives. The fundamental concepts of Hadoop, Hive, and Spark equip a Technical Lead to guide teams in building robust data infrastructures. Furthermore, by learning about the Databricks Lakehouse Platform and Delta Lake, a Technical Lead can strategically plan and manage data storage and processing solutions, ensuring the team's success in data projects.

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Machine Learning Engineer

A Machine Learning Engineer develops and implements machine learning models. The ability to process and manage large datasets, facilitated by tools like Spark, is crucial for a Machine Learning Engineer. Learning how to use Spark with Python (PySpark) and Scala allows for efficient data preprocessing and feature engineering, which are essential steps in the machine learning pipeline. Through this course, a Machine Learning Engineer will learn how to leverage big data technologies to build and deploy scalable machine learning solutions.

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Database Administrator

A Database Administrator (DBA) is responsible for the performance, integrity, and security of a database. The course's coverage of Hive and Spark SQL may provide some useful tools for a DBA who works with big data environments. The discussion on managing Delta tables by accessing version history, recovering data, and utilizing time travel functionality can be directly applied to database maintenance and disaster recovery tasks. These skills may prove useful to a Database Administrator.

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Cloud Solutions Architect

A Cloud Solutions Architect designs and implements cloud-based solutions. The course's direct experience with cloud platforms, such as Google Dataproc and Databricks, may be highly relevant to the daily activities of a Cloud Solutions Architect. Furthermore, understanding how to leverage Spark and Hadoop in the cloud enables a Cloud Solutions Architect to design scalable and cost-effective data processing solutions. The course's insights into real-world scenarios can provide valuable practical knowledge for a Cloud Solutions Architect working with big data applications.

See salaries and explore the career path for Cloud Solutions Architect

Business Intelligence Analyst

A Business Intelligence Analyst analyzes data to identify trends and insights that can inform business decisions. Although this course may seem geared towards engineering, it does cover essential tools and technologies for big data analytics. Familiarity with Spark SQL and Databricks SQL helps a Business Intelligence Analyst to efficiently query and analyze large datasets. The hands-on experience can enable a Business Intelligence Analyst to derive meaningful insights from complex data.

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Data Scientist

A Data Scientist uses statistical analysis, machine learning, and data visualization to extract insights from data. Managing and manipulating large datasets is a daily task for Data Scientists. This course introduces tools like Hadoop, Hive, and Spark, which may enable them to handle big data challenges. Furthermore, the exposure to Databricks and Delta Lake may prepare a Data Scientist to work with modern data lakehouse architectures, which are common in data science projects.

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Data Analytics Consultant

A Data Analytics Consultant advises organizations on how to use data to improve their business performance. This course's concepts of Hadoop, Hive, and Spark may provide a Data Analytics Consultant with the technical knowledge to recommend and implement data-driven solutions. The hands-on experience with tools like Databricks and Spark SQL helps a Data Analytics Consultant understand the practical challenges involved in big data analytics. This understanding helps data analytics consultants better guide their clients through data-related decisions.

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Software Engineer

A Software Engineer designs, develops, and tests software applications. While this Software Engineer role is broad, gaining familiarity with big data tools and technologies can open up specialized opportunities in data-intensive applications. The course's coverage of Spark, Python, and Scala can help a Software Engineer contribute to the development of data processing pipelines or analytics platforms. This allows a Software Engineer to grow their skill set and explore new areas of software development.

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Solutions Architect

A Solutions Architect designs and oversees the implementation of technical solutions to address business problems. This course may equip a Solutions Architect with the skills to design and implement big data solutions. The data engineering skills learned in this course may improve a Solutions Architect's ability to integrate large datasets and create efficient data workflows. Overall skills may enhance a Solutions Architect's ability to design comprehensive data solutions.

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A Big Data Hadoop and Spark project for absolute beginners

What's inside

Syllabus

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Reviews summary

Comprehensive big data for beginners

Activities

Career center

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