The Best Data Engineering Online Courses

Banner Image The Best Data Engineering Online Courses

Are you ready to get your hands dirty with big data? I know I am! Being a data engineer is one of the most in-demand and lucrative career paths in today’s tech landscape, and trust me, the need for skilled data professionals won’t be dwindling anytime soon. With our increasingly data-driven world, having the ability to collect, process, and utilize those massive amounts of information is crucial for businesses to grow and innovate. So, why not dive in and learn the ropes? That’s where online courses come in – giving you the flexibility to level up your data engineering skills at your own pace and in your own environment.

But with so many data engineering courses out there on the vast world wide web, where should you start? Fear not, dear reader! We’ve got you covered. In this blog post, we’ll navigate our way through a carefully curated selection of top data engineering online courses. Each course is designed to help you learn or sharpen those essential big data skills, from mastering popular platforms like Hadoop and Spark, to creating durable data pipelines and implementing ETL processes. Whether you’re a complete beginner or an experienced data whiz, you’ll find something to suit your needs and kickstart your data engineering journey. So grab a cup of joe (or the caffeinated beverage of your choice), and buckle in – we’re about to embark on a data expedition like no other!

Data Engineering Courses – Table of Contents

  1. Data Engineering Essentials using SQL, Python, and PySpark
  2. Data Engineering using AWS Data Analytics
  3. Master Data Engineering using GCP Data Analytics
  4. 100 Days of Code: The Complete Python Pro Bootcamp for 2023
  5. DP-203: Data Engineering on Microsoft Azure – 2022
  6. The Data Science Course: Complete Data Science Bootcamp
  7. Writing production-ready ETL pipelines in Python / Pandas
  8. Data Engineering using Databricks on AWS and Azure

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Data Engineering Essentials using SQL, Python, and PySpark

Course Preview Data Engineering Essentials using SQL, Python, and PySpark


4.4 out of 5

If you’re on the hunt for a comprehensive data engineering course, look no further! This course covers essential data engineering skills, teaching you how to build data pipelines using SQL, Python, Hadoop, Hive, Spark SQL, and PySpark Data Frame APIs. You’ll develop and deploy Python applications using Docker, manage PySpark on multinode clusters, and gain basic know-how on reviewing Spark Jobs using Spark UI. The course also addresses key challenges that learners often face, ensuring that you have a suitable environment, quality content, and adequate exercises to practice.

The course is designed for professionals at all levels and covers a wide range of topics, such as setting up your environment and necessary tables, writing basic and advanced SQL queries with practical examples, performance tuning of queries, Python programming basics, data processing using Pandas, troubleshooting and debugging scenarios, and so much more. You’ll even have the opportunity to work on real-time Python projects! With its emphasis on hands-on learning and in-depth coverage of essential data engineering skills, this course will undoubtedly set you on the path to mastering the art of data processing and pipeline development.

Skills you’ll learn in this course:

  1. Master data engineering essentials using SQL, Python, and PySpark Data Frame APIs.
  2. Develop and deploy Python applications using Docker & PySpark on multinode clusters.
  3. Build various data pipelines, including batch and streaming pipelines.
  4. Troubleshoot and debug database-related issues and performance tuning of SQL queries.
  5. Gain proficiency in programming using Python as a language and Python collections for data engineering.
  6. Work on real-time Python projects and learn data processing using Pandas.
  7. Set up and work with Google Cloud Platform and Databricks for Spark environment and write basic Spark SQL queries.
  8. Gain an in-depth understanding of Apache Spark Catalyst Optimizer, Explain Plans, and performance tuning using Partitioning.

Data Engineering using AWS Data Analytics

Course Preview Data Engineering using AWS Data Analytics


4.4 out of 5

Data Engineering has become an important field in today’s data-driven world, and this course aims to help you build Data Engineering Pipelines using AWS Data Analytics Stack. Covering a wide range of AWS services such as Glue, Elastic Map Reduce (EMR), Lambda Functions, Athena, Kinesis, and more; it will make sure you acquire the essential skills to create efficient data processing pipelines on the cloud.

The course starts by setting up your development environment and providing a solid foundation by covering AWS basics, including storage (Simple Storage Service), user level security (Identity & Access Management), and infrastructure (Elastic Cloud Compute). As you progress, you will learn how to build data ingestion pipelines using AWS Lambda Functions, explore AWS Glue Components, and even dive into deploying Spark applications using AWS EMR. The course also covers various aspects of AWS Kinesis, Amazon Redshift, and more advanced features like AWS Redshift Federated Queries and Spectrum to ensure you have a comprehensive understanding of the AWS Data Analytics Stack.

In summary, this course will help you build the foundational knowledge required to work with AWS Data Analytics Services and enable you to create

Skills you’ll learn in this course:

  1. Build and manage data pipelines using AWS Data Analytics Stack.
  2. Manage storage resources and user-level security using AWS S3 and IAM.
  3. Develop AWS Lambda Functions for data ingestion.
  4. Work with AWS Glue components and setup Spark History Server.
  5. Develop and deploy Spark applications using AWS EMR.
  6. Build streaming data ingestion pipelines with AWS Kinesis.
  7. Use AWS Athena for querying and managing data on AWS.
  8. Develop applications using AWS Redshift cluster and optimize with Distkeys and Sortkeys.

Master Data Engineering using GCP Data Analytics

Course Preview Master Data Engineering using GCP Data Analytics


4.6 out of 5

Dive into the world of Data Engineering with this comprehensive course on building Data Engineering Pipelines using GCP Data Analytics Stack. You’ll explore various services, including Google Cloud Storage, Google BigQuery, GCP Dataproc, and Databricks on GCP. The course kicks off with setting up an environment using VS Code on Windows or Mac, then guides you through the process of signing up for a Google Cloud Account, complete with instructions for claiming your USD 300 credit.

Throughout the course, you’ll learn how to use Google Cloud Storage as a Data Lake, manage files with both commands and Python, and integrate Pandas. You’ll also discover how to set up a PostgreSQL Database Server using Cloud SQL, and develop Python applications integrated with GCP Secretmanager. As you progress, you’ll explore BigQuery as a Data Warehouse, learn about GCP Dataproc for Big Data Processing, and set up a development environment using VS Code with remote connection to the Dataproc Cluster. Additionally, the course covers end-to-end ELT data pipeline building using Dataproc Workflow Templates and Databricks Jobs and Workflows. By the end of the course, you’ll be comfortable with BigQuery, GCP Dataproc, and integrating these key services to build ELT Data Pipelines.

Skills you’ll learn in this course:

  1. Setting up a development environment with VS Code on Windows and Mac.
  2. Using Google Cloud Storage as a Data Lake and managing files with commands and Python.
  3. Setting up PostgreSQL Database Servers using Cloud SQL and integrating with GCP Secretmanager.
  4. Exploring BigQuery as a Data Warehouse and understanding its integrations with Python and Pandas.
  5. Setting up and managing GCP Dataproc clusters for Big Data Processing.
  6. Building end-to-end ELT Data Pipelines using Dataproc Workflow Templates and Spark SQL.
  7. Getting started with Databricks on GCP and building end-to-end ELT Data Pipelines using Databricks Jobs and Workflows.
  8. Integrating BigQuery and GCP Dataproc to build end-to-end ELT Data Pipelines with Spark BigQuery connector.

100 Days of Code: The Complete Python Pro Bootcamp for 2023

Course Preview 100 Days of Code: The Complete Python Pro Bootcamp for 2023


4.7 out of 5

Get ready to dive into the “100 Days of Code – The Complete Python Pro Bootcamp,” a comprehensive online course designed to teach coding with Python! With an impressive 4.8 average rating and over 500,000 5-star reviews, this course stands out as one of the highest-rated courses on Udemy. Tailored for learners with varying levels of programming experience, including absolute beginners, this 60+ hour course will guide you through step-by-step video tutorials, helping you master Python through engaging real-world projects.

The course curriculum, developed by the lead instructor at the App Brewery and refined over a period of 2 years, covers a massive range of tools and technologies including Python 3, PyCharm, Jupyter Notebook, web scraping, web development, and data science, among many others. It also offers numerous real-world projects, such as auto job applications on LinkedIn, blog websites, and even popular games like Snake and Pong, in order to help you hone your skills and build an impressive portfolio. Moreover, with constant updates and new content driven by student feedback, you’ll never fall behind the curve! So why wait? Sign up now and enjoy learning to code with support from high-quality materials, code challenges, and quizzes, as well as a full money-back guarantee for 30 days.

Skills you’ll learn in this course:

  1. Python 3 programming and scripting automation
  2. Web scraping with Beautiful Soup and Selenium WebDriver
  3. Data Science using Pandas, NumPy, Matplotlib, and Plotly
  4. Python GUI Desktop App Development with Tkinter
  5. Front-End Web Development using HTML, CSS, and Bootstrap
  6. Backend Web Development with Flask, REST, APIs and databases
  7. Git, GitHub, and Version Control
  8. Deployment with GitHub Pages, Heroku, and GUnicorn

DP-203: Data Engineering on Microsoft Azure – 2022

Course Preview DP-203: Data Engineering on Microsoft Azure - 2022


4.5 out of 5

Looking for a way to boost your data engineering career and get ready for the Microsoft Azure DP-203 certification exam? You’ve come across the right course! Designed specifically for Azure data engineers, this comprehensive course will guide you through everything you need to know about implementation, creation, management, and configuration of data services in the Azure portal. According to the 2019 dice dot com report, data engineering roles experienced the highest growth rate among all technological jobs, making the DP-203 certification a smart career move.

This course is jam-packed with 25+ hours of content, two practice tests, quizzes, and supplementary study materials – all designed to cover 100% of the exam syllabus and get you prepared to tackle the DP-203 exam. Course highlights also include full lifetime access with all future updates, a 30-day money-back guarantee, and a certificate of completion. Plus, with a broad intended audience, from those preparing for the DP-203 exam to database administrators and data analysts, everybody can benefit! So, why wait? Enroll today and kickstart your journey towards becoming a certified Azure Data Engineer.

Skills you’ll learn in this course:

  1. Implement and manage non-relational data stores such as Blob Storage and Cosmos DB.
  2. Implement and manage relational data stores like Azure SQL Server and Azure Synapse Analytics Service.
  3. Manage data security with data masking and encryption techniques.
  4. Develop batch processing solutions using Azure Data Factory and Azure Databricks.
  5. Develop streaming solutions with Azure Streaming Service.
  6. Monitor data storage services like Azure Blob Storage, Azure Data Lake, Azure SQL Database, Azure Synapse Analytics, and Azure Cosmos DB.
  7. Monitor data processing services like Azure Data Factory, Azure Databricks, and Azure Stream Analytics.
  8. Optimize Azure data solutions such as Azure Data Lake, Azure Stream Analytics, Azure Synapse Analytics, and Azure SQL Database.

The Data Science Course: Complete Data Science Bootcamp

Course Preview The Data Science Course: Complete Data Science Bootcamp


4.6 out of 5

Introducing “The Data Science Course 2023,” a comprehensive online training program designed to address the challenges of entering the data science field. This program ensures that students acquire the necessary skills in the right order, taking you from an absolute beginner to a qualified data scientist at a fraction of the cost and time of traditional programs. The Data Science Course 2023 covers a broad range of topics, including data science fundamentals, mathematics, statistics, Python programming, data visualization with Tableau, advanced statistics, machine learning with TensorFlow, and deep learning.

The course is structured to flow smoothly and complement previous topics, starting with an introduction to data and data science, progressing through essential mathematics and statistics concepts, Python programming, Tableau visualization, advanced statistical techniques, and ultimately machine learning and deep learning. Not only will you gain the practical knowledge needed to become a data scientist, but you’ll also develop a strong foundation in thinking like a scientist with a focus on problem-solving and hypothesis testing. So why wait? Click the “Buy Now” button and begin your journey to becoming a data scientist from scratch today.

Skills you’ll learn in this course:

  1. Comprehensive understanding of data science concepts and methods
  2. Proficiency in mathematics, particularly calculus and linear algebra
  3. Strong foundation in statistics and hypothesis testing
  4. Python programming skills for data manipulation and analysis
  5. Data visualization using Tableau for effective storytelling
  6. Mastery of advanced statistics for predictive modeling
  7. Practical knowledge of machine learning techniques
  8. Deep learning methods implementation with TensorFlow

Writing production-ready ETL pipelines in Python / Pandas

Course Preview Writing production-ready ETL pipelines in Python / Pandas


4.3 out of 5

Get ready to dive into the world of ETL pipelines with this comprehensive course! Utilizing tools like Python 3.9, Jupyter Notebook, Git, Github, Visual Studio Code, Docker, and various Python packages, you’ll learn how to write an ETL pipeline in Python from scratch to production. The course content covers both functional and object-oriented programming approaches, ensuring you have a well-rounded understanding of data engineering.

Throughout the course, you’ll apply best practices in Python code development such as design principles, clean coding, virtual environments, logging, and more. The primary goal of this course is to use the Xetra dataset (minute-by-minute trading data from Deutsche Börse Group) to build an ETL pipeline that extracts, transforms, and loads data to an AWS S3 target bucket. You’ll learn to deploy your pipeline effortlessly on various production platforms using containerized applications. Expect a mix of practical interactive lessons, hands-on coding, and supporting theory lessons. Course materials also include Python code for each lesson, a complete GitHub project, and a ready-to-use Docker image. Happy learning!

Skills you’ll learn in this course:

  1. Developing Python ETL pipelines using functional and object-oriented programming approaches.
  2. Applying design principles and clean coding practices in Python projects.
  3. Utilizing virtual environments, project/folder setup, and configuration management.
  4. Implementing logging and exception handling in Python code.
  5. Mastering linting, dependency management, and performance tuning with profiling.
  6. Conducting unit testing and integration testing in Python projects.
  7. Deploying and dockerizing Python code for production environments.
  8. Using Python packages and tools like Pandas, botopyyaml, and Jupyter Notebook to build and manage ETL pipelines.

Data Engineering using Databricks on AWS and Azure

Course Preview Data Engineering using Databricks on AWS and Azure


4.4 out of 5

This online course covers everything you need to know about Data Engineering using the cloud platform-agnostic technology, Databricks. Participants will dive deep into various aspects of Data Engineering pipelines, ranging from batch to streaming. The course focuses on Databricks, a popular cloud-based data engineering tech stack that evolved from the Apache Spark project. Key features of Databricks, such as Spark, Delta Lake, cloudFiles, and Databricks SQL, are thoroughly explored throughout the course modules.

The extensive curriculum includes setting up local development environments, learning Databricks CLI to manage data engineering applications, developing Spark applications, working with Databricks jobs and clusters, deploying and running jobs on Databricks job clusters, handling Delta Lake using DataFrames and Spark SQL, and much more. This advanced course is ideal for experienced application developers, data engineers, and testers who want to gain expertise in Data Engineering using Databricks. Prerequisites for the course include prior knowledge of Apache Spark, experience as a Data Engineer, and familiarity with cloud concepts. Note that participants are responsible for any associated AWS or Azure and Databricks costs, as only course materials are provided.

Skills you’ll learn in this course:

  1. Data Engineering concepts with Databricks
  2. Databricks platform features and its components
  3. Set up local development environment for Data Engineering Applications using Databricks
  4. Develop and deploy Spark Data Engineering jobs on Databricks
  5. Delta Lake deep dive with DataFrames and Spark SQL
  6. Building Data Engineering pipelines using Spark Structured Streaming
  7. Incremental file processing with Databricks Auto Loader cloudFiles
  8. Analyze and visualize data using Databricks SQL

In conclusion, the world of data engineering is vast and ever-evolving, offering immense opportunities for personal and professional growth. With a plethora of online courses available, getting started or advancing your skills has never been more accessible. By researching and choosing the right fit for your specific needs, you can confidently embark on a journey to becoming a proficient data engineer.

Remember, learning is a continuous process, and it’s crucial to stay up-to-date with the latest advancements in the field. Don’t fret if a concept doesn’t click right away, or if you need to revisit certain topics— persistence is key. As you dive into the world of data engineering through online courses, always be open to learning, networking, and embracing the challenges that come your way. Equip yourself with the necessary tools and knowledge, and watch as doors for exciting career opportunities open up ahead. Happy learning!