Data Engineering Courses
These data engineering courses teach essential tools like PostgreSQL, Spark, and pandas using practical, beginner-friendly projects. You’ll learn how data flows through systems, how pipelines are structured, and how clean data is prepared for analytics and machine learning.
Data Engineering Courses
These data engineering courses teach essential tools like PostgreSQL, Spark, and pandas using practical, beginner-friendly projects. You’ll learn how data flows through systems, how pipelines are structured, and how clean data is prepared for analytics and machine learning.
Showing 33 of 33 courses
- 30 courses 14 projects 81 hours 125K+
Data Engineer
In this path, you'll master the mandatory technical skills for modern data engineering, including Python programming, distributed computing, containerization, and cloud deployment. You'll learn how to work with production databases like PostgreSQL, Snowflake, and MongoDB, process data at scale with PySpark, orchestrate workflows with Apache Airflow, and deploy containerized applications to cloud platforms using Docker and Kubernetes. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Beginner Python SQLStart career path → - 4 courses 11 hours 29K+
CLI and Git
In this path, you'll learn the command line and Git skills essential for data engineering. You'll go from basic shell navigation through advanced command line techniques and version control with Git, applying everything to practical, real-world data engineering workflows.
Beginner Command Line GitStart skill path → - 3 courses 1 projects 18 hours 19K+
Production Databases
In this path, you'll learn how to work with the production database systems used in modern data engineering. You'll gain hands-on experience with PostgreSQL, Snowflake, and MongoDB through practical, real-world data engineering scenarios.
Intermediate SQL PostgreSQLStart skill path → - 2 courses 1 projects 12 hours 11K+
Data Pipelines with Airflow
In this path, you'll learn how to build data pipelines in Python and orchestrate them with Apache Airflow. You'll go from foundational pipeline concepts to automating complex workflows through hands-on, real-world scenarios.
Intermediate Python Apache AirflowStart skill path → - 2 courses 9 hours 505+
Distributed Data Processing with PySpark
In this path, you'll learn how to use PySpark and Apache Spark to process data at scale. You'll work with RDDs, DataFrames, and Spark SQL to build and optimize production ETL pipelines through hands-on, real-world data engineering scenarios.
Intermediate PySpark Apache SparkStart skill path → - 2 courses 12 hours 201+
Containerization and Infrastructure with Docker and Kubernetes
In this path, you'll learn how to use Docker and Kubernetes to containerize and orchestrate data engineering applications. You'll build reproducible environments, manage multi-service deployments, and prepare production-ready containerized systems through practical, real-world exercises.
Intermediate Docker KubernetesStart skill path → - 1 courses 8 hours 126+
Introduction to Cloud Computing
In this path, you'll learn the fundamentals of cloud computing and how to deploy data engineering systems to cloud platforms. You'll gain hands-on experience with cloud services through practical, real-world scenarios involving realistic data engineering workloads.
Intermediate Cloud Computing AWSStart skill path → - 1 courses 8 hours 61+
Data Transformation with dbt
In this path, you'll learn how to use dbt to transform raw data into analytics-ready datasets. You'll go from foundational dbt concepts through production patterns including incremental models, testing, and deployment workflows — all through hands-on, real-world scenarios.
Intermediate dbt SQLStart skill path → - 4 lessons 4 hours 58K+
Introduction to Python for Data Engineering
This Python course for beginners teaches Python fundamentals and helps you take your first steps to becoming a successful data engineer. In this course, you'll learn to write code using Python syntax; work with different types of data; and perform basic Python operations, such as working with variables, processing numerical and text data, and manipulating lists. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser.
Beginner Python Data ScienceStart course → - 5 lessons 4 hours 25K+
Command Line for Data Science
In this course, you'll learn how to navigate the filesystem, how to alter permissions for different users, and how to create and run a Python script from the command line. You'll also learn how to use the terminal on UNIX machines and how to use the command line's powerful text processing tools like awk and sed. The lack of a graphical user interface (GUI) also makes the command line faster than other approaches for many tasks. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. When you finish the course, you'll have enough hands-on practice that you'll be comfortable using the command line in your day-to-day data analysis tasks.
Intermediate Shell Command LineStart course → - 7 lessons 7 hours 19K+
PostgreSQL for Data Engineering
In this course, you'll learn about the SQL database management system PostgreSQL and what differentiates it from SQLite. You'll learn about proper data types for your data and why they're important. You'll also install PostgreSQL on your own machine and learn how to work with psycopg2, a Python database API for PostgreSQL that allows you to interact with PostgreSQL databases using Python. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll complete a project that asks you to work on a real-life example - storing storm data in a PostgreSQL database.
Intermediate PostgreSQL SQLStart course → - 5 lessons 3 hours 16K+
Dictionaries and Functions in Python
In this course, you'll explore the world of Python data engineering. You'll learn basic Python concepts such as dictionaries, functions, and default arguments. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. The course will conclude with two guided projects: The first one teaches you to learn and install Jupyter Notebook The second one asks you to perform practical data analysis on profitable app profiles for the App Store and Google Play Market
Beginner Python Data ScienceStart course → - 5 lessons 4 hours 16K+
Text Processing for Data Science
This course builds on the Command Line for Data Science course. You'll learn how to read documentation, how to inspect files, how to perform basic text processing using the command line, how to redirect and pipe output, and how to access documentation for different commands if you get stuck. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate Shell Text ProcessingStart course → - 5 lessons 4 hours 11K+
Building a Data Pipeline
In this course, you'll learn how to build a simple data pipeline using imperative and functional paradigms. You'll also learn how to use functional closures in Python, how to implement a well-designed pipeline API, how to write decorators, and how to apply them to functions. At the end of the course, you'll work on a real-world project, using a data pipeline to summarize Hacker News data. This project is a chance for you to combine the skills you learned in this course and build a real-world data pipeline from raw data to summarization.
Intermediate Python Data PipelinesStart course → - 5 lessons 3 hours 11K+
Introduction to Git and Version Control
This course teaches you how to use Git, one of the most popular version control systems. You will learn how Git is helpful in the context of data analysis and data science. We'll cover the fundamentals, including how to clone a project to your local machine, how to iterate on the project by creating branches, and how to push your work to Git remotes like GitHub. You'll learn how Git automatically creates "merge conflicts" to prevent catastrophic mistakes. By the end of this course, you'll know how to install Git on your local machine. An active GitHub account is crucial for making your data analysis and data science projects available to potential employers. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate Git ShellStart course → - 7 lessons 3 hours 9.3K+
Intermediate Command Line for Data Science
In our intermediate command line course, you'll learn to improve your data analysis workflow with concepts such as piping and redirecting output into a file; searching files for a string; and cleaning, exploring, and consolidating data using the command line. You'll also learn to work with Jupyter console, an enhanced Python interpreter, to develop scripts. And you'll learn how to clean and explore data using csvkit, a suite of command line tools for converting and working with CSV formats. Then you'll build a project that combines your Python data skills with your new command line expertise - you'll write Python scripts to compute summary statistics and then run the scripts directly from the command line. When you finish this course, you'll be able to add "UNIX Command Line" skills to your data science resume!
Intermediate Shell Command LineStart course → - 4 lessons 5 hours 6.8K+
Intermediate Python for Data Engineering
In this course, you'll expand your Python for data engineering knowledge. Using real-world data from the Museum of Modern Art, you' ll learn how to prepare text data, introduce uniformity into a messy dataset, and more. You'll also explore object-oriented programming (OOP) and how it powers Python. Finally, you'll learn new and exciting Python coding concepts for data engineering. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Beginner Python Data ScienceStart course → - 6 lessons 7 hours 6.1K+
Introduction to Algorithms
Algorithms are at the center of almost any programming job - particularly in the world of data engineering, where this is a recurring topic in job interviews. In this course, you'll learn how to assess and model the time and space complexity of algorithms (i.e., how fast they'll be, how much memory they'll require), and you'll learn how to trade memory for speed. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll put together what you've learned in a guided project that tasks you with building indices for a CSV using dictionaries.
Intermediate Algorithms PythonStart course → - 7 lessons 5 hours 5.8K+
Processing Large Datasets In Pandas
In this course, you'll learn how to reduce the memory footprint of a pandas DataFrame while working with data from the Museum of Modern Art. You'll learn how to work with DataFrame chunks, how to use them to increase processing speed in pandas, and how to optimize DataFrame types while exploring data from the Lending Club. You'll also learn how to augment pandas with SQLite to combine the best of both tools. Finally, you'll learn when to use disk space over in-memory space, as well as how to run SQL queries using pandas. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll complete a project that asks you to work on a real-life example - using the pandas SQLite workflow to analyze startup fundraising deals using data from CrunchBase.
Intermediate Pandas SQLiteStart course → - 4 lessons 4 hours 5.4K+
Programming Concepts in Python
In this course, you'll build a critical understanding of the inner workings of Python and basic computation. You'll also explore basic number systems, methods of encoding data, how to work with text files, and the best way to optimize data usage. Finally, you'll learn how to develop simple techniques for reading and writing to files, converting between encodings, and optimizing data usage. This course will put you on your way to mastering Python for data engineering. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Beginner Python Data ScienceStart course → - 5 lessons 4 hours 4.0K+
Optimizing PostgreSQL Databases
In this course, you'll learn how to write database descriptions. You'll discover how to manage meta information about databases and tables by using PostgreSQL Internals. You'll also learn how to debug your PostgreSQL queries using the EXPLAIN clause. You'll learn how to measure estimated and actual execution times of your queries and determine which SQL clause is the most computationally expensive to perform, as well as the biggest cause for long-running queries. You'll learn concepts such as indexing and how it can greatly reduce querying speed. You'll also learn what it means to vacuum a PostgreSQL database, how it reduces query speeds, and how to vacuum a database, as well as what ACID means for database transactions and why it's important for transaction blocks. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate PostgreSQL SQLStart course → - 5 lessons 4 hours 3.1K+
NumPy for Data Engineering
Python programming skills are critical for data engineering. But for many critical data analysis and processing tasks, using stock Python isn't the most efficient approach. That's where NumPy comes in. In this course, you'll learn how to manipulate data using NumPy - it's much more efficient than Python alone if you're working with large amounts of data. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate NumPy PythonStart course → - 6 lessons 4 hours 2.8K+
Introduction to Data Structures
In this course, you'll learn the fundamentals of data structures. You'll explore linked lists and how using linked nodes is helpful in creating data structures. Then you'll learn about queues, the FIFO data structure (first in, first out) , and the FCPS process scheduling algorithm (first come, first serve). From there, you'll dig into stacks, LIFO (last in, first out), and LCFS process scheduling (last come, first serve) - and then dictionaries and parallel processing. By the end, you'll understand the performance difference between data structures such as hash tables, stacks, queues, and more. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. You'll apply this knowledge by completing two real-world data projects: In the first one, you'll use stacks when implementing complex algorithms In the second one, you'll analyze stock prices using hash tables and by implementing various algorithms
Intermediate Python Data StructuresStart course → - 5 lessons 5 hours 2.6K+
Parallel Processing for Data Engineering
In this course, you’ll explore how to process large datasets efficiently using parallel processing and the MapReduce programming model. You’ll learn how to divide work across multiple processors, implement MapReduce workflows, and apply these techniques to common data engineering problems. Through hands-on practice, you’ll gain practical experience designing scalable solutions for data-intensive tasks.
Advanced Python Parallel ProcessingStart course → - 8 lessons 5 hours 1.9K+
Recursion and Trees for Data Engineering
In this course, you'll learn about recursion, binary trees, binary heaps, and more. By the end, you'll be able to explain the difference between iteration and recursion, build a binary heap to query large datasets, implement and query a dataset using Binary Search trees, and more. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll complete a guided project in which you'll use a B-Tree to implement a key-value datastore in Python.
Advanced Python Data StructuresStart course → - 3 lessons 6 hours 177+
Docker Fundamentals
Modern data engineering requires reproducible environments that work the same on every machine. Docker creates isolated containers that bundle everything your code needs to run—dependencies, databases, configuration—eliminating "it worked on my machine" problems. This course takes you from Docker fundamentals through production-ready containerization. You'll start by running PostgreSQL in a container, connecting to it, and persisting data with volumes. Then you'll use Docker Compose to orchestrate complete data pipelines: a Python ETL script that connects to a database, all defined in a single file and started with one command. Finally, you'll learn the production patterns that DevOps teams expect—health checks that prevent startup race conditions, multi-stage builds that create slim images, security hardening with non-root users, and proper secret management with environment files. By the end, you'll build containerized data workflows that are portable, maintainable, and ready for production deployment.
Intermediate Data Science Data AnalysisStart course → - 3 lessons 6 hours 166+
PySpark for Data Engineering
Building PySpark notebooks is one thing. Building production pipelines that integrate with your company's cloud infrastructure is another. This course teaches you to write PySpark code that runs reliably every day in real environments. You'll start by building a complete ETL pipeline that cleans messy CSV data with inconsistent formats and quality issues. Then you'll learn systematic performance optimization, taking a slow pipeline and making it 10x faster by reading the Spark UI and applying targeted fixes. Finally, you'll explore the big data ecosystem—understanding managed Spark platforms like Databricks and how to integrate PySpark with cloud storage (AWS S3) and data catalogs (AWS Glue). By the end, you'll know how to build pipelines that work at scale, diagnose performance problems, and deploy on the platforms that companies actually use.
Intermediate PySpark SparkStart course → - 4 lessons 8 hours 155+
Building Data Pipelines with Apache Airflow
Manual scripts and cron jobs break down as data pipelines grow complex. Apache Airflow brings order to chaos through workflow orchestration—ensuring tasks run in the right order, at the right time, with proper failure handling and monitoring. This course teaches you to build production-grade data pipelines the way professional teams do. You'll start by understanding orchestration concepts and Airflow's architecture, then deploy a complete Airflow environment in Docker. Using the TaskFlow API, you'll build increasingly sophisticated workflows: from simple ETL processes to pipelines with dynamic parallel processing and database connections. You'll integrate Git-based version control and GitHub Actions CI/CD for automated deployment. Finally, you'll build a real-world pipeline that scrapes Amazon book data, cleans it with Python, and loads it into MySQL on a schedule—complete with monitoring and alerting. By the end, you'll have the skills to orchestrate complex data workflows reliably at scale.
Intermediate Airflow PythonStart course → - 4 lessons 8 hours 126+
Introduction to Cloud Computing
Cloud computing transformed technology from owning infrastructure to renting it on demand — eliminating the complexity and cost of managing physical servers. This course teaches you the fundamentals. You'll understand service models: IaaS for maximum control, PaaS for faster development, and SaaS for turnkey solutions. You'll explore deployment strategies — public clouds for scalability, private for security, hybrid for flexibility — and compare AWS, Azure, and GCP to understand what each platform offers. By the end, you'll have the conceptual foundation to make smart cloud architecture decisions and be ready to deploy real pipelines to AWS and GCP in the next course.
Intermediate Cloud Computing AWSStart course → - 3 lessons 6 hours 119+
Production Database Tools
Production data systems require more than traditional SQL databases. This course takes you beyond PostgreSQL into the tools that power modern data infrastructure at scale. You'll start with Snowflake, learning how its cloud-native architecture separates storage from compute to handle massive datasets efficiently. Then you'll explore the NoSQL landscape—understanding when document, key-value, column-family, and graph databases solve problems that SQL can't. Finally, you'll get hands-on with MongoDB, building a flexible review system that handles schema changes without migrations and connects to Python analytics workflows. By the end, you'll understand how companies like Netflix and Uber combine multiple database types in production, and you'll be able to choose the right tool for each part of your data pipeline.
Intermediate Data Science Data AnalysisStart course → - 3 lessons 6 hours 93+
Introduction to Kubernetes
Kubernetes transforms container management from manual tasks into automated systems. This course teaches you to orchestrate containerized applications at scale through hands-on practice with realistic scenarios. You'll start by deploying applications to local clusters and watching Kubernetes automatically replace crashed pods. Then you'll solve the networking puzzle—how applications find each other when pods constantly restart with new IP addresses—using Services and performing rolling updates for zero-downtime deployments. Finally, you'll add production safeguards: health checks that prevent broken applications from receiving traffic, resource limits that protect clusters from runaway workloads, and ConfigMaps and Secrets for secure configuration management. By the end, you'll understand when Kubernetes adds value over Docker Compose and how to build applications that are good citizens in shared production clusters.
Intermediate Data Science Data AnalysisStart course → - 4 lessons 8 hours 61+
Data Transformation with dbt
dbt has become essential for transforming data in modern analytics workflows. In this course, you'll learn to build transformation pipelines from the ground up—starting with core concepts like models and DAGs, then adding testing, documentation, and incremental processing. You'll finish with production patterns that make pipelines maintainable and deployable, always with an eye toward knowing when added complexity is justified.
Intermediate dbt SQLStart course → - 5 lessons 10 hours 57+
Deploying to the Cloud
You understand cloud fundamentals — now deploy for real. This course takes you through hands-on deployment to both AWS and GCP. On AWS, you'll provision S3 buckets, RDS databases, and deploy Airflow to ECS with Fargate — a managed container architecture with load balancing and auto-scaling. On GCP, you'll take the same pipeline and deploy it to a Compute Engine VM with Docker Compose, configure service accounts for secure access, and upload pipeline output to Cloud Storage. You'll see how the same Airflow and Docker skills transfer across platforms while the infrastructure layer changes. By the end, you'll have deployed working pipelines to both major cloud providers — giving you the versatility to work on any team, with any stack.
Intermediate Cloud Computing AWSStart course →
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Learn Data Engineering by building projects
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Free
Project
Profitable App Profiles for the App Store and Google Play Markets
For this project, we’ll assume the role of data analysts for a company that builds free Android and iOS apps. Our revenue depends on in-app ads, so our goal is to analyze data to determine which kinds of apps attract more users.
14 steps Start project → -
Free
Project
Analyzing Kickstarter Projects
For this project, you’ll assume the role of a data analyst at a startup considering launching a Kickstarter campaign. You’ll analyze data to help the team understand what might influence a campaign’s success.
8 steps Start project → -
Free
Project
Exploring Hacker News Posts
For this project, we’ll step into the role of data analysts to explore Hacker News submissions, analyzing trends using skills in string manipulation, object-oriented programming, and date handling in Python.
8 steps Start project → -
Free
Project
Building Fast Queries on a CSV
For this project, we’ll step into the role of Python developers to build an inventory system for a laptop store. We’ll apply efficient data structures and algorithms to enable fast queries.
10 steps Start project →
Frequently Asked Questions
How do you choose the right data engineering course for your goals?
Start by identifying the core skills needed for data engineering roles. These include Python, SQL, database management, and pipeline orchestration. Together, they form the basis of data engineering fundamentals.
If you are new to the field, choose a structured course that focuses on hands-on learning. Practical exercises and real examples help you understand how data engineering solutions work in real systems. Dataquest’s career paths teach these skills step-by-step through guided, practical courses.
What is data engineering?
Data engineering focuses on building systems that collect, store, and process data at scale. Engineers use tools like SQL, Python, and cloud platforms to create pipelines that keep data clean, reliable, and accessible for analysts and data scientists. Dataquest teaches these data engineering fundamentals through interactive lessons where you build pipelines and manage databases hands on.
Is data engineering hard to learn?
No, data engineering is not hard to learn, but it does require time and practice. It builds on concepts from software engineering and computer science, including coding, data management, and distributed systems.
Dataquest makes learning manageable by breaking complex topics into small lessons and providing hands-on exercises with immediate feedback. This structured, project-based approach helps you understand how data flows through systems while building confidence step by step.
What are the best data engineering courses online?
The best courses focus on building real systems such as pipelines, databases, and data architectures. They teach data engineering foundations using industry-standard tools like Python, SQL, and Spark.
Dataquest stands out by going beyond video lessons. You write code and configure systems directly in your browser, which helps you understand data engineering basics through practice. Learners say this hands-on approach gives them the practical experience employers expect.
Are data engineering skills still in demand?
Yes, demand is incredibly high. As companies collect more data and adopt AI, they need engineers to build the infrastructure that supports it. AI cannot function without the clean, accessible data pipelines that engineers build. Dataquest’s projects help you build the foundational skills to stay essential in this data-driven economy.
What jobs can you get with data engineering skills?
Data engineering skills prepare you for data engineering roles such as: Data Engineer, Analytics Engineer, Database Administrator, ETL Developer, Cloud Data Engineer
Your opportunities expand as you master tools like Python, SQL, PostgreSQL, and cloud concepts. Dataquest paths help you build these skills step by step.
Which programming language should you learn first for data engineering?
Python and SQL are the non-negotiable foundations. SQL is used to manage and query data, while Python is used to write the scripts that move and transform that data (ETL). Dataquest teaches both in parallel, ensuring you have the complete toolkit needed for the job.
How is a data engineer different from a data scientist or data analyst?
A data engineer builds and maintains the systems that move and store data. This includes data pipelines, data storage, and data processing.
A data scientist analyzes data to build models and generate insights, often using statistics and machine learning.
A data analyst focuses on querying data, creating reports, and supporting business decisions. Data engineers support both roles by making sure data is reliable and accessible.
What is the difference between a data engineer and a data architect?
A data engineer implements and maintains data systems in practice. This includes building pipelines, managing databases, and handling data ingestion.
A data architect designs the overall structure of data systems. They define how data flows, how it is stored, and how different systems connect. Data engineers turn this architecture into working systems.
Do you need a technical background before starting data engineering courses?
While helpful, it is not required. Many learners start from zero. Our courses begin with the basics of Python and SQL and progressively introduce more complex engineering concepts, using hands-on practice to build your confidence.
What tools are commonly used in data engineering?
Key tools include Python, SQL, PostgreSQL, Spark, command line (Bash), Airflow, and cloud services (AWS/Azure). Dataquest integrates many of these tools directly into your browser, allowing you to learn the modern data stack by using it.
What is the best way to learn data engineering fast?
Follow a structured path that combines coding practice with architectural concepts. Dataquest speeds up learning by removing fluff and focusing on the practical skills used on the job, reinforced by projects that simulate real engineering tasks.
How long will it take to become job-ready in data engineering?
Most learners are ready to apply for a data engineering job in 6 to 12 months, depending on their prior experience and the time they dedicate each week.
Dataquest’s paths focus on practical work with hands-on projects and portfolio-building exercises. You gain experience with coding, data systems, and cloud tools, and you can show employers what you can build.
How much do data engineering courses cost?
Costs vary widely, from free introductory courses to monthly subscriptions on learning platforms to university programs costing thousands.
Dataquest offers an affordable subscription with full access to all data engineering, data science, analytics, and AI courses. It also includes free lessons and a 14-day money-back guarantee, so you can start learning risk-free.
Will you get a certificate, and does it help you stand out?
Yes. You earn a data engineering certificate for each course and path you complete. However, in data engineering, your ability to code and design systems matters most. The real-world projects you build on Dataquest demonstrate these practical skills to employers better than a certificate alone.
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