Learn Data Engineering From Scratch in 2025: The Complete Guide

Understanding the Role of a Data Engineer

Before we go into the details, let’s first understand what a data engineer's day-to-day entails.

At its core, data engineering focuses on designing and maintaining systems that handle data efficiently. These systems must work at scale, process data quickly, and guarantee accuracy. Here is how data engineers spend their working days:

Designing data pipelines

Data engineers create workflows that move data from its source (like databases, APIs, or logs) to a central storage system like a data warehouse or data lake. This process, called ETL (Extract, Transform, Load), involves:

• Extracting raw data from multiple sources.
• Cleaning, transforming, and formatting it into a usable state.
• Loading it into storage systems where it can be accessed for analysis.

In my opinion, the design process is one of the most fun parts of being a data engineer.

Optimizing data storage

Data engineers make sure data is stored securely and efficiently. They choose the right databases or storage solutions depending on the type and size of the data:

• Relational databases for structured data, like customer orders.
• NoSQL databases for unstructured or semi-structured data, like social media posts.
• Cloud solutions like Amazon S3 or Google Cloud Storage for scalability and cost-efficiency.

Protecting data quality

High-quality data is required for accurate analysis. That’s why data engineers implement checks to monitor the integrity, consistency, and accuracy of data throughout its lifecycle. This includes:

• Detecting and correcting errors in data.
• Preventing duplicate or incomplete records.
• Building validation rules that automatically flag issues.

In practice, good data quality practices are often overlooked. That’s a mistake, and I advise you to include this topic in your learning path from the beginning, which will set you apart as a data engineer. The Introduction to Data Quality course is a great place to start.

Collaborating across teams

Data engineers work closely with:

• Data scientists to provide datasets for machine learning and analytics.
• Analysts to ensure dashboards and reports have reliable data.
• Software engineers to integrate data workflows into applications.

Data scientists and analysts are our stakeholders, meaning they’re often the end users of our data products. Software engineers are often the ones who produce the data we handle. 

So, by understanding the needs of different teams, data engineers align the infrastructure with the company’s overall goals.

Maintaining system performance

With ever-growing data volumes, pipelines and systems must remain fast and scalable. Data engineers:

• Optimize workflows to handle large datasets.
• Implement solutions that reduce latency and improve processing times.

This becomes more relevant depending on the industry or specific company you work for as a data engineer. Performance is always important, but it is even more important if you dream of working at a company that handles massive amounts of data, such as Netflix, Meta, or Amazon.

Regardless of the company, the truth is that most of your time as a data engineer will be spent maintaining and enhancing data pipelines!

Monitoring and troubleshooting

Things can go wrong in any system, and data engineers ensure these issues are caught early! They set up alerts and dashboards to track:

• Pipeline failures.
• System slowdowns.
• Data inconsistencies.

Data engineers often deal with foundational data pipelines, which are critical for a company's smooth operation. I know this because, for many years, I was part of my team's on-call rotation. Our goal was to troubleshoot and fix issues to keep systems running smoothly when they arise.

Data Engineering vs. Related Fields

The previous section clarified what being a data engineer entails. However, if you’re entirely new, you may wonder how it differs from other related fields. While data engineering often overlaps with other professions, they actually have distinct purposes within the data ecosystem:

• Data engineering:
• Focuses on building infrastructure to collect, process, and store data.
• Involves creating tools and platforms that other teams can use for analysis or modeling.
• Example deliverable: A data pipeline that ingests data from APIs and loads it into a Redshift data warehouse.
• Data science:
• Focuses on extracting insights from data using statistical analysis and machine learning.
• Relies heavily on pre-prepared datasets, often managed by data engineers.
• Example deliverable: A predictive model forecasting customer churn based on historical data.
• Data analysts:
• Primarily involved in interpreting and visualizing data for business decision-making.
• Example deliverable: Dashboards showing key business metrics over time.
• DevOps engineers:
• Overlap with data engineers in areas like system deployment and infrastructure maintenance.
• Primarily focusing on general application reliability, data engineers specialize in data-focused systems.

Skills Required to Become a Data Engineer

To excel as a data engineer, you need a mix of technical and soft skills. These skills enable you to build reliable data systems, solve complex problems, and collaborate effectively with teams.

Let’s break down the main skills you’ll need to learn or practice:

Programming skills

I’d argue this is the most important skill, as programming forms the foundation of data engineering, which is a special subset of software engineering.

Programming helps you manipulate data, automate tasks, and build robust systems. These are the top programming languages to focus on, in order:

1. Python: The go-to language for data engineers due to its simplicity and vast ecosystem of libraries like Pandas, NumPy, and PySpark. Python is used for scripting, data manipulation, and building pipelines.
2. SQL: A must-have skill for querying and managing data in relational databases. SQL is essential for writing efficient queries to extract and transform data.
3. Java/Scala: These languages are relevant when working with big data frameworks like Apache Spark, as they offer strong performance for handling large datasets.

If I were to start learning data engineering, I’d first focus on Python and SQL and then move into Java or Scala if needed.

To master Python for data engineering, explore the Data Engineer in Python Career Track, which provides a structured, hands-on learning experience.

If you're new to SQL, the SQL Fundamentals track is an excellent starting point for building a strong foundation.

Data modeling and database management

A strong understanding of databases and data modeling ensures that your data systems are efficient and scalable, which is a must for data engineers! 

Here’s what you need to know:

Relational databases

Relational databases like PostgreSQL, MySQL, and Microsoft SQL Server are the backbone of structured data storage. As a data engineer, you’ll need to:

• Design schemas that define how data is organized.
• Optimize queries for performance and efficiency.
• Understand indexing to speed up data retrieval.

For hands-on practice, check out the Creating PostgreSQL Databases course. If you’re new to Microsoft SQL Server, the Introduction to SQL Server course is a great resource to get started.

NoSQL databases

NoSQL systems like MongoDB and Cassandra are designed for unstructured or semi-structured data. They’re essential in scenarios where:

• Flexibility in schema design is important.
• Applications need to handle large volumes of data at scale, such as real-time analytics or social media data.

The NoSQL Concepts course is an excellent resource for learning the fundamentals and understanding where and how to use these powerful databases effectively.

Data warehouses

Data warehouses are specialized systems optimized for analytical queries and reporting. Tools like Snowflake, Amazon Redshift, and Google BigQuery are commonly used by data engineers to:

• Store and analyze large volumes of historical data.
• Aggregate data from multiple sources for business intelligence.
• Ensure fast query performance for complex analytics.

DataCamp provides courses on all of these data warehouses, as well as data warehousing in general, for you to get started:

• Introduction to Snowflake
• Introduction to Redshift
• Introduction to BigQuery

Data lakes

Data lakes, such as those built on Amazon S3, Azure Data Lake, or Google Cloud Storage, are designed for storing raw, unprocessed data. Unlike data warehouses, data lakes handle both structured and unstructured data, making them ideal for:

• Storing large datasets for machine learning or AI applications.
• Supporting use cases like log storage, IoT data, and streaming data.

ETL processes

As I mentioned before, designing and managing data pipelines is one of the primary responsibilities of a data engineer. So, you need to be familiar with the following processes:

• Extract: Gathering data from various sources such as APIs, databases, or logs.
• Transform: Cleaning and reshaping data to fit the required format or schema.
• Load: Storing the processed data into data warehouses or lakes for further use.

Tools like Apache Airflow and dbt simplify the orchestration of ETL workflows.

Check out the ETL in Python course for a great start! Then, continue with the Introduction to Airflow and Introduction to dbt courses.

Cloud platforms

Cloud computing is now the standard for storing and processing data due to its scalability and cost-effectiveness. So, familiarity with cloud computing is a must! 

Of course, you don’t need to know every service. Just focus on the ones that are relevant to data engineering. For example:

• AWS (Amazon Web Services): Data engineers widely use services like S3 (storage), Glue (ETL), and Redshift (data warehousing).
• Azure: Tools like Synapse Analytics and Data Factory are used to create and manage data workflows.
• Google Cloud Platform (GCP): BigQuery and Dataflow are standard large-scale data processing and analysis solutions.

Understanding how to deploy and manage data systems on these platforms is a must. Check out the Understanding Cloud Computing course for an excellent overview.

Big data technologies

With organizations dealing with massive volumes of data, familiarity with big data technologies is sometimes required. As this highly depends on your goals, I would list this as optional.

• Apache Spark: Known for its speed and versatility, Spark is used for distributed data processing and analytics.
• Kafka: A popular tool for real-time data streaming, Kafka enables you to process data as it’s generated, making it useful for applications like log analysis or user activity tracking.

At this stage, the Introduction to PySpark course is highly recommended. Then, you could continue with Introduction to Kafka to tackle real-time data challenges. 

Soft skills

While technical skills are essential, soft skills are necessary for success in team environments and problem-solving scenarios. Obviously, this is not specific to data engineering, but I believe it’s worth mentioning it in this context:

• Problem-solving: You’ll often face system failures, data discrepancies, or performance bottlenecks. The ability to analyze and resolve these quickly is crucial.
• Collaboration: As mentioned before, data engineers work closely with data scientists, analysts, and other teams. Clear communication and the ability to align on goals make you a valuable team member.
• Communication: Explaining technical processes to non-technical stakeholders is often part of the job. Being able to present ideas clearly can drive better decision-making.

How to Learn Data Engineering: Step-by-Step Roadmap

If your goal is to learn data engineering from scratch, and you don’t have a background in a related field, the following roadmap is for you!

You would be more than ready to start applying to data engineering roles in 12 months (or less, depending on your level of commitment).

Transitioning from a related role

If you’re already working in a related field like software development, data analysis, or DevOps, transitioning to data engineering can be smoother. That was exactly my case. Here’s how:

1. Software developers:
• Leverage your programming experience to learn Python and SQL.
• Focus on building ETL pipelines and exploring big data tools.
• Transition your knowledge of system design to scalable data systems.
2. Data analysts:
• Deepen your knowledge of SQL and database optimization.
• Learn Python for automation and data transformation.
• Transition into building pipelines and exploring big data concepts.
3. DevOps engineers:
• Use your experience in system deployment to work with cloud platforms.
• Learn ETL tools and focus on data orchestration.
• Transition your infrastructure knowledge to data-focused workflows.

Pro tip: Highlight transferable skills on your resume, such as experience with cloud platforms, programming, or analytics. It can certainly be an advantage!

Top Resources for Learning Data Engineering

Okay, now you have a specific roadmap, but to become a skilled data engineer, it's essential to utilize high-quality learning materials that provide both theoretical knowledge and practical experience.

Below is a curated list of books, courses, certifications, and community resources to guide you:

Books

Books offer a deep understanding of data engineering concepts and best practices.

• "Designing Data-Intensive Applications" by Martin Kleppmann: This foundational book explains the principles behind modern data systems, covering distributed systems, data modeling, and stream processing.
• "The Data Warehouse Toolkit" by Ralph Kimball: A detailed guide to designing effective data warehouses and ETL systems.
• "Fundamentals of Data Engineering" by Joe Reis and Matt Housley: This book, perfect for beginners, covers the tools, skills, and concepts you need to start your data engineering journey.

Courses

Practical, hands-on courses are essential for mastering tools and technologies. I’ve listed a few courses throughout this blog post, but if you’re unsure where to start, here are great options:

• Understanding Data Engineering: An introductory course explaining the fundamentals of data engineering, including pipelines and ETL.
• Data Engineer in Python Career Track: A comprehensive program to master Python, SQL, and essential tools like Apache Airflow and Git.

Guided projects

After completing a few courses, apply your knowledge with hands-on projects. Here are a few guided ones, which include datasets and step-by-step instructions:

• Building a Retail Data Pipeline
• Performing a Code Review
• Cleaning an Orders Dataset with PySpark

Certifications

Certifications validate your knowledge and increase your employability. Consider the following ones once you’re ready:

• DataCamp Data Engineer Certification: Comprehensive Python, SQL, and data pipeline management certification.
• AWS Certified Data Engineer - Associate: Validates skills and knowledge in core data-related AWS services.
• Google Professional Data Engineer: Validates skills in designing and managing data systems on Google Cloud.
• Microsoft Azure Data Engineer Associate: Focuses on building data solutions on Azure.
• Databricks Certified Data Engineer Associate: Ideal for Spark and Databricks environments.

Common Mistakes to Avoid When Learning Data Engineering

As you work toward becoming a data engineer, it’s easy to fall into certain traps that can slow your progress or limit your career potential. 

Here are some common mistakes to watch out for—and tips on how to avoid them.

1. Overfocusing on theory without hands-on practice

Data engineering is a highly practical field! While it’s important to understand the underlying concepts, real-world success depends on your ability to apply that knowledge.

What happens:

• You may spend too much time reading books or taking courses without building actual projects.
• Employers often prioritize candidates who can demonstrate practical experience over theoretical knowledge.

How to avoid It:

• Pair every new concept you learn with a small project to apply it. For example, after learning about ETL, create a pipeline to process and store data from a public API.
• Participate in open-source projects or Kaggle competitions that require data engineering skills.
• Use tools like GitHub to showcase your hands-on projects to potential employers.

2. Ignoring the importance of soft skills

Data engineers don’t work in isolation. You’ll collaborate with data scientists, analysts, software developers, and business teams, making soft skills as important as technical expertise.

What happens:

• Poor communication or collaboration can lead to misaligned goals and inefficient workflows.
• You may struggle to explain your work to non-technical stakeholders, hindering your impact.

How to avoid It:

• Practice explaining technical concepts in simple terms, especially to non-technical audiences.
• Develop teamwork and active listening skills by collaborating on group projects.

3. Using outdated tools and technologies

The tech landscape is constantly evolving, and data engineering is no exception! Relying on outdated tools can make you less competitive in the job market and limit your ability to build scalable, modern systems.

What happens:

• You may focus on legacy tools like MapReduce when more efficient alternatives like Apache Spark are widely adopted.
• Employers expect familiarity with modern cloud platforms like AWS, Azure, and GCP; ignoring these can make you less relevant.

How to avoid It:

• Stay updated with industry trends by following blogs, newsletters, and communities.
• Regularly explore and experiment with new tools and frameworks. For example:
• Learn dbt for data transformation instead of relying solely on SQL scripts.
• Use Apache Airflow for workflow orchestration instead of manual scheduling.
• Include a “tools and technologies” section on your resume to highlight your familiarity with the latest industry standards.

Conclusion

Becoming a data engineer is an exciting journey that combines technical expertise, creativity, and problem-solving. You can thrive in this dynamic field by following a structured learning path, avoiding common mistakes, and continuously building your skills. 

Here’s a quick recap of the roadmap I proposed:

1. Build a strong foundation in programming and databases.
2. Master ETL processes, data pipelines, and cloud computing.
3. Dive into big data technologies and real-time processing tools (only of that’s part of your specific goals).
4. Apply your knowledge through portfolio projects that showcase your expertise.

Remember, success in data engineering is not just about knowing the right tools and technologies—it’s about applying them to solve real-world problems. Stay consistent, seek hands-on experience, and keep up with the latest trends.

To support your journey, consider these resources from DataCamp:

• Data Engineer Certification: Validate your skills and showcase your expertise with an industry-recognized certification.
• Data Engineer in Python Career Track: Learn Python, SQL, and essential tools like Apache Airflow and dbt in a structured, hands-on program.
• Understanding Data Engineering: A beginner-friendly course introducing key concepts like ETL, pipelines, and cloud integration.