5 Free AWS Data Engineering Projects for Beginners

When you’re starting out in data engineering, diving into hands-on projects can make all the difference. They bridge the gap between learning theory and applying real-world skills—especially when working with AWS, which dominates the industry for cloud-based data solutions. For beginners, picking the right projects can help you explore essential data workflows and tools without feeling overwhelmed.

If you’re unsure where to begin, platforms like Data Engineer Academy offer trusted guidance to jumpstart your learning. Whether you’re building data pipelines or processing large datasets, these free projects will give you the practical experience you need to grow.

Introduction to AWS Data Engineering

AWS is a leader in cloud computing, and its ecosystem of tools has made it a go-to platform for data engineers worldwide. Whether you’re just starting out or looking to strengthen your skills, understanding the role of AWS in data engineering can set the foundation for building scalable, efficient data solutions. Let’s break this down into why AWS matters and the tools you need to know.

Why AWS for Data Engineering?

AWS dominates the cloud market, and for good reason. It offers scalability, cost efficiency, and a portfolio of more than 200 services to handle everything from storage to machine learning. But what makes AWS a favorite among data engineers?

1. Accessibility: AWS provides a diverse range of services tailored for various data engineering tasks, and many of these tools feature free-tier options to help you get started.
2. Flexibility: With AWS, you can build data pipelines that adapt to changing data sources and needs, ensuring your workflows stay agile and effective.
3. Community Support: The sheer popularity of AWS means there’s no shortage of tutorials, forums, and courses available to guide you.

New to AWS? Learn more about the basics and how to get started with data engineering on AWS through this overview of AWS with our Data Engineering Course. It offers insights that can simplify your learning curve.

Key AWS Tools for Beginners

When starting in data engineering, the vast array of AWS tools can feel overwhelming. To help, here are three beginner-friendly options that you’ll likely encounter in most data workflows:

1. Amazon S3 (Simple Storage Service) Think of S3 as your cloud filing cabinet. It allows you to store, organize, and access data efficiently. Whether you’re hosting CSV files for analysis or massive datasets for machine learning, S3 is where most data engineers begin. The fact that it’s highly scalable and cost-efficient only adds to its appeal.
2. AWS Lambda Lambda is AWS’s serverless compute service, perfect for processing small-to-medium tasks like cleaning data or running scripts. Since it’s serverless, you don’t have to worry about managing infrastructure—you only pay for the compute time you use, making it a beginner-friendly way to automate tasks.
3. Amazon Redshift Need to analyze large datasets quickly? Redshift is AWS’s powerful data warehouse solution. It enables fast querying and is optimized for analytics-driven workflows. If you’re working with structured data, Redshift is your go-to.

By mastering these tools, you’ll cover critical aspects of data engineering, such as storage, automation, and analysis. For more beginner-focused AWS data engineering projects, this guide to data engineering for beginners can be a helpful resource.

AWS’s learning curve might seem steep at first, but with the right tools and guidance, progressing from a novice to a confident data engineer is entirely possible. Ready to dive deeper? Stay tuned for project ideas that will take your skills to the next level.

Project 1: Setting Up a Data Lake Using Amazon S3

Building a data lake is a foundational skill for any aspiring data engineer, and Amazon S3 offers one of the most straightforward ways to do it. A data lake enables you to store raw, structured, semi-structured, and unstructured data at scale, providing a centralized data repository. If you’ve been wanting to try your hand at end-to-end data engineering, setting up a data lake is the perfect place to start. Let’s walk through key steps and considerations.

Understanding Amazon S3’s Role in Data Lakes

Amazon S3 (Simple Storage Service) is more than a glorified cloud drive; it’s the backbone of many enterprise data lakes because of its scalability, durability, and cost-efficiency. Whether you’re storing terabytes or petabytes of data, S3 can handle it. But why is it ideal for a data lake?

1. Scalability Without Limits With S3, the storage capacity scales automatically. Whether you’re collecting IoT stream data or hosting public data sets for machine learning, you don’t have to worry about hitting capacity limits.
2. Durability and Availability Imagine storing everything in one place, knowing it’s backed by Amazon’s robust infrastructure with built-in redundancy. S3 guarantees 99.999999999% durability—basically meaning your data isn’t going anywhere without your say-so.
3. Cost-Effectiveness S3 offers multiple storage classes tailored to your data access needs (e.g., Glacier for archival storage), helping you minimize costs without sacrificing performance.

Want a deeper dive into foundational AWS technologies? Check out this comparison of AWS and Azure for data engineering.

Step-by-Step Guide to Setting Up Your Data Lake

Getting started with S3 is far easier than it sounds. Here are the main steps to follow so that you can create your own functional data lake.

1. Create an S3 Bucket

The entire data lake revolves around S3 buckets, which are like folders on steroids. Inside your AWS Management Console:

• Navigate to the S3 Dashboard.
• Click on “Create Bucket” and name it something meaningful (e.g., “my-data-lake”).
• Choose the region closest to your target users or applications for better performance.

2. Organize Your Data

Now that you have a bucket, you can think of it as your archive. Create sub-folders (referred to as prefixes in S3 lingo) to organize datasets:

• Raw data: All unprocessed files (e.g., sensor data, JSON logs).
• Processed data: Cleaner, manipulated datasets used for analytics.
• Curated data: Final datasets ready for reporting or sharing.

This structure makes it easier to scale while maintaining clear organization.

3. Use AWS Lake Formation to Secure Your Data

AWS simplifies permissions management and data cataloging with Lake Formation. Think of it as the project manager that helps you wrangle users, permissions, and metadata. Follow these core steps:

• Grant permissions for specific users or services.
• Use Lake Formation to catalog data for easy discovery later on.

Learn more about getting started with Lake Formation in this helpful AWS blog post.

4. Select a Storage Class

Not all data is created equal—some you’ll need access to daily, while other sets can sit untouched for months. Choose the storage class that matches your usage:

• Standard: For frequently accessed data.
• Infrequent Access (IA): Cheaper but incurs retrieval costs.
• Glacier: Ideal for long-term archive.

Best Tools to Enhance Your S3-Based Data Lake

To make your data lake functional and ready for analytics, you’ll need to integrate tools for processing, querying, and managing data efficiently. Here are some AWS companions that work hand-in-hand with S3:

1. AWS Glue: ETL tasks (extract, transform, load) take center stage here. You can automate data integration, clean raw datasets, and prepare it for downstream analytics quickly.
2. Amazon Athena: A serverless query service that lets you analyze your data directly in S3 using SQL. No infrastructure setup required—queries are pay-per-execution.
3. AWS Lambda: Automates repetitive operations like compressing incoming files or partitioning new data entries.

Read more about ingestion strategies and their related tools in this guide to data ingestion methods.

Resources to Help You Learn

Amazon S3 is packed with features, and mastering its capabilities can seem intimidating at first. However, AWS provides a wealth of documentation and free-tier offers to let you experiment without financial risks. For advanced details on S3 as a data lake backbone, refer to this AWS resource page.

By completing this project, you gain practical knowledge in core AWS services while sharpening essential data engineering skills. Plus, it’s a critical building block for more advanced pipelines and workflows you’ll encounter later.

Project 2: Building a Real-Time Data Pipeline with Kinesis

When you think about handling high-speed, continuously flowing streams of data, Amazon Kinesis is a service that immediately stands out. Whether you’re working with transaction logs, sensor data, or live user interactions, Kinesis enables you to capture, process, and analyze data in real time. It’s an integral skill for aspiring data engineers, especially as organizations increasingly rely on real-time insights for decision-making. Below, we break down how to set up a Kinesis stream and explore the diverse applications of this tool.

Steps to Set Up Kinesis Stream

Setting up a real-time data pipeline with Kinesis can seem daunting, but breaking it down into steps simplifies the process. Here’s what you need to do:

1. Create a Kinesis Stream
   • Start by opening the AWS Management Console and navigating to the Kinesis service.
   • Choose “Create Data Stream” and name your stream. The name should reflect its purpose (e.g., “user-clicks-stream”).
   • Define the number of shards based on your data ingestion needs. Shards determine data throughput, so you can scale these as needed.
2. Set Up a Producer
   • Producers are responsible for sending data to the Kinesis stream. This could be a cloud-based application, an IoT device, or an event-driven service.
   • Use the AWS SDK or the Kinesis Producer Library (KPL) to configure your producer application. These tools streamline data ingestion, automatically batching and retrying data when necessary.
   • A command-line interface (CLI) can also be used for quick tests by running sample commands to push data into the stream.
3. Integrate a Consumer
   • Consumers read data directly from the stream for further processing. One of the easiest consumers to set up is AWS Lambda, which triggers events whenever data enters the stream. This is ideal for real-time processing tasks like data transformation or storage into another service.
   • Other options include Kinesis Data Analytics for SQL-based processing or custom-built applications using the Kinesis Consumer Library (KCL).
4. Enable Lambda for Real-Time Processing
   • Navigate to the Lambda console and create a function.
   • Add a Kinesis trigger to this function by selecting your stream.
   • Write Python or Node.js code to process the incoming data (e.g., aggregating clickstream data or filtering out unwanted logs). Lambda automatically scales to handle incoming data, which is a huge time-saver.
5. Monitor and Scale as Needed
   • Once everything is configured, monitor throughput and latency using Amazon CloudWatch, which integrates seamlessly with Kinesis.
   • You may need to increase the number of shards for higher data throughput or optimize the consumer applications if you notice delays.

For more information on mastering AWS tools like Kinesis, check out this AWS Beginner Course, which provides an excellent foundation.

Real-Life Use Case Scenarios

Still wondering why you’d want to build a Kinesis pipeline? Let’s dig into some practical applications that showcase its power.

1. Fraud Detection in Banking Financial institutions deal with large-scale transactions every second. Kinesis allows banks to build pipelines that examine transaction patterns in real time. Using data analytics, suspicious activities are flagged instantly, preventing potential fraud.
2. Clickstream Analysis for E-Commerce Every click on a website or mobile app generates data. E-commerce platforms use Kinesis to capture this clickstream and analyze user behavior in real-time. This data feeds into recommendation systems, improving the user experience and driving higher sales.
3. IoT Sensor Monitoring In industries like manufacturing or logistics, IoT sensors send vast amounts of operational data constantly. Kinesis streams this data to central systems for live monitoring and predictive maintenance, reducing downtime and improving efficiency.
4. Video Streaming Analytics Platforms like live sports streaming services often leverage Kinesis to handle video data and associated metadata. This ensures a seamless user experience, minimizing lags or disruptions during high-demand periods.

Want to implement real-time insights with Kinesis pipelines? This step-by-step guide on AWS Kinesis patterns offers architectural best practices for building pipelines tailored to specific business needs.

Real-time data processing is no longer a niche skill in data engineering; it’s quickly becoming essential. By learning how to set up and utilize Kinesis, you’re positioning yourself to meet the growing demand for real-time analytics. Whether you’re processing millions of transactions or monitoring IoT devices, this AWS service is a versatile and scalable choice.

Project 3: Data Transformation and ETL with AWS Glue

AWS Glue is a fully managed service that provides data integration and ETL (Extract, Transform, Load) at scale. Whether you’re cleaning raw datasets or preparing it for analysis, Glue takes much of the heavy lifting off your shoulders. For beginners, understanding how AWS Glue crawlers and Python ETL scripts work can be a game-changer in streamlining your data engineering workflows. Let’s break this project into two key areas: configuring Glue Crawlers and writing scripts for ETL jobs in Python.

Understanding AWS Glue Crawlers

If you’re working with data stored in Amazon S3 and don’t want to manually define table metadata, Glue Crawlers simplify your life. Think of them as a GPS for your data—they scan datasets in S3, figure out their schema, and catalog that information in AWS Glue’s Data Catalog. Here’s how to configure them:

1. Set Up a Crawler in AWS Glue
   • Navigate to the Glue Dashboard in your AWS Console and select “Crawlers.”
   • Click “Add Crawler” and define a name that represents the data source you’ll be scanning. Naming conventions help you stay organized, so be specific.
2. Point the Crawler Toward Your Data
   • Choose S3 as your data source and specify the bucket or folder path where your files are stored. Crawlers work seamlessly with files like CSV, JSON, and Parquet.
   • If your data source is partitioned (e.g., split by date), Glue will automatically categorize the partitions for you.
3. Set Permissions with IAM Roles
   • Create or select an IAM role that allows Glue to access your S3 bucket. Be sure to follow the principle of least privilege—grant access only to what’s necessary.
4. Run the Crawler and Check the Data Catalog
   • Once configured, launch the Crawler. It will discover your data files, infer their schema, and generate table metadata in the AWS Data Catalog.
   • Head over to the catalog to confirm the process worked. You’ll see tables that align with your datasets, complete with schema details.

For a deeper dive into Glue Crawlers, check out AWS Documentation on using Crawlers. If mastering ETL tools excites you, you might also want to explore these top data pipeline tools.

Writing ETL Scripts in Python

AWS Glue supports serverless ETL, and writing Python-based scripts is a crucial skill to bring data into actionable formats. Not a Python expert yet? Don’t fret—the framework and structure are relatively beginner-friendly. Here’s a simple overview to get you started:

1. Access the Script Editor
   • Navigate to the Glue Jobs Dashboard and create a new job.
   • Choose “Spark” as the job type. AWS Glue uses an Apache Spark environment under the hood, so your scripts will run efficiently on large datasets.
2. Define the Job Logic Write your job in Python—a basic script looks like this:import sys from awsglue.transforms import * from awsglue.utils import getResolvedOptions from pyspark.context import SparkContext from awsglue.context import GlueContext from awsglue.job import Job # Initialize Glue job context glueContext = GlueContext(SparkContext.getOrCreate()) # Load source data from S3 source_data = glueContext.create_dynamic_frame.from_catalog( database="your_database_name", table_name="your_table_name" ) # Apply transformations (e.g., filter rows) transformed_data = Filter.apply( frame=source_data, f=lambda x: x["column_name"] == "desired_value" ) # Write transformed data back to S3 glueContext.write_dynamic_frame.from_options( frame=transformed_data, connection_type="s3", connection_options={"path": "s3://your-output-bucket"}, format="parquet" )
3. Customize Transformations Use built-in AWS Glue transforms (like Map, Filter, or Relationalize) to tailor your data. For instance, using Filter, you can remove unnecessary rows, or Map to perform column transformations.
4. Run and Test the Job
   • Test your code on small datasets before scaling. Once everything looks good, run it fully.
   • Monitor logs through Amazon CloudWatch for real-time job progress and troubleshooting.

For beginners, sample scripts often serve as invaluable guides. Amazon provides an excellent resource for Spark scripts programming to get you started.

To round out your knowledge, dive into AWS Glue’s ETL capabilities for a comprehensive understanding of its features and best practices.

AWS Glue combines simplicity with power, making it a go-to tool for those stepping into ETL or data integration for the first time. By mastering crawlers and scripting, you’ll build pipelines that effectively transform raw data into meaningful insights.

Project 4: Data Warehousing with Amazon Redshift

Amazon Redshift is one of AWS’s most powerful services when it comes to data warehousing. It’s designed to handle massive amounts of data, enabling lightning-fast query performance for reports, dashboards, and analytics. Beginners often find Redshift less intimidating than it looks because much of the heavy lifting is managed by AWS. In this project, you’ll learn how to optimize your queries and connect Redshift to BI tools for real-world applications.

Optimizing Redshift Queries

When working with Amazon Redshift, optimizing query performance is paramount. Poorly performing queries can lead to long wait times and higher costs, especially when dealing with large datasets. Luckily, Redshift comes packed with a range of features to keep your queries running smoothly.

• Sort Keys Sorting data is like arranging books in a library. By assigning sort keys, you ensure that frequently queried data is arranged efficiently, drastically cutting query times. Use CREATE TABLE statements to define your sort keys when setting up your tables.
• Distribution Styles Ever notice how splitting tasks in a team makes everything quicker? Redshift’s distribution styles work the same way. Choose between distribution styles like KEY, EVEN, or ALL based on your dataset size and processing needs. For instance:
  • Use KEY when tables share a commonly queried column.
  • Opt for EVEN distribution for uniform data spread.
  • Go with ALL when tables are small and frequently joined with other tables.
• Analyzing Queries Debugging slow queries doesn’t have to be a mystery. Redshift’s EXPLAIN command provides a visual breakdown of query execution. It shows where bottlenecks occur, allowing you to tweak your SQL statements or table configurations accordingly.

For additional tips, check out this top 10 performance tuning techniques for Amazon Redshift. It’s an indispensable resource for understanding advanced optimizations that beginners can grow into.

Connecting BI Tools to Redshift

Amazon Redshift is only as good as what you do with it, and one of the best ways to unlock its potential is by connecting it to business intelligence (BI) tools. You can turn raw data into actionable insights that help drive better decisions across industries.

Here’s how you can get started:

1. Set Up a Redshift Database Begin by provisioning a Redshift cluster in your AWS Console. Load sample datasets into your cluster to test its capabilities, such as weather data or sales numbers. AWS provides resources to help you set up Redshift for data warehousing.
2. Install and Configure BI Tools Popular BI tools like Tableau, Power BI, and Looker integrate seamlessly with Redshift. Install your chosen tool and connect it to Redshift by providing the cluster’s endpoint, database name, and credentials. For Tableau:
   • Open Tableau and choose “Amazon Redshift” as the data source.
   • Enter the connection details and sign in.
   • Select the schema and tables you need, then start building dashboards.
3. Enable Visualization and Reporting Once connected, you can drag and drop fields to generate charts, graphs, and detailed reports. This is where the magic happens! Use filters, calculated fields, and visualizations to explore trends and forecast outcomes.

By completing this project, you’ll learn how to set up a fully functioning data warehouse and empower decision-making through analytics and BI tools. Don’t forget to monitor cluster performance and tune queries regularly to keep everything running at peak efficiency. For further assistance with this, refer to query performance tuning techniques for Redshift.

Project 5: Serverless Data Engineering Workflow Using AWS Lambda

AWS Lambda is a game-changer in the world of serverless computing. It lets data engineers automate tasks without worrying about server management. Whether you’re processing events in real-time or setting up workflows, AWS Lambda ensures that everything runs smoothly and efficiently. What makes Lambda a favorite is how adaptable and cost-effective it is—perfect for engineers looking to streamline operations. Let’s break it down.

Conclusion

Starting with free AWS data engineering projects is one of the smartest moves you can make in building a solid foundation. Each project gives you hands-on experience with essential tools and workflows, preparing you for real-world challenges. Whether it’s creating data lakes, handling real-time streams, or automating workflows, these exercises will sharpen your problem-solving skills and boost your confidence.

Ready to explore even more? Check out free end-to-end projects to deepen your expertise. These curated challenges provide practical, real-life scenarios that can take your skills to the next level.

If you’re looking for personalized guidance to match your learning goals, Data Engineer Academy’s training is worth considering. The support and resources they offer ensure that you’re not navigating this journey alone.

Dive in, experiment, and transform your knowledge into action. There’s no better time to kickstart or advance your data engineering career!

Real stories of student success

▶

Student TRIPLES Salary with Data Engineer Academy

▶

DEA Testimonial – A Client’s Success Story at Data Engineer Academy