Data engineering portfolio projects using AWS

In the competitive world of data engineering, having a strong portfolio is key to standing out to employers and clients. While certifications and theoretical knowledge are important, hands-on projects demonstrate your ability to solve real-world problems using scalable cloud solutions. Amazon Web Services (AWS), the industry leader in cloud computing, offers a robust set of tools for building professional-grade data engineering pipelines. In this blog, we’ll explore the top data engineering portfolio projects using AWS that can help you land your next role or freelance gig.


1. End-to-End ETL Pipeline with AWS Glue and S3

Objective: Build a serverless ETL (Extract, Transform, Load) pipeline.

Tools Used: AWS S3, AWS Glue, AWS Lambda, Athena

Process: Ingest raw CSV/JSON data into S3, transform it using Glue (PySpark), and store the cleaned data in a new S3 bucket or partition.

Bonus: Query the data using Athena and visualize with Amazon QuickSight.

What You'll Learn:

Serverless data transformation

Data cataloging

Schema evolution handling

This project is foundational and highlights your ability to automate and scale ETL operations.


2. Real-Time Data Streaming with Kinesis

Objective: Capture and analyze streaming data in real time.

Tools Used: Amazon Kinesis Data Streams, Kinesis Firehose, Lambda, Redshift or S3

Process: Simulate real-time data (like IoT sensor data or clickstream logs), stream it to Kinesis, use Lambda to process it, and load into Redshift or S3.

What You'll Learn:

Event-driven architecture

Real-time analytics setup

Integrating Lambda for on-the-fly processing

This project demonstrates your skills in building real-time, scalable pipelines—a must-have for modern data applications.


3. Data Lakehouse Architecture with AWS

Objective: Build a scalable Lakehouse combining the best of data lakes and warehouses.

Tools Used: AWS S3, Lake Formation, Glue, Athena, Redshift Spectrum

Process: Store raw data in S3, govern access with Lake Formation, transform with Glue, and query via Athena or Redshift Spectrum.

What You'll Learn:

Lakehouse principles

Data governance

Hybrid analytics

This project reflects your understanding of modern data architectures and how to apply them in real-world scenarios.


4. Batch Processing Workflow with EMR and Apache Spark

Objective: Perform large-scale batch data processing.

Tools Used: Amazon EMR, Spark, S3, Step Functions

Process: Upload large data files to S3, use EMR to run Spark jobs, and automate the pipeline using Step Functions or CloudWatch triggers.

What You'll Learn:

Big data processing with Spark

Cost optimization using spot instances

Orchestration of batch jobs

This project proves your ability to handle high-volume datasets efficiently and cost-effectively.


5. Data Pipeline Monitoring and Logging

Objective: Set up observability for your data pipelines.

Tools Used: CloudWatch, SNS, Lambda, S3

Process: Monitor logs and metrics from Glue, EMR, or Kinesis; send alerts on failures or anomalies.

What You'll Learn:

Logging best practices

Error detection and alerting

Maintaining pipeline reliability

Including observability in your projects shows maturity and readiness for production environments.


Final Thoughts

A well-rounded data engineering portfolio using AWS should demonstrate your command over core concepts like data ingestion, transformation, storage, streaming, and monitoring. Each project listed here not only builds your technical skills but also aligns with industry best practices.

To stand out, host your code on GitHub, document your process in blog posts or Medium articles, and include architectural diagrams. Bonus points for deploying projects using Terraform or AWS CDK!


Learn AWS Data Engineer Training

Read More: Running Spark ML models on Amazon EMR
Read More: Using AWS Secrets Manager in data pipelines
Read More: Working with AWS Glue job bookmarks in PySpark


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