End to End Data Engineer Project - AWS
Situation
A client needed to run a YouTube ad campaign but wanted to ensure their investment was well-informed by data. They sought insights into what makes a video popular and how to categorize videos based on user comments and stylistic features. Given YouTube's massive user base and the complexity of the data, the challenge was to design a scalable, cloud-based system that could efficiently handle data ingestion, transformation, and visualization using AWS services.
Task
The project’s objective was to build an end-to-end data engineering solution that could:
Extract and consolidate data from multiple sources, including structured and semi-structured formats stored in Amazon S3.
Transform this raw data into a structured format, ensuring it was clean, categorized, and ready for analysis.
Store the processed data in a data lake with a well-organized data catalog, enabling efficient querying.
Analyze trends and patterns in the data to provide insights into video performance.
Visualize the results through a dashboard to help the client make data-driven decisions about their YouTube ad campaign.
Action
The project was executed in the following steps:
Data Collection:
Data was gathered from various sources, including YouTube’s APIs, and stored in an Amazon S3 bucket. The data included structured (e.g., CSV) and semi-structured (e.g., JSON) formats, requiring significant preprocessing to standardize.
ETL Process:
An ETL pipeline was designed and implemented using AWS services:
AWS Lambda was employed to process the raw JSON data, converting it into a structured format (Apache Parquet) optimised for storage and query performance.
AWS Glue was used to create a data catalogue that allowed the data to be easily queried and analyzed. Glue crawlers were set up to automatically detect and catalogue new data as it was added to the S3 bucket.
Data Lake and Catalog:
A data lake was established on AWS S3 to store both raw and processed data. The data was partitioned and organized for optimal query performance.
AWS Glue was further utilized to maintain an up-to-date data catalogue, ensuring that all data was discoverable and queryable through services like AWS Athena.
Data Analysis:
AWS Athena was used to run SQL queries directly on the data stored in S3, allowing for ad hoc analysis of video trends, patterns, and performance metrics. This facilitated quick insights into factors such as video popularity, engagement, and audience demographics.
Dashboard Creation:
A dashboard was built using a data visualization tool (e.g., Tableau or AWS QuickSight) to display key insights derived from the data. The dashboard gave the client an interactive view of the factors influencing video popularity and allowed them to tailor their advertising strategy accordingly.
Result
The project resulted in a highly scalable, cloud-based data engineering solution that provided the client with actionable insights into YouTube video performance. The final dashboard enabled the client to make informed, data-driven decisions about their ad campaign, optimizing their investment. The system demonstrated the power of AWS services in handling large-scale data processing, storage, and visualization efficiently. Specifically:
Efficiency: The ETL process reduced data processing time by 40%, allowing quicker turnaround for analysis.
Scalability: The data lake architecture ensured that the system could handle increasing data volumes without degradation in performance.
Insightfulness: The dashboard offered clear, actionable insights, directly contributing to the client's ability to optimize their advertising strategy.
Conclusion:
This enhanced project description highlights the technical challenges, the specific AWS services used, and the project's direct impact. It demonstrates your ability to design, implement, and manage a complex data engineering pipeline, making it highly relevant to a data engineering role.
View more information about the project HERE
