In today's data-driven world, organisations deal with massive volumes of structured and unstructured data coming from various sources. To manage, store, and analyse this diverse data efficiently, Data Lakes have emerged as a flexible and scalable solution.
A Data Lake is a centralised repository that allows you to store all your data — structured, semi-structured, and unstructured — at any scale. You can store your data as-is, without having to structure it first, and run different types of analytics — from dashboards and visualisations to big data processing, real-time analytics, and machine learning — to guide better decisions.
While traditional data lakes often require managing servers, storage, and complex pipelines, a Serverless Data Lake takes this a step further by removing the need to manage infrastructure.
In this project, I have implemented a Serverless Data Lake architecture using various AWS services through one of AWS's official workshops.
The workflow includes:
- Data Ingestion
- Data Transformation
- Data Visualization
All of this was accomplished without provisioning or managing any servers.
This end-to-end solution was built using the following AWS tools:
- AWS CloudFormation
- Amazon S3
- AWS Glue
- Amazon Athena
- Amazon QuickSight
📖 For detailed steps and insights, check out my blog on Medium:
👉 Read the full article on Medium
- AWS S3 – Data storage
- AWS Glue – Data transformation
- Amazon Athena – Querying data
- Amazon QuickSight – Data visualization
- CloudFormation – Infrastructure as code
For any queries or suggestions, feel free to reach out!