Introduction
Sports fans buy jerseys, caps, and collectibles every day. Our client runs one of the largest sports merchandise businesses. They manage thousands of licensed products across online and offline stores.
Every sale created data. Every shipment added more. Soon, they had 100TB of data from five systems. Their SQL setup could not handle it. Reports were slow. Analytics were poor. Decisions lagged.
They needed a new way to store and use data.
The Client
The client had licenses for thousands of sports items. They sold online and through physical outlets. Orders, stock moves, and sales created massive records.
But their SQL storage was stretched. It could not scale. Queries were slow. Data was scattered. They wanted a solution that stored everything in one place.
The Challenge
The job was clear:
- Move 100TB of raw data from five systems.
- Store it in a secure cloud.
- Make it ready for analysis.
Their old ETL pipeline with SSIS was rigid. It slowed down transfers. It lacked strong data checks. The client wanted speed, security, and easier access.
The Solution: AWS Data Lake
Teleglobal chose AWS Data Lake services. It was the best fit for three reasons:
- Amazon S3 gave secure and cheap storage.
- It could hold raw data in native format.
- It connected easily to AWS analytics tools like AWS Redshift data lake, Spark, and Hadoop.
This created a strong AWS Data Lake architecture. The client could store raw data first, then structure it later.
Migration Steps
- Moving Data
We set up pipelines to move raw data from on-prem servers to Amazon S3 data lake.
- Security
All transfers were encrypted with SSL. Checks confirmed accuracy at each stage.
- Automation
We enabled monitoring for audits and compliance.
- Integration
Data in S3 was ready for analysis with AWS Redshift and other tools.
What is AWS Data Lake?
An AWS Data Lake is a central store for all business data. It keeps structured and unstructured data in its native format.
This means companies don’t need to reshape data before storage. They can extract, clean, and use it later with data extraction tools and ETL pipelines.
For this client, it allowed sales, stock, and customer records to sit together in one secure place.
Does AWS Have a Data Lakehouse?
Yes. AWS supports a data lakehouse model. It combines the flexibility of a data lake with the structure of a warehouse.
Using AWS Redshift data lake integration, companies can query both structured and raw data. This setup makes analytics faster and easier.
Our client used this model to track sales patterns and inventory needs more accurately.
Results and Benefits
The move to AWS Data Lake delivered clear results:
- Safe migration of 100TB of data.
- Report speed improved by over 60%.
- One store for all sales, orders, and inventory records.
- Scalable system ready for future growth.
- Strong encryption and compliance monitoring.
The client now runs analytics in hours instead of days. Their teams act faster with accurate data.
Example
Before:
- Order data sat in silos.
- Reports took days to prepare.
- Data errors slowed decisions.
After AWS migration:
- All order data flows into Amazon S3.
- Teams query it with AWS Redshift data lake.
- Reports are ready in hours.
This gave leaders real-time insight into demand.
Future Scope
The client can now:
- Use machine learning on AWS for demand forecasting.
- Add real-time streaming with Amazon Kinesis.
- Expand analytics dashboards with Redshift.
The AWS Data Lake architecture makes these steps simple to add.
Conclusion
This case study shows how AWS Data Lake services help large businesses handle massive data.
Teleglobal migrated 100TB of raw data to a secure Amazon S3 data lake. We set up integration with AWS Redshift data lake for faster analytics.
The client now has a flexible, secure, and scalable system. They are ready to use data for stronger decisions and long-term growth.