In the retail sector, where quick and informed decision-making is critical for success, leveraging data has become a key factor in leading the market. One of the most effective strategies for managing large volumes of information and maximising its value is implementing a Data Lake. In this project, we built one on AWS using Amazon S3 and Athena, enabling a major retail company to centralise its information, enhance its analytics, and generate key KPIs to support decision-making.
Our expertise played a pivotal role in this transformation. As specialists in data processing and analysis with AWS tools, we not only implemented the Data Lake architecture but also optimised every stage of the process. This ensured efficient and secure data management, fully tailored to the business’s needs. Thanks to the hyper-specialised team at Daus Data, we helped our client maximise the value of their data infrastructure. Below, we explain in detail how we achieved this.
The Initial Challenge: Migrating Data to AWS Cloud
One of the initial challenges was migrating data from Google Analytics to Amazon S3, a task Daus Data managed using AWS AppFlow. This service automated the secure and efficient transfer of large volumes of data, facilitating seamless integration between Google Analytics and AWS services.
With AppFlow and the precise configuration managed by Daus Data, we eliminated data silos. All information was consolidated into a centralised Data Lake, providing a unified, up-to-date view for the entire team, improving collaboration and decision-making.
Amazon S3 and Athena: The Heart of the Data Lake
At the core of the Data Lake is Amazon S3, which acts as the primary repository for the retail company. Its unlimited storage capacity and scalability make it the ideal choice for centralising everything from sales data to performance metrics and user logs.
To enable efficient analysis of this vast amount of data, Daus Data implemented Amazon Athena. This tool allows SQL queries to be run directly on the data stored in S3 without the need to move it, which is crucial for generating real-time KPIs. With this solution, the retail team can now monitor performance and obtain insights quickly and cost-effectively, optimising decision-making processes.
Ensuring a Flexible and Efficient ETL Process with Glue and Lambda
The ETL (Extract, Transform, Load) process was managed by our Daus team using a hybrid strategy that combined the power of AWS Glue and AWS Lambda:
- AWS Glue, handled the more complex transformations, enabling the processing of large data volumes and turning them into analysis-ready information.
- AWS Lambda, was used for lighter ETL tasks, such as quick data fixes and triggering low-volume load processes, maximising efficiency without requiring additional infrastructure.
This hybrid ETL strategy, fully designed by Daus Data, provided flexibility and scalability, adapting to the client’s diverse data needs.
Seamless Orchestration with Step Functions
To coordinate all the ETL processes and workflows, we implemented AWS Step Functions, a service that smoothly orchestrates multiple AWS services. This automation of workflows allowed the extraction, transformation, and loading processes to run continuously and error-free, effectively integrating Glue, Lambda, and other services into a unified pipeline.
Accurate KPIs, Informed Decisions, and Impactful Results
Once the data was migrated, transformed, and stored in the Data Lake, the retail company’s team, with support from Daus Data, began generating key KPIs for their business. Among the most important metrics were:
- User behaviour in the online store.
- Conversion rates and marketing campaign performance.
- Real-time inventory management.
- Sales trend analysis and category performance.
These metrics provided unprecedented visibility into the company’s operations, enabling more informed decision-making and improving both efficiency and performance.
Daus Data and AWS: Transforming Retail Analytics from Every Angle
The implementation of a Data Lake on AWS, with our expert support and services such as S3, Athena, Glue, Lambda, and Step Functions, has revolutionised the way this retail company manages and analyses its data. The migration from Google Analytics via AppFlow and the ability to centralise and query all information in one place have enhanced analytical efficiency and empowered the team with the tools needed to optimise performance.
If your company is seeking a modern, scalable solution for data management, the combination of AWS infrastructure and Daus Data’s specialised knowledge is a proven route to more agile and powerful analytics, capable of completely transforming your operations. We’re here to help you take that step.