Data Centralization and Analytics using AWS
About The Client
The client is one of the largest B2B e-commerce platforms, serving the sales and distribution of consumer products from fast-moving consumer goods brands to retailers.
The Business Need
- The company wanted to provide more value to its customers on its platform, by offering them insights gathered from sales patterns and consumption trends to uncover potential sales opportunities. They were looking for high performance and scalable cloud solutions to integrate their scattered enterprise data from multiple historical and streaming sources – transactional, e-commerce, and back-office systems for analysis.
- More specifically, it could not provide the company with a real-time view of product movement and sales, which made it difficult for the company to make informed and timely decision-making corresponding to user demands.
- The company also had challenges related to cloud migration and legacy infrastructure which was restricting the company from unlocking new business opportunities. However, their internal engineering team knew that scaling in the available timeline would be challenging, so it sought a technology partner to augment the skills gap.
- To resolve these challenges, Scalex decided to build a data lake solution on AWS. This allowed us to aggregate data from various data silos into Amazon S3 where we cataloged and secured all data using AWS Lake Formation, dramatically reducing the operational load.
- Scalex used S3 as the data lake storage layer into which raw data is streamed via Kinesis. AWS Lambda functions are written in Python to process the data, which is then queried via a distributed engine and finally visualized in a BI tool.
- The company hosts its data lake and analytics platform on AWS, allowing its analysts to perform discovery on the deluge of data. The company centralizes all its data in Amazon S3 and uses analytics services such as Amazon EMR and Amazon Redshift, which enable its data scientists to apply machine learning techniques to test new patterns and use advanced analytics.
- Taking advantage of its new AWS data lake solution, the company is now able to analyze the huge volumes of data from its transactional, e-commerce, and back-office systems, and make this data available to its team immediately for analytics.
- By leveraging the power of data lake on AWS, the company saw a 50% infrastructure cost reduction and improved application performance mainly because of the flexibility of archiving data on S3.
- By acting on insights provided by real-time data can be used to identify sales opportunities such as cross-selling or upselling, make sales predictions, experience higher speed to market, and sales turnovers.
- With the capabilities of the new data infrastructure, the client’s team has been able to uncover insights that were not easily observed and make data-driven decisions to scale the reach of the company.
Data Centralization And Analytics Using AWS
February 28, 2020
3690 Brownton Road, Mississippi