Modern Data Lakehouse Architecture for Unified Storage & Advanced Analytics

Client Overview

Our client is a well-known name in the healthcare industry. They mostly deal with respiratory care and breathing technology. Currently they have over 27,000 products for respiratory care, including ventilation and respiratory diagnostics. With over 8+ brands under their kitty, their products are available in more than 100+ countries and are used by 350,000+ people.

When we first connected, their long-term business goal was to improve patient outcomes and increase value for healthcare seekers. They believed that healthcare should be accessible by one and all. Since the company was quite well-known, they generated huge amounts of data which they stored in multiple databases. However, to serve their customers better, they needed predictive analysis solution that could help them predict future trends. Due to our well-known expertise in providing such solutions, they hired big data engineers from us.

Challenge

As we discussed, since our client was a known name in the healthcare sector, they generated vast amounts of data that were being stored all over. This obviously caused issues with their cross functional teams that required to access this regularly. Plus they couldn’t make sense of so much data and couldn’t predict future trends from it.

Our Solution

Initially different data sources were identified to ensure a single source of truth. A better segregated data lake house was created to store better info on product inventory , spare parts , sales etc. Hence ETL process was implemented using AWS Glue.

SQL queries were used to extract data, and it was then loaded into AWS S3 buckets. To store all extracted data, Amazon S3 buckets were used. To extract the data Amazon Redshift was used. Data became usable once they were cleansed and structured for analytics. Sagemaker was used for predictive analytics and a dashboard was created using AWS QuickSight for data visualisation.

Business Outcome

Our client truly benefited from our implementation. Now, they had a single source of truth for all their data issues, making it much more usable to their team. And implementation of predictive analytics helped generate insights for up to 18 months in advance. Our robust pipeline creation helped provide their team with usable analytics required for their business expansion and better healthcare offering.

Tech Stack