Implemented a distributed data lake architecture and advanced analytics on the AWS cloud platform to reduce IT costs and improve productivity
Revolutionizing Pharma Analytics with AWS Data Lake
About the Client
The client is a multinational pharmaceutical company based in India. Moreover, the client deals in manufacturing and selling active pharmaceutical ingredients and pharmaceutical formulations in India and the US. Considered as one of the most reputed brands in India, the client has expanded with joint ventures and acquisitions in the last two decades.
The Business Challenge
The client wanted to improve and accelerate analytics-driven decisions and reduce the time for data analysis, data analytics, and data reporting on both structured and unstructured data. Furthermore, the client wanted to improve the deviation tracking of mitigation tasks and reduce the system stack cost by enabling an open-source, industrial-grade platform. In addition, the client also wanted to prepare ground and infra for AI/ML and advanced analytics.
Our Solution
We built enterprise data lake architecture and implemented analytics on the AWS platform. Specifically, this solution included AWS data lake architecture for scalable warehouse and AWS data lake architecture for IoT and unstructured data.
The Impact Aptus Data Labs Made
The enterprise data lake architecture on the AWS platform enabled the client to process, analyze, and report both structured and unstructured data quickly with better analytics-driven decisions. Additionally, this solution helped the client to reduce IT costs and improve business performance.
The Business and Technology Approach
Aptus Data Labs used the following process to build enterprise data lake architecture for scalable warehouse and for IoT and unstructured data to resolve the business challenge. The solution was in three stages.
- Carried out a detailed requirement and due diligence study
- Understood the client’s technology stack, infrastructure availability & business operation landscape
- Recommended AWS infrastructure/instance, and AWS services considering scalability, performance, and cost
- Created strategies for data migrations and AI/ML business use
- Installed, configured, and tested the instances & services
- Tested the deliverables platform and automated the process
- Followed the PMBOK project management process and CRISP-DM process for the data analytics solution
The Reference Architecture We Built
Tools Used
- AWS S3
- AWS RDS
- AWS Glacier
- AWS Lambda
- AWS Glue, ETL, and Data Catalog
- AWS CloudWatch
- AWS CloudTrail
- AWS DynamoDB
- AWS Quick sight
- Amazon DMS
- Amazon Kinesis
- PostgreSQL
- R
- Python
- Superset
- DbVis Software
The Outcome
The new data architecture based on AWS Cloud benefited the client in multiple ways and helped to resolve the business challenge. The benefits in all the three phases were:
- Advanced analytical capabilities-driven on both structured and unstructured data with Enterprise search enabled for any data
- Machine Learning used to drive improvements and productivity
- Demonstrated connectivity to various databases from Presto
- Backed up email and uploaded data to the cloud
- Uploaded IoT data to the cloud
- Established connectivity from R/Python to Cloud Database/S3 using Libraries
- Enabled Presto/AWS Athena for data search or ad-hoc queries
- Migrated Tableau dashboard to Superset or AWS Quicksights or D3.J3
Related Case Studies
Achieving low-latency API-based queries with Mongo DB
Revolutionizing Pharma Analytics with AWS Data Lake
Boosting Performance with Apache Spark Migration
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