Case Study

AWS Data Lake Platform Implementation

A digital innovation company in APAC implemented an AWS data lake platform to modernize its data infrastructure, enabling efficient data management and enhanced data-driven decision-making.

A SaaS-based working capital solutions company modernized their data infrastructure using the AWS data lake platform for efficient data management and enhanced data-driven decision-making.

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Industry & Region: Technology Solutions, APAC

Technology Stack:
SQL Database Service: AWS Relational Database Service (RDS), Object Storage: Amazon S3, Serverless Computing Stack: AWS Glue ETL, AWS Glue Data Crawler, AWS Glue Data Catalog, Identity and Access Management (IAM), AWS Lambda, AWS Glue Databrew, AWS Glue Notebook, Machine Learning Platform: Amazon SageMaker, Encryption: AWS KMS, Data Warehouse: Amazon Redshift, Data Lake Service: AWS Lake Formation
Client Overview
The client is a technology solutions provider for the supply chain finance ecosystem in APAC. They facilitate faster “buy-side,” “sell-side,” and “bank solutions” for their clients’ financing requirements in their supply chain needs. The company aims to streamline the financial processes involved in the supply chain by leveraging innovative digital solutions.
Business Challenge
As the client’s customer base expanded, they started facing challenges with their existing data platform. It lacked the scalability and flexibility required to handle the increasing volume and variety of data coming from diverse sources. This led to inefficiencies in data management, making it difficult to derive meaningful insights for predictive analytics and reporting purposes. To address these issues, the client was looking for a modernized and centralized data platform that can seamlessly ingest, cleanse, transform, and standardize data from various sources.
Solution Offered
After studying the client’s requirements and challenges, our experts designed and implemented a robust and scalable data platform on the Amazon Web Services (AWS) cloud infrastructure. The solution follows the principles of the “AWS well-architected” framework to ensure that it is reliable, cost-effective, and highly available. Key components of the solution include:
1. AWS Data Lake Platform Architecture
This provides a centralized and scalable repository for storing raw data from diverse sources.
2. Data Ingestion from RDS to S3
Data from the client’s relational database system (RDS) gets ingested and stored in Amazon Simple Storage Service (S3). S3 provides highly durable and scalable storage, ideal for handling large volumes of data.
3. Data Transformation and Anonymization
Before storing the data in the data lake, it gets transformed, cleansed, and anonymized to ensure data privacy and compliance with regulations.
4. Data Zone Segmentation
The data lake gets segmented into different zones, such as Raw, Cleansed, and Curated zones. This segregation ensures that data is managed at different levels of processing, making it easily accessible to different user groups.
5. Data Marts
Data stored in the curated zone is made available to data analysts for further analysis and reporting purposes through data marts established to support specific analytical needs. This empowers the analysts to derive valuable insights and make data-driven decisions.
6. SageMaker Setup for Data Analysts
Amazon SageMaker, a fully managed service for machine learning, further enables data analysts to leverage the data from the data lake for predictive analytics and other machine learning tasks. It provides a scalable and collaborative environment for data exploration and model development.
Value Delivered
  • Scalable AWS infrastructure to accommodate growing data volumes and future business needs.
  • Efficient data management – from ingestion to storage and analysis.
  • Reduced data silos and improved data quality.
  • A cost-effective serverless and decoupled architecture.
  • Accelerated analytics with Amazon SageMaker setup.

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AWS Data Lake Platform Implementation