1. Overview (Project Background)

This project was pursued with the goal of building a cloud-based data analytics environment,
establishing an analytics platform capable of large-scale data processing and machine learning and AI utilization.

Data Lake data distributed across on-premises environments was migrated to the cloud according to analysis purposes,
aiming to provide a more flexible analytics environment by improving efficiency across data collection, processing, and management.

Additionally, the focus was placed on building a system that enables data analytics experts to autonomously explore, analyze, and apply data,

2. Solution (Resolution Approach)

Data integration and analytics capability internalization were carried out in phases centered on a cloud-based analytics platform.

• Cloud-based analytics platform configuration and operation

• Establishment of standardized data integration and management environment

• Internalization of cloud capabilities for data utilization and analytics

3. Result (Achievements)

Secured analytics infrastructure with scalability and stability

• Cloud infrastructure configuration with high durability and availability

• Storage cost optimization through Intelligent Tiering application

• Support for diverse data collection methods and utilization of a wide range of analytics tools

Data utilization environment optimized for analytics purposes

• Databricks-based DW configuration

• Data access control and permission management through DP360 web portal

• Provision of analyst-level analytics environment and easy system management

• Improved convenience in adding required data and management

Strengthened data-driven business capabilities through technology internalization

• Improved analytics capabilities through Databricks and AWS training

• Improved work efficiency through acquisition of latest analytics technologies

• Established foundation for continuous advancement of data analytics capabilities

Achieved balance between performance and cost

• Secured cost efficiency through separated storage and computing structure

• Minimized unnecessary costs through usage-based billing

• Provided continuous cost optimization reports through billing solutions

• Blocked unnecessary resource usage through DP360 solution utilization

Expected Effects

• Secured data platform that simultaneously meets integration, scalability, and cost optimization

• Minimized initial investment costs and optimized operational costs

• Established Data Lake-based DW analytics environment

• Established high-quality data management system based on Databricks Delta Lake

• Strengthened data governance system

ML Ops automation and technology internalization

• Configured ML Ops automation environment based on AWS SageMaker

• Provided ML Ops pipeline templates

• Conducted technology internalization training for internal personnel