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.

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

Additionally, we focused on building a system that secures both user-driven analytics environments and control and availability of data usage,
enabling data analytics experts to autonomously explore, analyze, and apply data.

2. Solution (Resolution Approach)

We progressively proceeded with data integration and internalization of analytics capabilities centered on a cloud-based analytics platform.

• Configuration and operation of cloud-based analytics platform

• Establishment of standardized data integration and management environment

• Internalization of cloud capabilities for data utilization and analytics

3. Result (Achievements)

Securing analytics infrastructure with scalability and stability

• Configuration of cloud infrastructure 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

• DW configuration based on Databricks

• 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

Strengthening data-driven work capabilities through technology internalization

• Improved analytics capabilities through Databricks and AWS training

• Improved work efficiency through acquisition of latest analytics technologies

• Establishment of foundation for continuous advancement of data analytics capabilities

Securing balance between performance and cost

• Cost efficiency secured through separated storage and computing structure

• Minimization of unnecessary costs through usage-based billing

• Provision of continuous cost optimization reports through billing solutions

• Prevention of unnecessary resource usage through DP360 solution utilization

Expected Effects

• Securing a data platform that simultaneously satisfies integration, scalability, and cost optimization

• Minimization of initial investment costs and optimization of operational costs

• Construction of DW analytics environment based on Data Lake

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

• Strengthening of data governance system

ML Ops automation and technology internalization

• Configuration of ML Ops automation environment based on AWS SageMaker

• Provision of ML Ops pipeline templates

• Conducting technology internalization training for internal personnel