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Bigdata Platform
Big Data Platform Trend

Big data platforms focus on deriving deep insights from data by integrating Artificial Intelligence (AI) and Machine Learning (ML) technologies. This predictive analytics and automated decision-making processes. As the importance of data governance and security grows, technologies and processes for data quality management, regulatory compliance, and data privacy protection are being strengthened. The increasing demand for real-time data processing and analysis emphasizes the importance of streaming data technologies, enabling businesses to make real-time decisions and swiftly adapt to dynamic environments.

New Business Insights, Gen-AI Ready Platforms, EDP
(Enterprise Data Platform)
Unified Data Platform

A unified big data platform maximizes efficiency and productivity by managing the collection, storage, processing, and analysis of data in one environment. It enhances consistency and eliminates redundancy, improving overall quality while delivering valuable business insights through real-time analytics and predictive modeling. The platform also enhances data accessibility across various departments and teams, promoting collaboration and reducing operational complexity to lower costs. Its scalability enables flexible adaptation to changing data requirements, offering optimal performance by combining cloud and on-premises environments. This maximizes the value of data utilization and supports strategic decision-making.

Optimal Value Creation through Generative AI

Generative AI plays a crucial role in big data platforms by generating new content or insights based on datasets. It maximizes data utilization and provides value across various areas, including predictive modeling, personalized services, and content creation. By integrating real-time data analysis with AI model training, it enables the development of innovative products and services. Big data platforms provide the foundation for generative AI to efficiently handle large-scale data training and model operations, enhancing a company's competitive edge.

LG CNS EDP Services

Current Infrastructure Assessment & Optimal Architecture Design

LG CNS EDP service, powered by a team of big data experts and engineers, analyzes your existing infrastructure and services to propose an optimal architecture for a unified big data platform while ensuring seamless data governance for integrated management.

Analytics Services through Customized Consulting Based on Client Data

As Korea's No. 1 AI company, LG CNS unites top experts across industries to discover new analytics tasks and provide custom data pipelines and tailored analytics services.

Deployment Tailored to Clients’ Business Environment

LG CNS EDP service provides big data platform architecture design and deployment for both cloud and on-premises environments. Based on the task scope, we ​establish ​​verified logical, physical, and solution architectures to fit your environment. ​​The service also enables the deployment of a faster, more cost-efficient unified big data platform using our Smart Bigdata Platform (SBP) solution.

LG CNS EDP Architecture
LG CNS Solutions Completing EDP
SBP (Smart Bigdata Platform)

LG CNS SBP is a proven, enterprise- big data platform used across industries such as manufacturing, finance, public services, and more.
SBP provides an open-source-based, optimized solution and architecture for each stage of big data collection, storage, processing, analytics, and management. It enables efficient deployment by enhancing governance systems, ensuring security integration, and minimizing service downtime.

SBP Solution Environment

Leveraging Delta Lake, LG CNS SBP rapidly deploys a Lakehouse environment. Designed on an open-source basis and open standards, Lakehouse enables AI and diverse user experiences to seamlessly operate on a unified platform.

SBP Solution Processing Model

LG CNS SBP solution offers Delta Lake as a unified data repository for Large Language Models (LLMs) and includes a Vector DB (CloudBerry with VectorDB) for Retrieval-Augmented Generation (RAG) and Massive Parallel Processing (MPP) for LLMs. It also provides DataPrep for data pre-processing required for analytics, while offering independent spaces for model development and training to accelerate AI training and application.

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