Powering efficient manufacturing worldwide
Hanwha Corporation unified data from its Global, Momentum, and Engineering and Construction divisions onto the Databricks Data + AI Platform, using Delta Lake and Unity Catalog to build an integrated data and AI system for scenario analysis and scheduling. The platform, with Databricks support for machine learning and large language model adoption, cut manual reporting time by more than 97% and reduced input-screen development time by more than 90%.
Overview
Hanwha Corporation unified data from its Global, Momentum, and Engineering and Construction divisions onto the Databricks Data + AI Platform, using Delta Lake and Unity Catalog to build an integrated data and AI system for scenario analysis and scheduling. The platform, with Databricks support for machine learning and large language model adoption, cut manual reporting time by more than 97% and reduced input-screen development time by more than 90%.
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Inspect the highlighted sourceThe challenge
Hanwha Corporation's existing databases were operated independently by each division and were siloed with limited integration, requiring access to multiple systems to obtain a complete picture of the data and causing time and cost limitations that made it difficult to derive effective insights. Following the company's 2023 business reorganization, timely data-informed business responses became essential across its Global, Momentum, and Engineering and Construction divisions.
The solution
Hanwha Corporation adopted the Databricks Data + AI Platform, organizing existing data infrastructure (SAP, RDBMS, etc.) and external data into an integrated pipeline through Delta Lake. Databricks SQL optimized data processing and let all data users query data, while flexible cluster configurations and Apache Spark versions with CPU and GPU selection let them optimize workloads across cloud platforms including AWS and Azure. Unity Catalog established standardized data governance with a unified repository and managed permissions, and Parquet made it easier for data scientists and ML engineers to share the same data. Databricks connected to Tableau and Microsoft Power BI for visualization, and provided technical support and training to Hanwha's data engineers on ML and LLM use cases.
Reported business value
Databricks automated manual tasks such as data processing, cleansing and adjustment, reducing the time to complete such tasks by more than 97%. Standardized data collection methods also reduced the time required for input screen development by more than 90%, boosting overall productivity and improving the accuracy and timeliness of data-driven decision-making.
Sources
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