{"id":1669,"date":"2026-02-26T22:00:00","date_gmt":"2026-02-26T13:00:00","guid":{"rendered":"https:\/\/datalab.flitto.com\/en\/company\/blog\/?p=1669"},"modified":"2026-02-25T11:56:08","modified_gmt":"2026-02-25T02:56:08","slug":"data-warehouse-ai-training-data","status":"publish","type":"post","link":"https:\/\/datalab.flitto.com\/en\/company\/blog\/data-warehouse-ai-training-data\/","title":{"rendered":"[Data Deep Dive #4] Data Warehouse Strategy for AI Training Data"},"content":{"rendered":"\n
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There was a time when the term Big Data<\/a><\/em> dominated industry conversations. Data was continuously generated from various sources such as web\/app services and IoT devices, yet due to limitations in processing large-scale data in real time, much of it was simply discarded.<\/p>\n\n\n\n

As technology advanced, it became possible to collect this data in a centralized environment. By analyzing it and extracting insights, services and systems could be improved. While each individual piece of data may seem small, when aggregated, it becomes a powerful asset.<\/p>\n\n\n\n

In the era of AI, the importance of data has grown even further. When first designed, an AI model is nothing more than an empty shell with randomly initialized parameters. Through training on datasets composed of input and output data, these parameters are gradually fine-tuned. Only after this learning process is complete does the AI model become fully functional.<\/p>\n\n\n\n

Recently, AI models have grown so large that even those with tens of billions of parameters may be considered relatively small. As models increase in size, they require larger and more complex training datasets. This is similar to how academic content evolves from elementary school to middle school, high school, and university as students grow. To efficiently handle large-scale datasets, organizations typically build and operate a Data Warehouse<\/a>.<\/p>\n\n\n\n

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Flitto\u2019s Data Warehouse<\/h2>\n\n\n\n
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Since its establishment in 2012, Flitto has accumulated multilingual parallel corpora generated by users on its platform.<\/p>\n\n\n\n

\ud83d\udc49 Explore Flitto\u2019s Data<\/strong><\/a><\/p>\n\n\n\n

In the early stages, a relational database management system (RDBMS) was sufficient for storing this data. However, as the volume increased, search speeds declined. Additionally, managing not only parallel corpora but also multi-turn dialogue, image, and audio data, multimodal datasets, revealed the limitations of RDBMS systems.<\/p>\n\n\n\n

After reviewing various solutions, Flitto selected a new system. Existing data was migrated accordingly, and newly generated data is now directly stored in the Data Warehouse.<\/p>\n\n\n\n

Similar to Big Data systems that collect large volumes of data and analyze them to uncover insights, Flitto\u2019s Data Warehouse stores only the data required for dataset construction. Based on this foundation, new data is generated and expanded continuously.<\/p>\n\n\n\n

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Expansion of Parallel Corpora<\/h2>\n\n\n\n
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Discover Flitto\u2019s Multilingual Parallel Corpora<\/strong>\ud83d\udc47<\/mark><\/p>\n\n\n\n

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Korea\u2019s Elite AI Team Selected: Upstage Advances, Flitto Leads Dataset Construction<\/a><\/blockquote>