Deepseek can handle both structured and unstructured data, though its approach and tools differ for each type. Structured data, such as databases or spreadsheets with fixed schemas, is processed using predefined rules and relationships. For example, Deepseek might use SQL-like queries or data transformation pipelines to clean, join, or aggregate tabular data. Unstructured data, like text, images, or audio, requires techniques like natural language processing (NLP) or computer vision to extract patterns. For instance, Deepseek could analyze customer reviews (text) to identify sentiment or classify product images (pixels) into categories. The platform’s flexibility allows developers to work with diverse data formats depending on the task.
For structured data, Deepseek provides tools to integrate with common databases (e.g., PostgreSQL, MySQL) or file formats like CSV or Parquet. Developers can define schemas, enforce data validation, and perform operations like filtering or aggregation. A practical example is training a recommendation system using user purchase histories (structured as user IDs, product IDs, timestamps) combined with product metadata. Deepseek’s pipelines could join these tables, normalize numerical features (e.g., purchase frequency), and encode categorical variables (e.g., product categories) for machine learning models. This structured approach ensures consistency and scalability for tasks like predictions or analytics.
With unstructured data, Deepseek relies on embeddings and neural networks to process raw inputs. For text, it might use transformer-based models to generate vector representations of sentences, enabling tasks like semantic search or topic modeling. For images, convolutional neural networks (CNNs) could extract features for object detection. A developer might combine structured product data (price, category) with unstructured product descriptions to build a hybrid search system. Deepseek’s APIs or libraries simplify these workflows—for example, preprocessing text with tokenization, generating embeddings, and storing results in a vector database. This dual capability allows developers to tackle complex problems that require both data types, such as fraud detection (transaction logs + email text) or customer segmentation (demographics + social media activity).