An AI data platform can ingest a wide variety of data types, which generally fall into three broad categories: structured, semi-structured, and unstructured data. Structured data, like relational databases or CSV files, is organized into predefined formats with clear relationships, such as tables with rows and columns. Semi-structured data, such as JSON, XML, or log files, has some organizational properties but doesn’t fit rigid schemas. Unstructured data includes formats like text documents, images, audio, and video, which lack inherent organization and require preprocessing. Each type requires specific handling to prepare it for tasks like training models, generating insights, or powering applications.
Structured data is the most straightforward to process because of its fixed schema. For example, SQL databases (e.g., PostgreSQL, MySQL) or tabular data from spreadsheets can be ingested directly into AI platforms using standard ETL (Extract, Transform, Load) pipelines. Time-series data, such as sensor readings or stock prices, is another structured type often stored in databases like InfluxDB. Semi-structured data, like JSON logs from web servers or API responses, might need schema validation or flattening to convert nested fields into a usable format. Tools like Apache Spark or cloud-native services (e.g., AWS Glue) help parse and structure this data before feeding it into ML models. Unstructured data, like social media posts, scanned PDFs, or medical images, often requires preprocessing—such as OCR for text extraction, resizing images for computer vision models, or transcribing audio with speech-to-text tools.
Beyond these core categories, AI platforms can also handle specialized data types. For instance, graph data representing relationships (e.g., social networks) can be ingested using graph databases like Neo4j. Geospatial data, such as GPS coordinates or satellite imagery, often needs geohashing or coordinate normalization. Streaming data from IoT devices or real-time APIs (via platforms like Apache Kafka) enables continuous ingestion for applications like fraud detection. Additionally, AI platforms may process hybrid datasets—combining structured sales records with unstructured customer reviews, for example—using multimodal pipelines. Developers should consider storage optimizations (e.g., Parquet for columnar data) and scalability when designing ingestion workflows, ensuring the platform can handle high-volume or high-velocity data without bottlenecks. This flexibility allows teams to integrate diverse data sources into unified AI pipelines.