Structured data fits tables; unstructured includes images, text, audio, videos.

Queries structured datasets for training and evaluation tasks.

Splitting data across servers for performance and scalability.

NoSQL (e.g., MongoDB) or specialized object storage solutions.

They provide input/output data for model training and predictions.

They store, organize, and retrieve large datasets efficiently for model training and evaluation.

Using structured databases or tools like MLflow or SQLite.

They handle large, unstructured data with high scalability and flexibility.

Centralized repository for structured, historical data from different sources.

A storage repository holding vast raw data in native format until needed.

Stores data indexed by timestamps, useful for sensor or sequential data.

Organizing data to reduce redundancy and improve integrity.

Relational, NoSQL, and specialized time-series or graph databases.

Yes, using binary storage (e.g., BLOBs in SQL or GridFS).

Imputation, deletion, or flagging using preprocessing steps.

Extract, Transform, Load process for preparing data for models.

Yes, using APIs or streaming data frameworks.

Speeds up search queries using special data structures.

Yes, especially for relational data like social networks or recommendations.

Enable scalable, distributed storage and access to data.

Using sharding, indexing, compression, and distributed storage.

Defines structure, fields, and data types in a database.

OLAP for analysis, OLTP for real-time transactions.

With tools like DVC, Git, or metadata tables.

In tables with rows and predefined columns.