TinyDB differs from traditional SQL databases in several key ways:

Data model : TinyDB follows a document-oriented data model, where data is stored in JSON-like documents. Each document can have its own structure, and there's no fixed schema enforced across the entire database. In contrast, traditional SQL databases use a tabular data model, where data is organized into tables with predefined schemas consisting of rows and columns.

Query language : TinyDB provides a simple and intuitive query language for filtering and retrieving data from the database. The queries are based on Python expressions and can be constructed using the Query object or dictionary syntax. SQL databases, on the other hand, use SQL (Structured Query Language) for querying data, which involves a more complex syntax and is optimized for relational data models.

Storage mechanism : By default, TinyDB operates entirely in memory, storing data in Python data structures. However, it also provides support for persistent storage, allowing data to be saved to disk in JSON format. Traditional SQL databases typically use a client-server architecture, where data is stored in a separate database server process and accessed by clients over a network connection.
Scalability : TinyDB is designed for simplicity and is well-suited for small to medium-sized datasets. It may not be as scalable or performant as traditional SQL databases, which are optimized for handling large volumes of data and concurrent access by multiple clients.

Transactions and concurrency : TinyDB does not provide built-in support for transactions or concurrent access control mechanisms. While it is possible to implement basic locking mechanisms for concurrency control, TinyDB may not be suitable for highly concurrent applications that require strict transactional guarantees. Traditional SQL databases offer robust transactional support, ACID (Atomicity, Consistency, Isolation, Durability) properties, and sophisticated concurrency control mechanisms.

Indexing and optimization : TinyDB does not support advanced indexing or optimization techniques commonly found in traditional SQL databases, such as B-tree indexes, query optimization, or query execution plans. As a result, query performance in TinyDB may be limited compared to SQL databases, especially for complex queries or large datasets.