SQL Vs NoSQL
SQL (Structured Query Language) Databases
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Structured Data: SQL databases store data in tables (like a spreadsheet) with rows and columns. Each row represents a record, and each column represents a field (e.g., name, age, address).
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Schema-based: They follow a predefined schema, meaning the structure of the data (like what fields exist and their types) must be defined upfront. All data must fit into this structure.
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Relational: They are also called relational databases because data can be linked between different tables through relationships (using foreign keys).
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Examples: MySQL, PostgreSQL, Oracle, SQL Server.
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Use Case: Ideal for applications that require complex queries, structured data, and strong consistency, like financial systems, inventory management, and enterprise applications.
NoSQL (Not Only SQL) Databases
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Unstructured or Semi-structured Data: NoSQL databases store data in a more flexible way, often using documents, key-value pairs, graphs, or wide-column stores.
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Document-based: Data is stored as documents (usually in JSON or BSON format) that can have varying structures (e.g., some documents might have different fields).
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Key-value: Data is stored as key-value pairs, like a dictionary.
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Schema-less: No predefined schema is required. Data can be added without needing to define a strict structure, allowing for greater flexibility in handling different types of data.
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Non-relational: Relationships between data are less strict, or sometimes nonexistent, compared to SQL.
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Examples: MongoDB, Cassandra, Redis, CouchDB.
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Use Case: Ideal for applications that handle large volumes of unstructured or semi-structured data, need high scalability, or deal with frequently changing data, like social media, real-time analytics, or IoT data.