Introduction to SQL and NoSQL Databases

Overview

Understanding the differences between SQL (relational) and NoSQL (non-relational) databases.
Explanation of SQL using the analogy of an Excel spreadsheet.
Introduction to NoSQL, particularly MongoDB.

Structured Query Language (SQL)
- Relational databases structured in tables with rows and columns.
- Each row is a record; each column is a record field.
Challenges with SQL
- Adding new fields requires new columns, leading to potential bloated tables.
- Example: Multiple email addresses, physical addresses, and phone numbers needing separate columns.
- Relational databases separate related data into distinct tables linked by IDs.
- Data access across multiple tables requires JOIN operations.
- Schemas
  - Define the structure or blueprint of data.
  - Are strict and inflexible, making changes cumbersome.
- Scaling
  - Typically involves vertical scaling (upgrading to larger servers), which is costly.
- SQL databases have been around since the 1970s, optimized for data deduplication due to expensive storage.
- Complex structure, inflexibility, inefficiency, and high cost due to vertical scaling.

Definition and Types
- Term "NoSQL" can mean "Not SQL" or "Not Only SQL".
- Various types of NoSQL databases: key-value, wide-column stores, graph, and document databases.
MongoDB
- A document database.
- Uses JSON (JavaScript Object Notation) to store data.
- Structure
  - Documents contain key-value pairs.
  - Simple format: { "key": "value" }
  - Values can be strings, numbers, arrays, or other objects.
  - Example of user data in JSON format: { "_id": 1, "firstName": "John", "lastName": "Doe", "emails": ["[email protected]"], "addresses": ["123 Main St"], "phoneNumbers": ["123-456-7890"] }
- Documents store all related data together, avoiding the need for JOINs.
- Uses BSON (Binary JSON) for storage to support more data types.
- Flexible schemas: Any type of data can be stored in any document, with no enforced structure by default.
- Scalability
  - Supports horizontal scaling (adding more servers) instead of just vertical scaling, making it more cost-effective.

Self-hosted
- Host MongoDB server on own hardware.
- Requires self-management for server updates and maintenance.
- Free to use open-source community server.
MongoDB Atlas
- Fully managed, global cloud database service.
- Deployable across AWS, Google Cloud, Azure.
- Ensures availability, scalability, and compliance with data security and privacy standards.

Compass
- GUI for MongoDB, available on Linux, Mac, and Windows.
- Allows visual data exploration, ad hoc queries, CRUD operations, and performance optimization.
MongoDB Realm
- For building mobile apps with edge-to-cloud sync and back-end services.
- Handles conflict resolution and networking code.
MongoDB Atlas Search
- Full-text search capabilities, powered by Lucene.
- Features fuzzy searching, autocomplete, filters, facets, and custom scoring.
MongoDB Charts
- Data visualization tool integrated with MongoDB Cloud.
- For real-time insights, business intelligence, and embedded analytics.
Atlas Online Archive
- Tier data across managed databases and cloud storage, accessible via a single endpoint.
- Archive historical data to save on storage costs.
Atlas Data Lake
- Query and analyze data stored in AWS S3 and MongoDB Atlas.
- Supports CSV and other formats.
- Allows analysis without data movement or operational overhead.