Big Data
NoSQL |
Not Only SQL |
3 Vs of Big Data |
Volume (size), Velocity (speed), Variety (types). |
Volume |
Massive amount of data generated and stored. |
Velocity |
Speed at which data is produced, transferred, and processed. |
Variety |
Diversity of data formats and sources (text, images, videos, sensors, logs, etc.). |
Data Modelling |
Defining structure, relationships & rules for data. |
Relational DB query language |
SQL (Structured Query Language) |
ACID |
Atomicity, Consistency, Isolation, Durability |
BASE |
Basically Available, Soft state, Eventual consistency |
Scaling
Vertical Scaling (up) |
Add power to one server. |
Horizontal Scaling (up) |
Cluster of low-cost servers (preferred for Big Data). |
Normalisation |
Organising data to reduce redundancy, improve integrity. |
Denormalisation |
Trade redundancy for faster queries. |
Data Model Components
Entity |
Object / Class (e.g Student) |
Entity Instance |
Record of a entity |
Attribute |
Property / Field (e.g Age) |
Relationship |
Connection (e.g Student -> EnrolledIn -> Course) |
MongoDB
Document Oriented Database |
Subclass of key-value databases. |
Document |
Encoded in format such as XML, JSON, BSON. |
Schema-less |
No predefined structure on the stored data & each document can have its own structure. |
Collection |
Group of documents. |
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NoSQL Databases
Key-Value |
Stores simple key/value pairs. |
DynamoDB, Redis |
Document |
Stores JSON-like documents. |
MongoDB, CouchDB |
Column |
Stores data by columns instead of rows. |
Cassandra, BigTables |
Graphs |
vStores data as nodes + relationships. |
Neo4j, ArangoDB |
Time Series |
(not part of the unit but whatever it still is a type of NoSQL database) |
Prometheus, Timescale (postgreSQL fork (it's great tbh)) |
NoSQL - Quick Overview
▪ Non-relational and schema-less.
▪ Supports distributed database architectures.
▪ Provides high scalability, and high availability.
▪ Able to support very large amounts of sparse data.
▪ Designed mainly towards performance rather than transaction consistency. |
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