Database Basics and Schema : Splitting Real time data into multiple tables.

 Splitting real-time data into multiple tables can be a useful strategy for managing and organizing large volumes of data efficiently. This approach, often referred to as database normalization, involves dividing data into logical and cohesive units to eliminate redundancy and improve data integrity. Here's a brief overview of how real-time data can be split into multiple tables.

Let's consider an example where we have a real-time application that tracks online orders. Initially, we might have a single table called "Orders" that stores all the relevant information about each order, including customer details, order items, timestamps, and more.

To split this data into multiple tables, we can identify logical entities within the data and create separate tables for them. For instance, we might create a "Customers" table to store customer-related information such as customer ID, name, address, and contact details. This table would have a primary key (customer ID) that relates to the "Orders" table.

Similarly, we can create a "Products" table to store product details, with a primary key that relates to the "Orders" table. This helps avoid data duplication and allows us to update product information in one place.

By splitting the data into multiple tables, we can establish relationships between them using primary and foreign keys. This allows us to efficiently query and retrieve related data, as well as maintain data consistency and integrity.

For example, the "Orders" table might have a foreign key column referencing the customer ID from the "Customers" table and another foreign key column referencing the product ID from the "Products" table.

Splitting real-time data into multiple tables not only improves data organization but also facilitates data analysis, reduces redundancy, and enhances performance. It provides a structured approach to managing complex data relationships and allows for easier scalability as the volume of data grows.

In summary, splitting real-time data into multiple tables through database normalization helps in structuring and organizing data, establishing relationships, and improving data integrity, making it an effective strategy for managing large datasets efficiently.