Difference Between Big Data and Data Warehouse

Big Data:

It refers to a large volume of data that is too complex to be processed by traditional data processing databases and software. This large amount of data may be structured, semi-structured, or non-structured and cannot be processed by traditional data processing software and databases.

Data Warehouse:

A data warehouse is a collection of data from different heterogeneous sources. It serves as a major part of business intelligence in most organizations. Data is gathered from various sources, transformed, and loaded into a repository where data analytics and management can be done to derive meaningful insights from the data

Big Data vs Data Warehouse:

Big Data	Data Warehouse
1. It is a technology to store and manage large amount of data.	1. It is an architecture used to organize the data.
2. Big data doesn't follow any SQL queries to fetch data from database.	2. Data warehouse follows SQL queries to fetch data from database.
3. It takes structured, non-structured or semi-structured data as an input.	3. It only takes structured data as an input.
4. Big Data uses distributed file systems for processing.	4. Data warehouses typically do not use distributed file systems for processing.
5. Big data uses Apache Hadoop, Apache Spark, and various NoSQL databases.	5. Data Warehouse uses RDBMS like Oracle, SQL Server, etc.
6. In big data systems, when new data is added or changes occur, these changes are typically stored in the form of files.	6. Data warehouses are less agile when it comes to incorporating changes in data.