Big Data:
Big Data refers to datasets that are extremely large, complex, and fast-growing, making them difficult to manage, process, or analyze using traditional relational database systems.
Thank you for reading this post, don't forget to subscribe!- These datasets are typically generated from a variety of sources such as social media platforms, IoT (Internet of Things) devices, sensors, logs, mobile applications, and transaction records.
- Big Data systems are capable of handling structured, semi-structured, and unstructured data.
Big Data is commonly described using the three Vs:
- Volume – Refers to the enormous amount of data generated every second.
- Velocity – Refers to the speed at which data is generated and processed.
- Variety – Refers to the different types and formats of data, such as text, images, video, audio, and logs.
To handle Big Data effectively, specialized technologies like Hadoop and Apache Spark are used. These tools support distributed computing, which allows data to be processed across multiple machines in parallel.
NoSQL Databases:
NoSQL stands for “Not Only SQL“, and it refers to a class of database systems that are designed to handle large volumes of diverse, unstructured, or rapidly changing data.
- Unlike traditional relational databases, which store data in rows and tables with a fixed schema, NoSQL databases are schema-less, offering more flexibility for developers and applications.
- NoSQL databases are known for their ability to provide horizontal scalability, meaning they can distribute data across many servers to handle increased load.
- They are also optimized for high availability and fast read/write performance, which makes them well-suited for modern web, mobile, and big data applications.
Types of NoSQL Databases:
- MongoDB
- Redis, Cassandra
- HBase
- Neo4