Big Data:
Big data refers to datasets that are too large or complex to be processed by traditional database systems.
- These datasets often involve vast amounts of data generated at high velocity from various sources such as social media, IoT devices, and transaction logs. Big data is characterized by the three Vs: Volume, Velocity, and Variety.
- Big data technologies, such as Hadoop and Apache Spark, allow for the distributed processing of data across clusters of computers. These systems handle structured, semi-structured, and unstructured data, enabling organizations to extract valuable insights from large datasets.
NoSQL Databases:
NoSQL (Not Only SQL) databases are designed to handle large volumes of unstructured, semi-structured, or rapidly changing data.
- They are more flexible than traditional relational databases and often provide horizontal scalability and high availability.
Types of NoSQL databases include:
- Document Stores: Store data as documents (e.g., MongoDB).
- Key-Value Stores: Store data as key-value pairs (e.g., Redis, Cassandra).
- Column-family Stores: Organize data in columns rather than rows (e.g., HBase).
- Graph Databases: Store data as nodes and edges for relationships (e.g., Neo4j).
NoSQL databases are particularly useful for big data applications where the data may be distributed, highly dynamic, or complex.
